From dc16774cd589422d534927aa2a8f5462c5c7cf26 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 18 Jul 2025 15:26:31 -0400
Subject: [PATCH 01/47] feat: cleaning up dependencies

---
 poetry.lock                                   | 6542 +++++++++--------
 pyproject.toml                                |   43 +-
 .../ChiSquaredFeaturesTable.py                |   16 +-
 .../data_validation/SeasonalDecompose.py      |   16 +-
 .../tests/data_validation/ShapiroWilk.py      |   15 +-
 .../TimeSeriesDescriptiveStatistics.py        |   15 +-
 .../ModelPredictionResiduals.py               |   15 +-
 .../TimeSeriesPredictionWithCI.py             |   15 +-
 .../sklearn/KMeansClustersOptimization.py     |   16 +-
 .../statsmodels/RegressionCoeffs.py           |   16 +-
 .../ClassDiscriminationDrift.py               |   15 +-
 .../PredictionProbabilitiesHistogramDrift.py  |   15 +-
 .../ScorecardHistogramDrift.py                |   15 +-
 .../TargetPredictionDistributionPlot.py       |   15 +-
 14 files changed, 3829 insertions(+), 2940 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 23c7b54ca..05e746eb3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,174 +1,173 @@
-# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
-version = "3.2.0"
+version = "3.5.0"
 description = "Simple DNS resolver for asyncio"
 optional = false
-python-versions = "*"
+python-versions = ">=3.9"
 groups = ["main"]
-markers = "sys_platform == \"linux\" or sys_platform == \"darwin\""
 files = [
-    {file = "aiodns-3.2.0-py3-none-any.whl", hash = "sha256:e443c0c27b07da3174a109fd9e736d69058d808f144d3c9d56dbd1776964c5f5"},
-    {file = "aiodns-3.2.0.tar.gz", hash = "sha256:62869b23409349c21b072883ec8998316b234c9a9e36675756e8e317e8768f72"},
+    {file = "aiodns-3.5.0-py3-none-any.whl", hash = "sha256:6d0404f7d5215849233f6ee44854f2bb2481adf71b336b2279016ea5990ca5c5"},
+    {file = "aiodns-3.5.0.tar.gz", hash = "sha256:11264edbab51896ecf546c18eb0dd56dff0428c6aa6d2cd87e643e07300eb310"},
 ]
 
 [package.dependencies]
-pycares = ">=4.0.0"
+pycares = ">=4.9.0"
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.6.1"
 description = "Happy Eyeballs for asyncio"
 optional = false
-python-versions = ">=3.8"
-groups = ["main"]
+python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
-    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
-    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
+    {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
+    {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
 ]
+markers = {dev = "python_version == \"3.12\""}
 
 [[package]]
 name = "aiohttp"
-version = "3.10.11"
+version = "3.12.14"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.8"
-groups = ["main"]
+python-versions = ">=3.9"
+groups = ["main", "dev"]
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+]
+markers = {dev = "python_version == \"3.12\""}
+
+[package.dependencies]
+aiodns = {version = ">=3.3.0", optional = true, markers = "extra == \"speedups\""}
+aiohappyeyeballs = ">=2.5.0"
+aiosignal = ">=1.4.0"
 async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 Brotli = {version = "*", optional = true, markers = "platform_python_implementation == \"CPython\" and extra == \"speedups\""}
 brotlicffi = {version = "*", optional = true, markers = "platform_python_implementation != \"CPython\" and extra == \"speedups\""}
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.12.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "brotlicffi ; platform_python_implementation != \"CPython\""]
 
 [[package]]
 name = "aiosignal"
-version = "1.3.1"
+version = "1.4.0"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
-python-versions = ">=3.7"
-groups = ["main"]
+python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
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 ]
+markers = {dev = "python_version == \"3.12\""}
 
 [package.dependencies]
 frozenlist = ">=1.1.0"
+typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
 
 [[package]]
 name = "alabaster"
-version = "0.7.13"
-description = "A configurable sidebar-enabled Sphinx theme"
+version = "0.7.16"
+description = "A light, configurable Sphinx theme"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [[package]]
@@ -197,37 +196,37 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.5.2"
+version = "4.9.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "dev"]
 files = [
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+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
 ]
 
 [package.dependencies]
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 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21.0b1) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\""]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "anywidget"
-version = "0.9.15"
+version = "0.9.18"
 description = "custom jupyter widgets made easy"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -257,74 +256,74 @@ version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
 python-versions = ">=3.6"
-groups = ["main", "dev"]
+groups = ["dev"]
+markers = "platform_system == \"Darwin\""
 files = [
     {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
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 ]
-markers = {main = "sys_platform == \"darwin\"", dev = "sys_platform == \"darwin\" or platform_system == \"Darwin\""}
 
 [[package]]
 name = "arch"
-version = "5.6.0"
+version = "7.2.0"
 description = "ARCH for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "arch-7.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24977b2969225bbe039f1816253b1c06b1a03ad64a904679e2a1762bac03c0e4"},
+    {file = "arch-7.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:d7f7d50c3122e58540c530c333b25f9c50317a32dc43dedfd105f07712c5d4ec"},
+    {file = "arch-7.2.0.tar.gz", hash = "sha256:2c6128174e0e3ab4c2cad527e78eb3fbce4a4f8ba53580bdf0d02566eb2e953d"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.3"
+pandas = ">=1.4"
+scipy = ">=1.8"
+statsmodels = ">=0.12"
 
 [[package]]
 name = "argon2-cffi"
-version = "23.1.0"
+version = "25.1.0"
 description = "Argon2 for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
-    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
+    {file = "argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741"},
+    {file = "argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1"},
 ]
 
 [package.dependencies]
 argon2-cffi-bindings = "*"
 
-[package.extras]
-dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
-tests = ["hypothesis", "pytest"]
-typing = ["mypy"]
-
 [[package]]
 name = "argon2-cffi-bindings"
 version = "21.2.0"
@@ -401,18 +400,18 @@ test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "async-lru"
-version = "2.0.4"
+version = "2.0.5"
 description = "Simple LRU cache for asyncio"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
-    {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
+    {file = "async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943"},
+    {file = "async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb"},
 ]
 
 [package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "async-timeout"
@@ -421,7 +420,7 @@ description = "Timeout context manager for asyncio programs"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "python_version <= \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
     {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
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@@ -429,14 +428,14 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "25.2.0"
+version = "25.3.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "attrs-25.2.0-py3-none-any.whl", hash = "sha256:611344ff0a5fed735d86d7784610c84f8126b95e549bcad9ff61b4242f2d386b"},
-    {file = "attrs-25.2.0.tar.gz", hash = "sha256:18a06db706db43ac232cce80443fcd9f2500702059ecf53489e3c5a3f417acaf"},
+    {file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
+    {file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
 ]
 
 [package.extras]
@@ -462,18 +461,6 @@ files = [
 [package.extras]
 dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""]
 
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-optional = false
-python-versions = "*"
-groups = ["main", "dev"]
-files = [
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-]
-
 [[package]]
 name = "backports-tarfile"
 version = "1.2.0"
@@ -481,6 +468,7 @@ description = "Backport of CPython tarfile module"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
+markers = "python_version <= \"3.11\""
 files = [
     {file = "backports.tarfile-1.2.0-py3-none-any.whl", hash = "sha256:77e284d754527b01fb1e6fa8a1afe577858ebe4e9dad8919e34c862cb399bc34"},
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@@ -492,14 +480,14 @@ testing = ["jaraco.test", "pytest (!=8.0.*)", "pytest (>=6,!=8.1.*)", "pytest-ch
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.13.3"
+version = "4.13.4"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.7.0"
-groups = ["main", "dev"]
+groups = ["dev"]
 files = [
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+    {file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"},
+    {file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"},
 ]
 
 [package.dependencies]
@@ -573,23 +561,22 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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+    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
 ]
 
 [package.dependencies]
-six = ">=1.9.0"
-tinycss2 = {version = ">=1.1.0,<1.3", optional = true, markers = "extra == \"css\""}
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "brotli"
@@ -728,43 +715,43 @@ cffi = ">=1.0.0"
 
 [[package]]
 name = "catboost"
-version = "1.2.7"
+version = "1.2.8"
 description = "CatBoost Python Package"
 optional = false
 python-versions = "*"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
 graphviz = "*"
 matplotlib = "*"
-numpy = ">=1.16.0,<2.0"
+numpy = ">=1.16.0,<3.0"
 pandas = ">=0.24"
 plotly = "*"
 scipy = "*"
@@ -775,14 +762,14 @@ widget = ["ipython", "ipywidgets (>=7.0,<9.0)", "traitlets"]
 
 [[package]]
 name = "certifi"
-version = "2025.1.31"
+version = "2025.7.14"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 groups = ["main", "dev"]
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 [package.dependencies]
 pycparser = "*"
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 [[package]]
 name = "cryptography"
@@ -1117,7 +1228,7 @@ description = "cryptography is a package which provides cryptographic recipes an
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
-markers = "sys_platform == \"linux\""
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 test-randomorder = ["pytest-randomly"]
 
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+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -1293,38 +1465,38 @@ vision = ["Pillow (>=9.4.0)"]
 
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 [[package]]
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 [[package]]
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-version = "0.3.9"
+version = "0.4.0"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 groups = ["dev"]
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 [[package]]
@@ -1429,14 +1601,14 @@ files = [
 
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 description = "HuggingFace community-driven open-source library of evaluation"
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 python-versions = ">=3.8.0"
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 [package.extras]
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 torch = ["torch"]
 
 [[package]]
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 python-versions = ">=3.7"
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+[package.dependencies]
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+
 [package.extras]
 test = ["pytest (>=6)"]
 
@@ -1511,19 +1686,19 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
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 [[package]]
@@ -1545,66 +1720,58 @@ pyflakes = ">=2.4.0,<2.5.0"
 
 [[package]]
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 ]
 
 [package.extras]
@@ -1930,14 +2041,14 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "1.4.0"
+version = "1.7.3"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "griffe-1.4.0-py3-none-any.whl", hash = "sha256:e589de8b8c137e99a46ec45f9598fc0ac5b6868ce824b24db09c02d117b89bc5"},
-    {file = "griffe-1.4.0.tar.gz", hash = "sha256:8fccc585896d13f1221035d32c50dec65830c87d23f9adb9b1e6f3d63574f7f5"},
+    {file = "griffe-1.7.3-py3-none-any.whl", hash = "sha256:c6b3ee30c2f0f17f30bcdef5068d6ab7a2a4f1b8bf1a3e74b56fffd21e1c5f75"},
+    {file = "griffe-1.7.3.tar.gz", hash = "sha256:52ee893c6a3a968b639ace8015bec9d36594961e156e23315c8e8e51401fa50b"},
 ]
 
 [package.dependencies]
@@ -1955,15 +2066,38 @@ files = [
     {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
+[[package]]
+name = "hf-xet"
+version = "1.1.5"
+description = "Fast transfer of large files with the Hugging Face Hub."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
+files = [
+    {file = "hf_xet-1.1.5-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:f52c2fa3635b8c37c7764d8796dfa72706cc4eded19d638331161e82b0792e23"},
+    {file = "hf_xet-1.1.5-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:9fa6e3ee5d61912c4a113e0708eaaef987047616465ac7aa30f7121a48fc1af8"},
+    {file = "hf_xet-1.1.5-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc874b5c843e642f45fd85cda1ce599e123308ad2901ead23d3510a47ff506d1"},
+    {file = "hf_xet-1.1.5-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:dbba1660e5d810bd0ea77c511a99e9242d920790d0e63c0e4673ed36c4022d18"},
+    {file = "hf_xet-1.1.5-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:ab34c4c3104133c495785d5d8bba3b1efc99de52c02e759cf711a91fd39d3a14"},
+    {file = "hf_xet-1.1.5-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:83088ecea236d5113de478acb2339f92c95b4fb0462acaa30621fac02f5a534a"},
+    {file = "hf_xet-1.1.5-cp37-abi3-win_amd64.whl", hash = "sha256:73e167d9807d166596b4b2f0b585c6d5bd84a26dea32843665a8b58f6edba245"},
+    {file = "hf_xet-1.1.5.tar.gz", hash = "sha256:69ebbcfd9ec44fdc2af73441619eeb06b94ee34511bbcf57cd423820090f5694"},
+]
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "html2text"
-version = "2024.2.26"
+version = "2025.4.15"
 description = "Turn HTML into equivalent Markdown-structured text."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "html2text-2024.2.26.tar.gz", hash = "sha256:05f8e367d15aaabc96415376776cdd11afd5127a77fce6e36afc60c563ca2c32"},
+    {file = "html2text-2025.4.15-py3-none-any.whl", hash = "sha256:00569167ffdab3d7767a4cdf589b7f57e777a5ed28d12907d8c58769ec734acc"},
+    {file = "html2text-2025.4.15.tar.gz", hash = "sha256:948a645f8f0bc3abe7fd587019a2197a12436cd73d0d4908af95bfc8da337588"},
 ]
 
 [[package]]
@@ -2015,32 +2149,33 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "httpx-sse"
-version = "0.4.0"
+version = "0.4.1"
 description = "Consume Server-Sent Event (SSE) messages with HTTPX."
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
-    {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
-    {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
+    {file = "httpx_sse-0.4.1-py3-none-any.whl", hash = "sha256:cba42174344c3a5b06f255ce65b350880f962d99ead85e776f23c6618a377a37"},
+    {file = "httpx_sse-0.4.1.tar.gz", hash = "sha256:8f44d34414bc7b21bf3602713005c5df4917884f76072479b21f68befa4ea26e"},
 ]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.29.3"
+version = "0.33.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 groups = ["main"]
 files = [
-    {file = "huggingface_hub-0.29.3-py3-none-any.whl", hash = "sha256:0b25710932ac649c08cdbefa6c6ccb8e88eef82927cacdb048efb726429453aa"},
-    {file = "huggingface_hub-0.29.3.tar.gz", hash = "sha256:64519a25716e0ba382ba2d3fb3ca082e7c7eb4a2fc634d200e8380006e0760e5"},
+    {file = "huggingface_hub-0.33.4-py3-none-any.whl", hash = "sha256:09f9f4e7ca62547c70f8b82767eefadd2667f4e116acba2e3e62a5a81815a7bb"},
+    {file = "huggingface_hub-0.33.4.tar.gz", hash = "sha256:6af13478deae120e765bfd92adad0ae1aec1ad8c439b46f23058ad5956cbca0a"},
 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = ">=2023.5.0"
+hf-xet = {version = ">=1.1.2,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -2048,29 +2183,32 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
+hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
 inference = ["aiohttp"]
-quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
+mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
+oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
+quality = ["libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["safetensors[torch]", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
 name = "identify"
-version = "2.6.1"
+version = "2.6.12"
 description = "File identification library for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "identify-2.6.1-py2.py3-none-any.whl", hash = "sha256:53863bcac7caf8d2ed85bd20312ea5dcfc22226800f6d6881f232d861db5a8f0"},
-    {file = "identify-2.6.1.tar.gz", hash = "sha256:91478c5fb7c3aac5ff7bf9b4344f803843dc586832d5f110d672b19aa1984c98"},
+    {file = "identify-2.6.12-py2.py3-none-any.whl", hash = "sha256:ad9672d5a72e0d2ff7c5c8809b62dfa60458626352fb0eb7b55e69bdc45334a2"},
+    {file = "identify-2.6.12.tar.gz", hash = "sha256:d8de45749f1efb108badef65ee8386f0f7bb19a7f26185f74de6367bffbaf0e6"},
 ]
 
 [package.extras]
@@ -2105,14 +2243,14 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.5.0"
+version = "8.7.0"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
-    {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
+    {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"},
+    {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"},
 ]
 
 [package.dependencies]
@@ -2124,20 +2262,20 @@ cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+test = ["flufl.flake8", "importlib_resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
 type = ["pytest-mypy"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.5"
+version = "6.5.2"
 description = "Read resources from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 markers = "python_version < \"3.10\""
 files = [
-    {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
-    {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
+    {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
+    {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
 ]
 
 [package.dependencies]
@@ -2187,62 +2325,111 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.12.3"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "dev"]
+markers = "python_version < \"3.11\""
 files = [
-    {file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
-    {file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
+
+[[package]]
+name = "ipython"
+version = "9.4.0"
+description = "IPython: Productive Interactive Computing"
+optional = false
+python-versions = ">=3.11"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "ipython-9.4.0-py3-none-any.whl", hash = "sha256:25850f025a446d9b359e8d296ba175a36aedd32e83ca9b5060430fe16801f066"},
+    {file = "ipython-9.4.0.tar.gz", hash = "sha256:c033c6d4e7914c3d9768aabe76bbe87ba1dc66a92a05db6bfa1125d81f2ee270"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+ipython-pygments-lexers = "*"
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt_toolkit = ">=3.0.41,<3.1.0"
+pygments = ">=2.4.0"
+stack_data = "*"
+traitlets = ">=5.13.0"
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
+
+[package.extras]
+all = ["ipython[doc,matplotlib,test,test-extra]"]
+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinx_toml (==0.0.4)", "typing_extensions"]
+matplotlib = ["matplotlib"]
+test = ["packaging", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipykernel", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbclient", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
+
+[[package]]
+name = "ipython-pygments-lexers"
+version = "1.1.1"
+description = "Defines a variety of Pygments lexers for highlighting IPython code."
+optional = false
+python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.11\""
+files = [
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@@ -2317,18 +2504,18 @@ test = ["portend", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-c
 
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+more_itertools = "*"
 
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@@ -2395,116 +2582,117 @@ i18n = ["Babel (>=2.7)"]
 
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 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.8"
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 files = [
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 [[package]]
 name = "json5"
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+version = "0.12.0"
 description = "A Python implementation of the JSON5 data format."
 optional = false
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 [package.extras]
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+dev = ["build (==1.2.2.post1)", "coverage (==7.5.4) ; python_version < \"3.9\"", "coverage (==7.8.0) ; python_version >= \"3.9\"", "mypy (==1.14.1) ; python_version < \"3.9\"", "mypy (==1.15.0) ; python_version >= \"3.9\"", "pip (==25.0.1)", "pylint (==3.2.7) ; python_version < \"3.9\"", "pylint (==3.3.6) ; python_version >= \"3.9\"", "ruff (==0.11.2)", "twine (==6.1.0)", "uv (==0.6.11)"]
 
 [[package]]
 name = "jsonpatch"
@@ -2537,14 +2725,14 @@ markers = {main = "extra == \"all\" or extra == \"llm\""}
 
 [[package]]
 name = "jsonschema"
-version = "4.23.0"
+version = "4.25.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [package.dependencies]
@@ -2557,24 +2745,25 @@ jsonschema-specifications = ">=2023.03.6"
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3987-syntax = {version = ">=1.1.0", optional = true, markers = "extra == \"format-nongpl\""}
 rpds-py = ">=0.7.1"
 uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "rfc3987-syntax (>=1.1.0)", "uri-template", "webcolors (>=24.6.0)"]
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.12.1"
+version = "2025.4.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
-    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+    {file = "jsonschema_specifications-2025.4.1-py3-none-any.whl", hash = "sha256:4653bffbd6584f7de83a67e0d620ef16900b390ddc7939d56684d6c81e33f1af"},
+    {file = "jsonschema_specifications-2025.4.1.tar.gz", hash = "sha256:630159c9f4dbea161a6a2205c3011cc4f18ff381b189fff48bb39b9bf26ae608"},
 ]
 
 [package.dependencies]
@@ -2651,14 +2840,14 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.2"
+version = "5.8.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
-    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
+    {file = "jupyter_core-5.8.1-py3-none-any.whl", hash = "sha256:c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0"},
+    {file = "jupyter_core-5.8.1.tar.gz", hash = "sha256:0a5f9706f70e64786b75acba995988915ebd4601c8a52e534a40b51c95f59941"},
 ]
 
 [package.dependencies]
@@ -2667,23 +2856,24 @@ pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_
 traitlets = ">=5.3"
 
 [package.extras]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<9)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.10.0"
+version = "0.12.0"
 description = "Jupyter Event System library"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
-    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
+    {file = "jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb"},
+    {file = "jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b"},
 ]
 
 [package.dependencies]
 jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+packaging = "*"
 python-json-logger = ">=2.0.4"
 pyyaml = ">=5.3"
 referencing = "*"
@@ -2693,7 +2883,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 cli = ["click", "rich"]
-docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8)", "sphinxcontrib-spelling"]
 test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
 
 [[package]]
@@ -2714,14 +2904,14 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.14.2"
+version = "2.16.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "jupyter_server-2.14.2-py3-none-any.whl", hash = "sha256:47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd"},
-    {file = "jupyter_server-2.14.2.tar.gz", hash = "sha256:66095021aa9638ced276c248b1d81862e4c50f292d575920bbe960de1c56b12b"},
+    {file = "jupyter_server-2.16.0-py3-none-any.whl", hash = "sha256:3d8db5be3bc64403b1c65b400a1d7f4647a5ce743f3b20dbdefe8ddb7b55af9e"},
+    {file = "jupyter_server-2.16.0.tar.gz", hash = "sha256:65d4b44fdf2dcbbdfe0aa1ace4a842d4aaf746a2b7b168134d5aaed35621b7f6"},
 ]
 
 [package.dependencies]
@@ -2730,7 +2920,7 @@ argon2-cffi = ">=21.1"
 jinja2 = ">=3.0.3"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
-jupyter-events = ">=0.9.0"
+jupyter-events = ">=0.11.0"
 jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
@@ -2771,14 +2961,14 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.3.5"
+version = "4.4.4"
 description = "JupyterLab computational environment"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "jupyterlab-4.3.5-py3-none-any.whl", hash = "sha256:571bbdee20e4c5321ab5195bc41cf92a75a5cff886be5e57ce78dfa37a5e9fdb"},
-    {file = "jupyterlab-4.3.5.tar.gz", hash = "sha256:c779bf72ced007d7d29d5bcef128e7fdda96ea69299e19b04a43635a7d641f9d"},
+    {file = "jupyterlab-4.4.4-py3-none-any.whl", hash = "sha256:711611e4f59851152eb93316c3547c3ec6291f16bb455f1f4fa380d25637e0dd"},
+    {file = "jupyterlab-4.4.4.tar.gz", hash = "sha256:163fee1ef702e0a057f75d2eed3ed1da8a986d59eb002cbeb6f0c2779e6cd153"},
 ]
 
 [package.dependencies]
@@ -2793,15 +2983,15 @@ jupyter-server = ">=2.4.0,<3"
 jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
-setuptools = ">=40.8.0"
+setuptools = ">=41.1.0"
 tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.6.9)"]
-docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.1.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.4.1)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.2.post3)", "matplotlib (==3.9.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.14.1)", "vega-datasets (==0.9.0)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.11.4)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.2.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.5.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.3.post1)", "matplotlib (==3.10.0)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.15.1)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
 
@@ -2846,46 +3036,48 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.13"
+version = "3.0.15"
 description = "Jupyter interactive widgets for JupyterLab"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "dev"]
 files = [
-    {file = "jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54"},
-    {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
+    {file = "jupyterlab_widgets-3.0.15-py3-none-any.whl", hash = "sha256:d59023d7d7ef71400d51e6fee9a88867f6e65e10a4201605d2d7f3e8f012a31c"},
+    {file = "jupyterlab_widgets-3.0.15.tar.gz", hash = "sha256:2920888a0c2922351a9202817957a68c07d99673504d6cd37345299e971bb08b"},
 ]
 
 [[package]]
 name = "kaleido"
-version = "0.2.1"
-description = "Static image export for web-based visualization libraries with zero dependencies"
+version = "1.0.0"
+description = "Plotly graph export library"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "kaleido-0.2.1-py2.py3-none-macosx_10_11_x86_64.whl", hash = "sha256:ca6f73e7ff00aaebf2843f73f1d3bacde1930ef5041093fe76b83a15785049a7"},
-    {file = "kaleido-0.2.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:bb9a5d1f710357d5d432ee240ef6658a6d124c3e610935817b4b42da9c787c05"},
-    {file = "kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:aa21cf1bf1c78f8fa50a9f7d45e1003c387bd3d6fe0a767cfbbf344b95bdc3a8"},
-    {file = "kaleido-0.2.1-py2.py3-none-manylinux2014_aarch64.whl", hash = "sha256:845819844c8082c9469d9c17e42621fbf85c2b237ef8a86ec8a8527f98b6512a"},
-    {file = "kaleido-0.2.1-py2.py3-none-win32.whl", hash = "sha256:ecc72635860be616c6b7161807a65c0dbd9b90c6437ac96965831e2e24066552"},
-    {file = "kaleido-0.2.1-py2.py3-none-win_amd64.whl", hash = "sha256:4670985f28913c2d063c5734d125ecc28e40810141bdb0a46f15b76c1d45f23c"},
+    {file = "kaleido-1.0.0-py3-none-any.whl", hash = "sha256:a7e8bd95648378d2746f6c86084d419d15f95b1ec7bb0ec810289b7feb25b18d"},
+    {file = "kaleido-1.0.0.tar.gz", hash = "sha256:502d8ba64737924efaf5e94c2736745bcc7c926e6cc535838be36c0fc06330bd"},
 ]
 
+[package.dependencies]
+choreographer = ">=1.0.5"
+logistro = ">=1.0.8"
+orjson = ">=3.10.15"
+packaging = "*"
+
 [[package]]
 name = "keyring"
-version = "25.5.0"
+version = "25.6.0"
 description = "Store and access your passwords safely."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "keyring-25.5.0-py3-none-any.whl", hash = "sha256:e67f8ac32b04be4714b42fe84ce7dad9c40985b9ca827c592cc303e7c26d9741"},
-    {file = "keyring-25.5.0.tar.gz", hash = "sha256:4c753b3ec91717fe713c4edd522d625889d8973a349b0e582622f49766de58e6"},
+    {file = "keyring-25.6.0-py3-none-any.whl", hash = "sha256:552a3f7af126ece7ed5c89753650eec89c7eaae8617d0aa4d9ad2b75111266bd"},
+    {file = "keyring-25.6.0.tar.gz", hash = "sha256:0b39998aa941431eb3d9b0d4b2460bc773b9df6fed7621c2dfb291a7e0187a66"},
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=4.11.4", markers = "python_version < \"3.12\""}
+importlib_metadata = {version = ">=4.11.4", markers = "python_version < \"3.12\""}
 "jaraco.classes" = "*"
 "jaraco.context" = "*"
 "jaraco.functools" = "*"
@@ -2909,6 +3101,7 @@ description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "kiwisolver-1.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8a9c83f75223d5e48b0bc9cb1bf2776cf01563e00ade8775ffe13b0b6e1af3a6"},
     {file = "kiwisolver-1.4.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:58370b1ffbd35407444d57057b57da5d6549d2d854fa30249771775c63b5fe17"},
@@ -3026,6 +3219,97 @@ files = [
     {file = "kiwisolver-1.4.7.tar.gz", hash = "sha256:9893ff81bd7107f7b685d3017cc6583daadb4fc26e4a888350df530e41980a60"},
 ]
 
+[[package]]
+name = "kiwisolver"
+version = "1.4.8"
+description = "A fast implementation of the Cassowary constraint solver"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db"},
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+
 [[package]]
 name = "langchain"
 version = "0.3.26"
@@ -3070,25 +3354,25 @@ xai = ["langchain-xai"]
 
 [[package]]
 name = "langchain-community"
-version = "0.3.16"
+version = "0.3.27"
 description = "Community contributed LangChain integrations."
 optional = true
-python-versions = "<4.0,>=3.9"
+python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
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 ]
 
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-langsmith = ">=0.1.125,<0.4"
-numpy = {version = ">=1.22.4,<2", markers = "python_version < \"3.12\""}
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+langchain-core = ">=0.3.66,<1.0.0"
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 pydantic-settings = ">=2.4.0,<3.0.0"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
@@ -3097,21 +3381,21 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.66"
+version = "0.3.69"
 description = "Building applications with LLMs through composability"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
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 ]
 
 [package.dependencies]
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 langsmith = ">=0.3.45"
-packaging = ">=23.2,<25"
+packaging = ">=23.2"
 pydantic = ">=2.7.4"
 PyYAML = ">=5.3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
@@ -3119,20 +3403,20 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-openai"
-version = "0.3.8"
+version = "0.3.28"
 description = "An integration package connecting OpenAI and LangChain"
 optional = true
-python-versions = "<4.0,>=3.9"
+python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
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 ]
 
 [package.dependencies]
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-openai = ">=1.58.1,<2.0.0"
+langchain-core = ">=0.3.68,<1.0.0"
+openai = ">=1.86.0,<2.0.0"
 tiktoken = ">=0.7,<1"
 
 [[package]]
@@ -3168,22 +3452,22 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.3.45"
+version = "0.4.7"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
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+    {file = "langsmith-0.4.7.tar.gz", hash = "sha256:3864cf29295c2565c578e93d1533f5b39e2b4af616545ace30f069635a319890"},
 ]
 
 [package.dependencies]
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 orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
 packaging = ">=23.2"
-pydantic = {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""}
+pydantic = ">=1,<3"
 requests = ">=2,<3"
 requests-toolbelt = ">=1.0.0,<2.0.0"
 zstandard = ">=0.23.0,<0.24.0"
@@ -3194,38 +3478,98 @@ openai-agents = ["openai-agents (>=0.0.3,<0.1)"]
 otel = ["opentelemetry-api (>=1.30.0,<2.0.0)", "opentelemetry-exporter-otlp-proto-http (>=1.30.0,<2.0.0)", "opentelemetry-sdk (>=1.30.0,<2.0.0)"]
 pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
 
+[[package]]
+name = "lark"
+version = "1.2.2"
+description = "a modern parsing library"
+optional = false
+python-versions = ">=3.8"
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 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
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 packaging = ">=20.0"
-pillow = ">=6.2.0"
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 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
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+
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+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.7"
@@ -3442,14 +3844,14 @@ files = [
 
 [[package]]
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+version = "0.7.22"
 description = "CommonMark compliant Markdown formatter"
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 [package.dependencies]
@@ -3471,14 +3873,14 @@ files = [
 
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+version = "3.1.3"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -3486,14 +3888,14 @@ typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
 
 [[package]]
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-version = "10.5.0"
+version = "10.7.0"
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 [[package]]
@@ -3516,105 +3918,124 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
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 [package.dependencies]
 typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
@@ -3645,40 +4066,53 @@ files = [
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 [[package]]
-name = "multitasking"
-version = "0.0.11"
-description = "Non-blocking Python methods using decorators"
+name = "mypy-extensions"
+version = "1.1.0"
+description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
-python-versions = "*"
-groups = ["main"]
+python-versions = ">=3.8"
+groups = ["main", "dev"]
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 [[package]]
-name = "mypy-extensions"
-version = "1.0.0"
-description = "Type system extensions for programs checked with the mypy type checker."
+name = "narwhals"
+version = "1.47.1"
+description = "Extremely lightweight compatibility layer between dataframe libraries"
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-groups = ["main", "dev"]
+python-versions = ">=3.9"
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+sqlframe = ["sqlframe (>=3.22.0)"]
 
 [[package]]
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+version = "0.10.2"
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 [package.dependencies]
@@ -3689,8 +4123,8 @@ traitlets = ">=5.4"
 
 [package.extras]
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 [[package]]
 name = "nbconvert"
@@ -3766,55 +4200,80 @@ files = [
 
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+version = "3.2.1"
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-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["main"]
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+
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 [[package]]
@@ -4156,7 +4736,7 @@ description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\""
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 files = [
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@@ -4194,14 +4774,14 @@ files = [
 
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 [package.dependencies]
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 typing-extensions = ">=4.11,<5"
 
 [package.extras]
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+realtime = ["websockets (>=13,<16)"]
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+gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
+hdf5 = ["tables (>=3.8.0)"]
+html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
+mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
+parquet = ["pyarrow (>=10.0.1)"]
+performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
+plot = ["matplotlib (>=3.6.3)"]
+postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
+pyarrow = ["pyarrow (>=10.0.1)"]
+spss = ["pyreadstat (>=1.2.0)"]
+sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.9.2)"]
 
 [[package]]
 name = "pandocfilters"
@@ -4425,6 +5018,7 @@ files = [
 ]
 
 [package.dependencies]
+aiohttp = {version = ">=3.9.0", markers = "python_version == \"3.12\""}
 ansicolors = "*"
 click = "*"
 entrypoints = "*"
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 [package.extras]
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-[[package]]
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-description = "a little orm"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
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-
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 version = "4.9.0"
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 optional = false
 python-versions = "*"
 groups = ["main", "dev"]
-markers = "sys_platform != \"win32\""
+markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\" or python_version < \"3.11\" and sys_platform != \"win32\""
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-python-versions = "*"
-groups = ["main", "dev"]
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-
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+version = "11.3.0"
 description = "Python Imaging Library (Fork)"
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-python-versions = ">=3.8"
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+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=8.2)", "sphinx-autobuild", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+test-arrow = ["pyarrow"]
+tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"]
 typing = ["typing-extensions ; python_version < \"3.10\""]
 xmp = ["defusedxml"]
 
@@ -4667,71 +5265,60 @@ testing = ["pytest", "pytest-cov", "wheel"]
 
 [[package]]
 name = "platformdirs"
-version = "4.3.6"
+version = "4.3.8"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
-python-versions = ">=3.8"
-groups = ["main", "dev"]
+python-versions = ">=3.9"
+groups = ["dev"]
 files = [
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+    {file = "platformdirs-4.3.8-py3-none-any.whl", hash = "sha256:ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4"},
+    {file = "platformdirs-4.3.8.tar.gz", hash = "sha256:3d512d96e16bcb959a814c9f348431070822a6496326a4be0911c40b5a74c2bc"},
 ]
 
 [package.extras]
-docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
-type = ["mypy (>=1.11.2)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.4)", "pytest-cov (>=6)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.14.1)"]
 
 [[package]]
 name = "plotly"
-version = "5.24.1"
-description = "An open-source, interactive data visualization library for Python"
+version = "6.2.0"
+description = "An open-source interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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+    {file = "plotly-6.2.0.tar.gz", hash = "sha256:9dfa23c328000f16c928beb68927444c1ab9eae837d1fe648dbcda5360c7953d"},
 ]
 
 [package.dependencies]
+narwhals = ">=1.15.1"
 packaging = "*"
-tenacity = ">=6.2.0"
-
-[[package]]
-name = "plotly-express"
-version = "0.4.1"
-description = "Plotly Express - a high level wrapper for Plotly.py"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
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-]
 
-[package.dependencies]
-numpy = ">=1.11"
-pandas = ">=0.20.0"
-patsy = ">=0.5"
-plotly = ">=4.1.0"
-scipy = ">=0.18"
-statsmodels = ">=0.9.0"
+[package.extras]
+dev = ["plotly[dev-optional]"]
+dev-build = ["build", "jupyter", "plotly[dev-core]"]
+dev-core = ["pytest", "requests", "ruff (==0.11.12)"]
+dev-optional = ["anywidget", "colorcet", "fiona (<=1.9.6) ; python_version <= \"3.8\"", "geopandas", "inflect", "numpy", "orjson", "pandas", "pdfrw", "pillow", "plotly-geo", "plotly[dev-build]", "plotly[kaleido]", "polars[timezone]", "pyarrow", "pyshp", "pytz", "scikit-image", "scipy", "shapely", "statsmodels", "vaex ; python_version <= \"3.9\"", "xarray"]
+express = ["numpy"]
+kaleido = ["kaleido (>=1.0.0)"]
 
 [[package]]
 name = "polars"
-version = "1.8.2"
+version = "1.31.0"
 description = "Blazingly fast DataFrame library"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "polars-1.31.0-cp39-abi3-win_arm64.whl", hash = "sha256:62ef23bb9d10dca4c2b945979f9a50812ac4ace4ed9e158a6b5d32a7322e6f75"},
+    {file = "polars-1.31.0.tar.gz", hash = "sha256:59a88054a5fc0135386268ceefdbb6a6cc012d21b5b44fed4f1d3faabbdcbf32"},
 ]
 
 [package.extras]
@@ -4741,35 +5328,36 @@ async = ["gevent"]
 calamine = ["fastexcel (>=0.9)"]
 cloudpickle = ["cloudpickle"]
 connectorx = ["connectorx (>=0.3.2)"]
-database = ["nest-asyncio", "polars[adbc,connectorx,sqlalchemy]"]
-deltalake = ["deltalake (>=0.15.0)"]
+database = ["polars[adbc,connectorx,sqlalchemy]"]
+deltalake = ["deltalake (>=1.0.0)"]
 excel = ["polars[calamine,openpyxl,xlsx2csv,xlsxwriter]"]
 fsspec = ["fsspec"]
 gpu = ["cudf-polars-cu12"]
 graph = ["matplotlib"]
-iceberg = ["pyiceberg (>=0.5.0)"]
+iceberg = ["pyiceberg (>=0.7.1)"]
 numpy = ["numpy (>=1.16.0)"]
 openpyxl = ["openpyxl (>=3.0.0)"]
 pandas = ["pandas", "polars[pyarrow]"]
 plot = ["altair (>=5.4.0)"]
+polars-cloud = ["polars-cloud (>=0.0.1a1)"]
 pyarrow = ["pyarrow (>=7.0.0)"]
 pydantic = ["pydantic"]
 sqlalchemy = ["polars[pandas]", "sqlalchemy"]
 style = ["great-tables (>=0.8.0)"]
-timezone = ["backports-zoneinfo ; python_version < \"3.9\"", "tzdata ; platform_system == \"Windows\""]
+timezone = ["tzdata ; platform_system == \"Windows\""]
 xlsx2csv = ["xlsx2csv (>=0.8.0)"]
 xlsxwriter = ["xlsxwriter"]
 
 [[package]]
 name = "pre-commit"
-version = "3.5.0"
+version = "3.8.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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+    {file = "pre_commit-3.8.0-py2.py3-none-any.whl", hash = "sha256:9a90a53bf82fdd8778d58085faf8d83df56e40dfe18f45b19446e26bf1b3a63f"},
+    {file = "pre_commit-3.8.0.tar.gz", hash = "sha256:8bb6494d4a20423842e198980c9ecf9f96607a07ea29549e180eef9ae80fe7af"},
 ]
 
 [package.dependencies]
@@ -4781,14 +5369,14 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.21.1"
+version = "0.22.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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-    {file = "prometheus_client-0.21.1.tar.gz", hash = "sha256:252505a722ac04b0456be05c05f75f45d760c2911ffc45f2a06bcaed9f3ae3fb"},
+    {file = "prometheus_client-0.22.1-py3-none-any.whl", hash = "sha256:cca895342e308174341b2cbf99a56bef291fbc0ef7b9e5412a0f26d653ba7094"},
+    {file = "prometheus_client-0.22.1.tar.gz", hash = "sha256:190f1331e783cf21eb60bca559354e0a4d4378facecf78f5428c39b675d20d28"},
 ]
 
 [package.extras]
@@ -4796,14 +5384,14 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.50"
+version = "3.0.51"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
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+    {file = "prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07"},
+    {file = "prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed"},
 ]
 
 [package.dependencies]
@@ -4811,123 +5399,112 @@ wcwidth = "*"
 
 [[package]]
 name = "propcache"
-version = "0.2.0"
+version = "0.3.2"
 description = "Accelerated property cache"
 optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-files = [
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 [[package]]
 name = "psutil"
@@ -4955,43 +5532,44 @@ test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "psygnal"
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 test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
 
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 [package.dependencies]
@@ -5787,19 +6332,20 @@ md = ["cmarkgfm (>=0.8.0)"]
 
 [[package]]
 name = "referencing"
-version = "0.35.1"
+version = "0.36.2"
 description = "JSON Referencing + Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 files = [
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 [package.dependencies]
 attrs = ">=22.2.0"
 rpds-py = ">=0.7.0"
+typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.13\""}
 
 [[package]]
 name = "regex"
@@ -5907,19 +6453,19 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.32.3"
+version = "2.32.4"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
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 [package.dependencies]
 certifi = ">=2017.4.17"
-charset-normalizer = ">=2,<4"
+charset_normalizer = ">=2,<4"
 idna = ">=2.5,<4"
 urllib3 = ">=1.21.1,<3"
 
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 ]
 
+[[package]]
+name = "rfc3987-syntax"
+version = "1.1.0"
+description = "Helper functions to syntactically validate strings according to RFC 3987."
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
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+
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+
+[package.extras]
+testing = ["pytest (>=8.3.5)"]
+
 [[package]]
 name = "rich"
-version = "13.9.4"
+version = "14.0.0"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
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+version = "0.26.0"
 description = "Python bindings to Rust's persistent data structures (rpds)"
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 name = "scorecardpy"
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 scikit-learn = "*"
 scipy = "*"
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 tqdm = ">=4.27.0"
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 test-notebooks = ["datasets", "jupyter", "keras", "nbconvert", "nbformat", "nlp", "transformers"]
 
+[[package]]
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 [[package]]
 name = "six"
 version = "1.17.0"
@@ -6534,14 +7388,14 @@ files = [
 
 [[package]]
 name = "slicer"
-version = "0.0.7"
+version = "0.0.8"
 description = "A small package for big slicing."
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
 files = [
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 [[package]]
@@ -6558,26 +7412,26 @@ files = [
 
 [[package]]
 name = "snowballstemmer"
-version = "2.2.0"
-description = "This package provides 29 stemmers for 28 languages generated from Snowball algorithms."
+version = "3.0.1"
+description = "This package provides 32 stemmers for 30 languages generated from Snowball algorithms."
 optional = false
-python-versions = "*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*"
 groups = ["dev"]
 files = [
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 [[package]]
 name = "soupsieve"
-version = "2.6"
+version = "2.7"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
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 files = [
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 [[package]]
@@ -6657,50 +7511,53 @@ dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
 
 [[package]]
 name = "sphinxcontrib-applehelp"
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+version = "2.0.0"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
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-version = "1.0.2"
-description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
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+description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
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 [package.extras]
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+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
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+version = "2.1.0"
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 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
+standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
 [[package]]
@@ -6735,114 +7592,116 @@ test = ["flake8", "mypy", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.3"
-description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp document."
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 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
-test = ["pytest"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
+standalone = ["Sphinx (>=5)"]
+test = ["defusedxml (>=0.7.1)", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
-version = "1.1.5"
-description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)."
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+description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.9"
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 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
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 python-versions = ">=3.7"
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 typing-extensions = ">=4.6.0"
 
 [package.extras]
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+aioodbc = ["aioodbc", "greenlet (>=1)"]
+aiosqlite = ["aiosqlite", "greenlet (>=1)", "typing_extensions (!=3.10.0.1)"]
+asyncio = ["greenlet (>=1)"]
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 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10)"]
 mssql = ["pyodbc"]
 mssql-pymssql = ["pymssql"]
@@ -6853,7 +7712,7 @@ mysql-connector = ["mysql-connector-python"]
 oracle = ["cx_oracle (>=8)"]
 oracle-oracledb = ["oracledb (>=1.0.1)"]
 postgresql = ["psycopg2 (>=2.7)"]
-postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
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 postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
 postgresql-psycopg = ["psycopg (>=3.0.7)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
@@ -6884,57 +7743,55 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
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+version = "0.14.5"
 description = "Statistical computations and models for Python"
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 packaging = ">=21.3"
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-patsy = ">=0.5.4"
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+patsy = ">=0.5.6"
+scipy = ">=1.8,<1.9.2 || >1.9.2"
 
 [package.extras]
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-docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
+build = ["cython (>=3.0.10)"]
+develop = ["colorama", "cython (>=3.0.10)", "cython (>=3.0.10,<4)", "flake8", "isort", "jinja2", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty ; os_name == \"nt\"", "setuptools_scm[toml] (>=8.0,<9.0)"]
+docs = ["ipykernel", "jupyter_client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
 
 [[package]]
 name = "sympy"
@@ -6971,15 +7828,16 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tenacity"
-version = "8.5.0"
+version = "9.1.2"
 description = "Retry code until it succeeds"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "dev"]
 files = [
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 ]
+markers = {main = "extra == \"all\" or extra == \"llm\""}
 
 [package.extras]
 doc = ["reno", "sphinx"]
@@ -7029,60 +7887,55 @@ tests = ["numpy", "pytest"]
 
 [[package]]
 name = "threadpoolctl"
-version = "3.5.0"
+version = "3.6.0"
 description = "threadpoolctl"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [[package]]
 name = "tiktoken"
-version = "0.7.0"
+version = "0.9.0"
 description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
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 ]
 
 [package.dependencies]
@@ -7248,7 +8004,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
-markers = "python_version <= \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
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@@ -7337,6 +8093,7 @@ nvidia-cusparselt-cu12 = {version = "0.6.3", markers = "platform_system == \"Lin
 nvidia-nccl-cu12 = {version = "2.26.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvjitlink-cu12 = {version = "12.6.85", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 sympy = ">=1.13.3"
 triton = {version = "3.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 typing-extensions = ">=4.10.0"
@@ -7407,72 +8164,75 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.46.3"
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 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
-python-versions = ">=3.8.0"
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 groups = ["main"]
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 ]
 
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 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-ruff = ["ruff (==0.5.1)"]
+ruff = ["ruff (==0.11.2)"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
 serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 tiktoken = ["blobfile", "tiktoken"]
-timm = ["timm (<=0.9.16)"]
-tokenizers = ["tokenizers (>=0.20,<0.21)"]
-torch = ["accelerate (>=0.26.0)", "torch"]
+timm = ["timm (<=1.0.11)"]
+tokenizers = ["tokenizers (>=0.21,<0.22)"]
+torch = ["accelerate (>=0.26.0)", "torch (>=2.1)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
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-video = ["av (==9.2.0)"]
+torchhub = ["filelock", "huggingface-hub (>=0.30.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "tqdm (>=4.27)"]
+video = ["av"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
@@ -7525,26 +8285,26 @@ urllib3 = ">=1.26.0"
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20241206"
+version = "2.9.0.20250708"
 description = "Typing stubs for python-dateutil"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.2"
-description = "Backported and Experimental Type Hints for Python 3.8+"
+version = "4.14.1"
+description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "dev"]
 files = [
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 ]
 
 [[package]]
@@ -7568,10 +8328,9 @@ typing-extensions = ">=3.7.4"
 name = "typing-inspection"
 version = "0.4.1"
 description = "Runtime typing introspection tools"
-optional = true
+optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"all\" or extra == \"llm\""
 files = [
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@@ -7582,14 +8341,14 @@ typing-extensions = ">=4.12.0"
 
 [[package]]
 name = "tzdata"
-version = "2025.1"
+version = "2025.2"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 groups = ["main"]
 files = [
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-    {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
 ]
 
 [[package]]
@@ -7634,14 +8393,14 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.2.3"
+version = "2.5.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "dev"]
 files = [
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+    {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
 ]
 
 [package.extras]
@@ -7652,14 +8411,14 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.29.3"
+version = "20.31.2"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
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-    {file = "virtualenv-20.29.3.tar.gz", hash = "sha256:95e39403fcf3940ac45bc717597dba16110b74506131845d9b687d5e73d947ac"},
+    {file = "virtualenv-20.31.2-py3-none-any.whl", hash = "sha256:36efd0d9650ee985f0cad72065001e66d49a6f24eb44d98980f630686243cf11"},
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 ]
 
 [package.dependencies]
@@ -7669,7 +8428,7 @@ platformdirs = ">=3.9.1,<5"
 
 [package.extras]
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+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\" or platform_python_implementation == \"GraalVM\" or platform_python_implementation == \"CPython\" and sys_platform == \"win32\" and python_version >= \"3.13\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""]
 
 [[package]]
 name = "wcwidth"
@@ -7685,20 +8444,16 @@ files = [
 
 [[package]]
 name = "webcolors"
-version = "24.8.0"
+version = "24.11.1"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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-[package.extras]
-docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["coverage[toml]"]
-
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -7730,23 +8485,24 @@ test = ["websockets"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.13"
+version = "4.0.14"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "dev"]
 files = [
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 ]
 
 [[package]]
 name = "xgboost"
 version = "2.1.4"
 description = "XGBoost Python Package"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"xgboost\""
 files = [
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@@ -7922,154 +8678,134 @@ tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "yarl"
-version = "1.15.2"
+version = "1.20.1"
 description = "Yet another URL library"
 optional = false
-python-versions = ">=3.8"
-groups = ["main"]
+python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
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-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+    {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
+    {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
 ]
 markers = {main = "python_version < \"3.10\""}
 
@@ -8078,7 +8814,7 @@ check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
 type = ["pytest-mypy"]
 
 [[package]]
@@ -8196,12 +8932,14 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
+all = ["langchain-openai", "ragas", "scipy", "sentencepiece", "statsmodels", "torch", "transformers", "xgboost"]
 huggingface = ["sentencepiece", "transformers"]
-llm = ["langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
+llm = ["langchain-openai", "ragas", "sentencepiece", "torch", "transformers"]
 pytorch = ["torch"]
+stats = ["scipy", "statsmodels"]
+xgboost = ["xgboost"]
 
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.9.0,<3.12"
-content-hash = "d44d66b661fc8ddca8f5c66fca73056d9b186e53a5aad0730e5de8209868f8bc"
+python-versions = ">=3.9.0,<3.13"
+content-hash = "1f85934038f4a142c9d81ad320de7ba96b89f8b9fd43ec8491234362a3c5702c"
diff --git a/pyproject.toml b/pyproject.toml
index 37a347b03..c0ed48445 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,51 +13,45 @@ readme = "README.pypi.md"
 version = "2.8.28"
 
 [tool.poetry.dependencies]
-aiohttp = {extras = ["speedups"], version = "*"}
-anywidget = "^0.9.13"
+aiohttp = { extras = ["speedups"], version = "*" }
 arch = "*"
 bert-score = ">=0.3.13"
 catboost = "*"
 datasets = "^2.10.0"
 evaluate = "*"
-h11 = ">=0.16.0"
 ipywidgets = "*"
 kaleido = ">=0.2.1,!=0.2.1.post1"
-langchain-openai = {version = ">=0.1.8", optional = true}
+langchain-openai = { version = ">=0.1.8", optional = true }
 langdetect = "*"
-llvmlite = {version = "*", python = ">=3.8,<=3.11"}
 matplotlib = "*"
 mistune = "^3.0.2"
 nest-asyncio = "^1.6.0"
 nltk = "^3.8.1"
-numba = "<0.59.0" # TODO: https://github.com/validmind/validmind-library/pull/28
-numpy = "*"
+numba = "*"                                                 # TODO: https://github.com/validmind/validmind-library/pull/28
 openai = ">=1"
-pandas = ">=1.1,<=2.0.3"
-plotly = "<6.0.0"
-plotly-express = "*"
+pandas = "^2.0.3"
+plotly = ">=5.0.0"
 polars = "*"
-pycocoevalcap = {version = "^1.2", optional = true}
-python = ">=3.9.0,<3.12"
+python = ">=3.9.0,<3.13"
 python-dotenv = "*"
-ragas = {version = ">=0.2.3,<=0.2.7", optional = true}
+ragas = { version = ">=0.2.3,<=0.2.7", optional = true }
 rouge = ">=1"
-scikit-learn = "*,<1.6.0"
-scipy = "*"
+scikit-learn = "*"
+scipy = { version = "*", optional = true }
 scorecardpy = "^0.1.9.6"
 seaborn = "*"
-sentencepiece = {version = "^0.2.0", optional = true}
+sentencepiece = { version = "^0.2.0", optional = true }
 sentry-sdk = "^1.24.0"
-shap = "0.44.1"
-statsmodels = "*"
+shap = ">=0.46.0"
+statsmodels = { version = "*", optional = true }
 tabulate = "^0.8.9"
 textblob = "^0.18.0.post0"
 tiktoken = "*"
-torch = {version = "2.7.0", optional = true}
+torch = { version = "2.7.0", optional = true }
 tqdm = "*"
-transformers = {version = "^4.32.0", optional = true}
-xgboost = ">=1.5.2,<3"
-yfinance = "^0.2.48"
+transformers = { version = "^4.32.0", optional = true }
+xgboost = { version = ">=1.5.2,<3", optional = true }
+anywidget = "*"
 
 [tool.poetry.group.dev.dependencies]
 black = "^22.1.0"
@@ -81,11 +75,14 @@ twine = "^4.0.2"
 [tool.poetry.extras]
 all = [
   "torch",
+  "xgboost",
   "transformers",
   "pycocoevalcap",
   "ragas",
   "sentencepiece",
   "langchain-openai",
+  "scipy",
+  "statsmodels",
 ]
 huggingface = ["transformers", "sentencepiece"]
 llm = [
@@ -97,6 +94,8 @@ llm = [
   "langchain-openai",
 ]
 pytorch = ["torch"]
+stats = ["scipy", "statsmodels"]
+xgboost = ["xgboost"]
 
 [build-system]
 build-backend = "poetry.core.masonry.api"
diff --git a/validmind/tests/data_validation/ChiSquaredFeaturesTable.py b/validmind/tests/data_validation/ChiSquaredFeaturesTable.py
index 2e6620a25..e3251a9e4 100644
--- a/validmind/tests/data_validation/ChiSquaredFeaturesTable.py
+++ b/validmind/tests/data_validation/ChiSquaredFeaturesTable.py
@@ -4,10 +4,22 @@
 
 
 import pandas as pd
-from scipy.stats import chi2_contingency
 
 from validmind import tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
+
+try:
+    from scipy.stats import chi2_contingency
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for ChiSquaredFeaturesTable. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
 
 
 @tags("tabular_data", "categorical_data", "statistical_test")
diff --git a/validmind/tests/data_validation/SeasonalDecompose.py b/validmind/tests/data_validation/SeasonalDecompose.py
index bc0bc3f26..24e652bd1 100644
--- a/validmind/tests/data_validation/SeasonalDecompose.py
+++ b/validmind/tests/data_validation/SeasonalDecompose.py
@@ -9,11 +9,23 @@
 import pandas as pd
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-from scipy import stats
 from statsmodels.tsa.seasonal import seasonal_decompose
 
 from validmind import RawData, tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
+
+try:
+    from scipy import stats
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for SeasonalDecompose. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
 from validmind.logging import get_logger
 from validmind.vm_models import VMDataset
 
diff --git a/validmind/tests/data_validation/ShapiroWilk.py b/validmind/tests/data_validation/ShapiroWilk.py
index 909d097c0..a87b6e551 100644
--- a/validmind/tests/data_validation/ShapiroWilk.py
+++ b/validmind/tests/data_validation/ShapiroWilk.py
@@ -6,9 +6,22 @@
 from typing import Tuple
 
 import pandas as pd
-from scipy import stats
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    from scipy import stats
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for ShapiroWilk. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
 
 
 @tasks("classification", "regression")
diff --git a/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py b/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py
index 75d15eb87..3f30ee8ac 100644
--- a/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py
+++ b/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py
@@ -3,9 +3,22 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import pandas as pd
-from scipy.stats import kurtosis, skew
 
 from validmind import tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    from scipy.stats import kurtosis, skew
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for TimeSeriesDescriptiveStatistics. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
 
 
 @tags("time_series_data", "analysis")
diff --git a/validmind/tests/model_validation/ModelPredictionResiduals.py b/validmind/tests/model_validation/ModelPredictionResiduals.py
index e0db2a1e2..4a2295b36 100644
--- a/validmind/tests/model_validation/ModelPredictionResiduals.py
+++ b/validmind/tests/model_validation/ModelPredictionResiduals.py
@@ -6,11 +6,24 @@
 
 import pandas as pd
 import plotly.graph_objects as go
-from scipy.stats import kstest
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    from scipy.stats import kstest
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for ModelPredictionResiduals. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
+
 
 @tags("regression")
 @tasks("residual_analysis", "visualization")
diff --git a/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py b/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py
index c5f28b96d..f079cfa88 100644
--- a/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py
+++ b/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py
@@ -7,11 +7,24 @@
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
-from scipy.stats import norm
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    from scipy.stats import norm
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for TimeSeriesPredictionWithCI. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
+
 
 @tags("model_predictions", "visualization")
 @tasks("regression", "time_series_forecasting")
diff --git a/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py b/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py
index 6d7496451..a05044f74 100644
--- a/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py
+++ b/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py
@@ -7,12 +7,24 @@
 import numpy as np
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-from scipy.spatial.distance import cdist
 from sklearn import clone
 from sklearn.metrics import silhouette_score
 
 from validmind import RawData, tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
+
+try:
+    from scipy.spatial.distance import cdist
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for KMeansClustersOptimization. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
 from validmind.vm_models import VMDataset, VMModel
 
 
diff --git a/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py b/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py
index 8aa4a25c8..0f354671b 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py
@@ -7,10 +7,22 @@
 
 import pandas as pd
 import plotly.graph_objects as go
-from scipy import stats
 
 from validmind import RawData, tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
+
+try:
+    from scipy import stats
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for RegressionCoeffs. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
 from validmind.vm_models import VMModel
 
 
diff --git a/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py b/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py
index ee48eedaf..496df6223 100644
--- a/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py
+++ b/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py
@@ -6,13 +6,26 @@
 
 import numpy as np
 import pandas as pd
-from scipy import stats
 from sklearn.metrics import roc_auc_score
 from sklearn.preprocessing import LabelBinarizer
 
 from validmind import tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    from scipy import stats
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for ClassDiscriminationDrift. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
+
 
 def multiclass_roc_auc_score(y_test, y_pred, average="macro"):
     lb = LabelBinarizer()
diff --git a/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py b/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py
index e3b56f41e..78ab49c8b 100644
--- a/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py
+++ b/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py
@@ -8,11 +8,24 @@
 import pandas as pd
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-from scipy import stats
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    from scipy import stats
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for PredictionProbabilitiesHistogramDrift. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
+
 
 @tags("visualization", "credit_risk")
 @tasks("classification")
diff --git a/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py b/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py
index ded42cf64..09d18f998 100644
--- a/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py
+++ b/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py
@@ -8,11 +8,24 @@
 import pandas as pd
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-from scipy import stats
 
 from validmind import tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset
 
+try:
+    from scipy import stats
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for ScorecardHistogramDrift. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
+
 
 @tags("visualization", "credit_risk", "logistic_regression")
 @tasks("classification")
diff --git a/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py b/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py
index 55beee0aa..6c3106944 100644
--- a/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py
+++ b/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py
@@ -7,11 +7,24 @@
 import pandas as pd
 import plotly.figure_factory as ff
 import plotly.graph_objects as go
-from scipy.stats import kurtosis, skew
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    from scipy.stats import kurtosis, skew
+except ImportError as e:
+    if "scipy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scipy` for TargetPredictionDistributionPlot. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["scipy"],
+            extra="stats",
+        ) from e
+
+    raise e
+
 
 @tags("visualization")
 @tasks("monitoring")

From 8d3b48b30eff42652f65144d91466d295d073fea Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Tue, 29 Jul 2025 11:29:28 -0400
Subject: [PATCH 02/47] feat: adding testing with tox

---
 .github/workflows/dependency-testing.yaml | 224 ++++++++++++++++++++++
 .github/workflows/python.yaml             |   8 +
 .vscode/settings.json                     |   6 +-
 Makefile                                  |  22 ++-
 README.md                                 |  29 +++
 poetry.lock                               | 129 ++++++++++++-
 pyproject.toml                            |   1 +
 tox.ini                                   |  86 +++++++++
 8 files changed, 502 insertions(+), 3 deletions(-)
 create mode 100644 .github/workflows/dependency-testing.yaml
 create mode 100644 tox.ini

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
new file mode 100644
index 000000000..dd31f4618
--- /dev/null
+++ b/.github/workflows/dependency-testing.yaml
@@ -0,0 +1,224 @@
+name: Dependency Compatibility Testing
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'pyproject.toml'
+      - 'requirements/**'
+      - 'tox.ini'
+      - 'validmind/**'
+      - 'tests/**'
+  pull_request:
+    branches: ['*']
+    paths:
+      - 'pyproject.toml'
+      - 'requirements/**'
+      - 'tox.ini'
+      - 'validmind/**'
+      - 'tests/**'
+  schedule:
+    # Run weekly on Sundays at 2 AM UTC to catch new dependency releases
+    - cron: '0 2 * * 0'
+  workflow_dispatch:
+    inputs:
+      test_type:
+        description: 'Type of dependency test to run'
+        required: true
+        default: 'all'
+        type: choice
+        options:
+          - 'all'
+          - 'min-versions'
+          - 'max-versions'
+          - 'python-versions'
+          - 'pip-freeze'
+
+permissions:
+  contents: read
+
+jobs:
+  dependency-matrix:
+    name: Test Python ${{ matrix.python-version }} with ${{ matrix.deps-type }} dependencies
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ['3.9', '3.10', '3.11', '3.12']
+        deps-type: ['min', 'max', 'default']
+        exclude:
+          # Skip some combinations to reduce CI time
+          - python-version: '3.10'
+            deps-type: 'default'
+          - python-version: '3.11'
+            deps-type: 'default'
+    
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
+
+      - name: Install tox
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox
+
+      - name: Run dependency tests
+        run: |
+          tox -e py$(echo ${{ matrix.python-version }} | tr -d .)-deps-${{ matrix.deps-type }}
+        env:
+          # Limit OpenMP threads to prevent CI resource issues
+          OMP_NUM_THREADS: 1
+          MKL_NUM_THREADS: 1
+
+  pip-freeze-testing:
+    name: Test Client Pip Freeze Environments
+    runs-on: ubuntu-latest
+    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'pip-freeze' || github.event_name == 'schedule'
+    
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+          cache: 'pip'
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
+
+      - name: Install tox
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox
+
+      - name: Test pip freeze environments
+        run: |
+          tox -e pip-freeze-test
+        env:
+          OMP_NUM_THREADS: 1
+          MKL_NUM_THREADS: 1
+
+  min-versions-only:
+    name: Test Minimum Dependency Versions
+    runs-on: ubuntu-latest
+    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'min-versions'
+    
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+          cache: 'pip'
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
+
+      - name: Install tox
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox
+
+      - name: Test minimum versions
+        run: |
+          make test-deps-min
+        env:
+          OMP_NUM_THREADS: 1
+          MKL_NUM_THREADS: 1
+
+  max-versions-only:
+    name: Test Maximum Dependency Versions  
+    runs-on: ubuntu-latest
+    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'max-versions'
+    
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+          cache: 'pip'
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
+
+      - name: Install tox
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox
+
+      - name: Test maximum versions
+        run: |
+          make test-deps-max
+        env:
+          OMP_NUM_THREADS: 1
+          MKL_NUM_THREADS: 1
+
+  python-versions-only:
+    name: Test All Python Versions with Default Dependencies
+    runs-on: ubuntu-latest
+    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'python-versions'
+    
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+          cache: 'pip'
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
+
+      - name: Install tox
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox
+
+      - name: Test all Python versions
+        run: |
+          make test-python-versions
+        env:
+          OMP_NUM_THREADS: 1
+          MKL_NUM_THREADS: 1
+
+  report-status:
+    name: Report Dependency Testing Status
+    runs-on: ubuntu-latest
+    needs: [dependency-matrix]
+    if: always()
+    
+    steps:
+      - name: Report results
+        run: |
+          echo "Dependency testing completed"
+          echo "Matrix job status: ${{ needs.dependency-matrix.result }}"
+          
+          if [[ "${{ needs.dependency-matrix.result }}" == "failure" ]]; then
+            echo "❌ Some dependency combinations failed"
+            exit 1
+          else
+            echo "✅ All dependency combinations passed"
+          fi 
\ No newline at end of file
diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 2905fb354..2476f0f62 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -76,6 +76,14 @@ jobs:
       - name: Unit Tests
         run: make test
 
+      - name: Test Minimum Dependencies (Python 3.9)
+        run: |
+          poetry run tox -e py39-min
+
+      - name: Test Latest Dependencies (Python 3.9)  
+        run: |
+          poetry run tox -e py39-max
+
       # Tests that we can build the docs
       - name: Generate Docs
         run: make docs
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 480d07375..107b4f8ce 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -7,5 +7,9 @@
   "python.analysis.typeCheckingMode": "off",
   "python.analysis.extraPaths": [
     "./tests"
-  ]
+  ],
+  "cursorpyright.analysis.extraPaths": [
+    "./tests"
+  ],
+  "cursorpyright.analysis.typeCheckingMode": "off"
 }
diff --git a/Makefile b/Makefile
index f2a8f0652..54290ecf4 100644
--- a/Makefile
+++ b/Makefile
@@ -87,10 +87,30 @@ verify-exposed-credentials:
 ensure-clean-notebooks:
 	poetry run python scripts/ensure_clean_notebooks.py
 
+# Dependency testing with tox
+test-deps-min:
+	poetry run tox -e py39-min,py310-min,py311-min,py312-min
+
+test-deps-max:
+	poetry run tox -e py39-max,py310-max,py311-max,py312-max
+
+test-python-versions:
+	poetry run tox -e py39,py310,py311,py312
+
+test-freeze-env:
+ifdef FREEZE_FILE
+	FREEZE_FILE=$(FREEZE_FILE) poetry run tox -e freeze
+else
+	@echo "Usage: make test-freeze-env FREEZE_FILE=path/to/requirements.txt"
+endif
+
+test-tox-all:
+	poetry run tox
+
 # Quick target to run all checks
 check: copyright format lint test verify-copyright verify-exposed-credentials ensure-clean-notebooks
 
-.PHONY: docs quarto-docs
+.PHONY: docs quarto-docs test-deps-min test-deps-max test-python-versions test-freeze-env test-tox-all
 
 notebook:
 	@python notebooks/templates/e2e_template.py
diff --git a/README.md b/README.md
index 767c9b947..94212b6cf 100644
--- a/README.md
+++ b/README.md
@@ -119,6 +119,35 @@ The value of `tag` corresponds to one of the options provided by Poetry: <https:
 
 ## Generating API Reference Docs
 
+## Dependency Testing
+
+Test compatibility across different Python versions and dependency combinations:
+
+```bash
+# Test all Python versions with default dependencies
+make test-python-versions
+
+# Test minimum supported dependency versions
+make test-deps-min
+
+# Test latest dependency versions  
+make test-deps-max
+
+# Test with a specific pip freeze file
+make test-freeze-env FREEZE_FILE=requirements.txt
+
+# Run all tox environments
+make test-tox-all
+```
+
+### Available tox environments:
+- `py{39,310,311,312}` - Test with default Poetry-managed dependencies
+- `py{39,310,311,312}-min` - Test with minimum supported dependency versions
+- `py{39,310,311,312}-max` - Test with latest dependency versions
+- `freeze` - Test with specific pip freeze file (requires `FREEZE_FILE` env var)
+
+## Documentation
+
 The [API reference documentation](https://docs.validmind.ai/validmind/validmind.html) you see in our docs site is generated in HTML format with `pdoc` with the following
 command:
 
diff --git a/poetry.lock b/poetry.lock
index 05e746eb3..c9c82197a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -597,6 +597,10 @@ files = [
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:a37b8f0391212d29b3a91a799c8e4a2855e0576911cdfb2515487e30e322253d"},
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e84799f09591700a4154154cab9787452925578841a94321d5ee8fb9a9a328f0"},
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+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:5dab0844f2cf82be357a0eb11a9087f70c5430b2c241493fc122bb6f2bb0917c"},
+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e4fe605b917c70283db7dfe5ada75e04561479075761a0b3866c081d035b01c1"},
+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:1e9a65b5736232e7a7f91ff3d02277f11d339bf34099a56cdab6a8b3410a02b2"},
+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:58d4b711689366d4a03ac7957ab8c28890415e267f9b6589969e74b6e42225ec"},
     {file = "Brotli-1.1.0-cp310-cp310-win32.whl", hash = "sha256:be36e3d172dc816333f33520154d708a2657ea63762ec16b62ece02ab5e4daf2"},
     {file = "Brotli-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:0c6244521dda65ea562d5a69b9a26120769b7a9fb3db2fe9545935ed6735b128"},
     {file = "Brotli-1.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a3daabb76a78f829cafc365531c972016e4aa8d5b4bf60660ad8ecee19df7ccc"},
@@ -609,8 +613,14 @@ files = [
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+    {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c247dd99d39e0338a604f8c2b3bc7061d5c2e9e2ac7ba9cc1be5a69cb6cd832f"},
+    {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:1b2c248cd517c222d89e74669a4adfa5577e06ab68771a529060cf5a156e9757"},
+    {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:2a24c50840d89ded6c9a8fdc7b6ed3692ed4e86f1c4a4a938e1e92def92933e0"},
+    {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f31859074d57b4639318523d6ffdca586ace54271a73ad23ad021acd807eb14b"},
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     {file = "Brotli-1.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:aac0411d20e345dc0920bdec5548e438e999ff68d77564d5e9463a7ca9d3e7b1"},
+    {file = "Brotli-1.1.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:32d95b80260d79926f5fab3c41701dbb818fde1c9da590e77e571eefd14abe28"},
+    {file = "Brotli-1.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b760c65308ff1e462f65d69c12e4ae085cff3b332d894637f6273a12a482d09f"},
     {file = "Brotli-1.1.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:316cc9b17edf613ac76b1f1f305d2a748f1b976b033b049a6ecdfd5612c70409"},
     {file = "Brotli-1.1.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:caf9ee9a5775f3111642d33b86237b05808dafcd6268faa492250e9b78046eb2"},
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@@ -621,8 +631,24 @@ files = [
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@@ -655,6 +689,10 @@ files = [
     {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1ab4fbee0b2d9098c74f3057b2bc055a8bd92ccf02f65944a241b4349229185a"},
     {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:141bd4d93984070e097521ed07e2575b46f817d08f9fa42b16b9b5f27b5ac088"},
     {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fce1473f3ccc4187f75b4690cfc922628aed4d3dd013d047f95a9b3919a86596"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:d2b35ca2c7f81d173d2fadc2f4f31e88cc5f7a39ae5b6db5513cf3383b0e0ec7"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:af6fa6817889314555aede9a919612b23739395ce767fe7fcbea9a80bf140fe5"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:2feb1d960f760a575dbc5ab3b1c00504b24caaf6986e2dc2b01c09c87866a943"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:4410f84b33374409552ac9b6903507cdb31cd30d2501fc5ca13d18f73548444a"},
     {file = "Brotli-1.1.0-cp38-cp38-win32.whl", hash = "sha256:db85ecf4e609a48f4b29055f1e144231b90edc90af7481aa731ba2d059226b1b"},
     {file = "Brotli-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:3d7954194c36e304e1523f55d7042c59dc53ec20dd4e9ea9d151f1b62b4415c0"},
     {file = "Brotli-1.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5fb2ce4b8045c78ebbc7b8f3c15062e435d47e7393cc57c25115cfd49883747a"},
@@ -667,6 +705,10 @@ files = [
     {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:949f3b7c29912693cee0afcf09acd6ebc04c57af949d9bf77d6101ebb61e388c"},
     {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:89f4988c7203739d48c6f806f1e87a1d96e0806d44f0fba61dba81392c9e474d"},
     {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:de6551e370ef19f8de1807d0a9aa2cdfdce2e85ce88b122fe9f6b2b076837e59"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0737ddb3068957cf1b054899b0883830bb1fec522ec76b1098f9b6e0f02d9419"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:4f3607b129417e111e30637af1b56f24f7a49e64763253bbc275c75fa887d4b2"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:6c6e0c425f22c1c719c42670d561ad682f7bfeeef918edea971a79ac5252437f"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:494994f807ba0b92092a163a0a283961369a65f6cbe01e8891132b7a320e61eb"},
     {file = "Brotli-1.1.0-cp39-cp39-win32.whl", hash = "sha256:f0d8a7a6b5983c2496e364b969f0e526647a06b075d034f3297dc66f3b360c64"},
     {file = "Brotli-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:cdad5b9014d83ca68c25d2e9444e28e967ef16e80f6b436918c700c117a85467"},
     {file = "Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724"},
@@ -713,6 +755,18 @@ files = [
 [package.dependencies]
 cffi = ">=1.0.0"
 
+[[package]]
+name = "cachetools"
+version = "6.1.0"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+    {file = "cachetools-6.1.0-py3-none-any.whl", hash = "sha256:1c7bb3cf9193deaf3508b7c5f2a79986c13ea38965c5adcff1f84519cf39163e"},
+    {file = "cachetools-6.1.0.tar.gz", hash = "sha256:b4c4f404392848db3ce7aac34950d17be4d864da4b8b66911008e430bc544587"},
+]
+
 [[package]]
 name = "catboost"
 version = "1.2.8"
@@ -864,6 +918,18 @@ files = [
     {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
 ]
 
+[[package]]
+name = "chardet"
+version = "5.2.0"
+description = "Universal encoding detector for Python 3"
+optional = false
+python-versions = ">=3.7"
+groups = ["dev"]
+files = [
+    {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
+    {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
+]
+
 [[package]]
 name = "charset-normalizer"
 version = "3.4.2"
@@ -5304,6 +5370,22 @@ dev-optional = ["anywidget", "colorcet", "fiona (<=1.9.6) ; python_version <= \"
 express = ["numpy"]
 kaleido = ["kaleido (>=1.0.0)"]
 
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+description = "plugin and hook calling mechanisms for python"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+    {file = "pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746"},
+    {file = "pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3"},
+]
+
+[package.extras]
+dev = ["pre-commit", "tox"]
+testing = ["coverage", "pytest", "pytest-benchmark"]
+
 [[package]]
 name = "polars"
 version = "1.31.0"
@@ -5990,6 +6072,26 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
+[[package]]
+name = "pyproject-api"
+version = "1.9.1"
+description = "API to interact with the python pyproject.toml based projects"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+    {file = "pyproject_api-1.9.1-py3-none-any.whl", hash = "sha256:7d6238d92f8962773dd75b5f0c4a6a27cce092a14b623b811dba656f3b628948"},
+    {file = "pyproject_api-1.9.1.tar.gz", hash = "sha256:43c9918f49daab37e302038fc1aed54a8c7a91a9fa935d00b9a485f37e0f5335"},
+]
+
+[package.dependencies]
+packaging = ">=25"
+tomli = {version = ">=2.2.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+docs = ["furo (>=2024.8.6)", "sphinx-autodoc-typehints (>=3.2)"]
+testing = ["covdefaults (>=2.3)", "pytest (>=8.3.5)", "pytest-cov (>=6.1.1)", "pytest-mock (>=3.14)", "setuptools (>=80.3.1)"]
+
 [[package]]
 name = "pysbd"
 version = "0.3.4"
@@ -8124,6 +8226,31 @@ files = [
     {file = "tornado-6.5.1.tar.gz", hash = "sha256:84ceece391e8eb9b2b95578db65e920d2a61070260594819589609ba9bc6308c"},
 ]
 
+[[package]]
+name = "tox"
+version = "4.28.3"
+description = "tox is a generic virtualenv management and test command line tool"
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+files = [
+    {file = "tox-4.28.3-py3-none-any.whl", hash = "sha256:1debe9daf0b7e64d425ef99a17292b0792385686b1d541df34c7298211e99269"},
+    {file = "tox-4.28.3.tar.gz", hash = "sha256:b91db7219e5242002cf4040a299c8852026d6af35fcd21274d456fb62dafee7b"},
+]
+
+[package.dependencies]
+cachetools = ">=6.1"
+chardet = ">=5.2"
+colorama = ">=0.4.6"
+filelock = ">=3.18"
+packaging = ">=25"
+platformdirs = ">=4.3.8"
+pluggy = ">=1.6"
+pyproject-api = ">=1.9.1"
+tomli = {version = ">=2.2.1", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.14.1", markers = "python_version < \"3.11\""}
+virtualenv = ">=20.31.2"
+
 [[package]]
 name = "tqdm"
 version = "4.67.1"
@@ -8942,4 +9069,4 @@ xgboost = ["xgboost"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9.0,<3.13"
-content-hash = "1f85934038f4a142c9d81ad320de7ba96b89f8b9fd43ec8491234362a3c5702c"
+content-hash = "08fa1ad9af71a111947dffbbf5e3e2c09475ed680238415cf2d65b3d0e0002d9"
diff --git a/pyproject.toml b/pyproject.toml
index caa174de9..939a49c3b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,6 +70,7 @@ pre-commit = "^3.3.3"
 sphinx = "^6.1.3"
 sphinx-markdown-builder = "^0.5.5"
 sphinx-rtd-theme = "^1.2.0"
+tox = "^4.0.0"
 twine = "^4.0.2"
 
 [tool.poetry.extras]
diff --git a/tox.ini b/tox.ini
new file mode 100644
index 000000000..21c1a8d5a
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,86 @@
+[tox]
+envlist = 
+    py{39,310,311,312}
+    py{39,310,311,312}-min
+    py{39,310,311,312}-max
+    docs
+    lint
+isolated_build = true
+skip_missing_interpreters = true
+
+[testenv]
+deps = 
+    poetry
+setenv =
+    # Limit OpenMP on Mac to prevent segfaults
+    OMP_NUM_THREADS = 1
+    MKL_NUM_THREADS = 1
+allowlist_externals = 
+    poetry
+    pip
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install --all-extras
+commands =
+    poetry run python -m unittest discover tests
+    poetry run python -m unittest tests.test_unit_tests
+
+[testenv:py{39,310,311,312}-min]
+description = Test with minimum supported dependency versions
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install --all-extras
+    # Install minimum versions of key dependencies
+    pip install pandas==2.0.3 numpy==1.21.0 scikit-learn==0.24.0 matplotlib==3.5.0
+    pip install transformers==4.32.0 torch==2.0.0 datasets==2.10.0 xgboost==1.5.2
+    pip install nltk==3.8.1 plotly==5.0.0 scipy==1.7.0 statsmodels==0.13.0
+
+[testenv:py{39,310,311,312}-max]
+description = Test with latest dependency versions
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install --all-extras
+    # Upgrade to latest versions of key dependencies
+    pip install --upgrade pandas numpy scikit-learn matplotlib transformers
+    pip install --upgrade torch datasets xgboost nltk plotly scipy statsmodels
+
+[testenv:docs]
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install --all-extras
+commands =
+    poetry run make docs
+
+[testenv:lint]
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry  
+    poetry config virtualenvs.create false
+    poetry install --all-extras
+commands =
+    poetry run make lint
+    poetry run make verify-copyright
+    poetry run make verify-exposed-credentials
+
+[testenv:freeze]
+description = Test with a specific pip freeze file (specify via FREEZE_FILE env var)
+allowlist_externals = 
+    poetry
+    pip
+    bash
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    bash -c 'if [ -n "$FREEZE_FILE" ] && [ -f "$FREEZE_FILE" ]; then pip install -r "$FREEZE_FILE"; else echo "Set FREEZE_FILE env var to pip freeze file path"; exit 1; fi'
+    poetry install --no-deps
+commands =
+    poetry run python -m unittest discover tests -v 
\ No newline at end of file

From ce35462a043975f1ec6119fdbf5410f9ea83a394 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 13:51:57 -0400
Subject: [PATCH 03/47] fix: using new plotly express module thats included in
 plotly

---
 .../quickstart_option_pricing_models.ipynb    |    4 +-
 .../custom_tests/implement_custom_tests.ipynb |    4 +-
 .../integrate_external_test_providers.ipynb   |    8 +-
 .../post_processing_functions.ipynb           |    2 +-
 .../quickstart_model_documentation.ipynb      |   43 +-
 ...uickstart_model_documentation.ipynb.backup |  867 +++++++++++
 .../quickstart_model_documentation.out.ipynb  | 1367 +++++++++++++++++
 7 files changed, 2281 insertions(+), 14 deletions(-)
 create mode 100644 notebooks/quickstart/quickstart_model_documentation.ipynb.backup
 create mode 100644 notebooks/quickstart/quickstart_model_documentation.out.ipynb

diff --git a/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb b/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb
index bcb9dcf2c..cab17f34c 100644
--- a/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb
+++ b/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb
@@ -1016,12 +1016,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "d96969b7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from plotly_express import bar\n",
+    "from plotly.express import bar\n",
     "from validmind.vm_models.figure import Figure\n",
     "from validmind.vm_models.result import TestResult\n",
     "import plotly.graph_objects as go\n",
diff --git a/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb b/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb
index 805a7771f..5559bdae1 100644
--- a/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb
+++ b/notebooks/code_samples/custom_tests/implement_custom_tests.ipynb
@@ -679,7 +679,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import plotly_express as px\n",
+    "import plotly.express as px\n",
     "\n",
     "\n",
     "@vm.test(\"my_custom_tests.ParameterExample\")\n",
@@ -737,7 +737,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import plotly_express as px\n",
+    "import plotly.express as px\n",
     "\n",
     "\n",
     "@vm.test(\"my_custom_tests.ComplexOutput\")\n",
diff --git a/notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb b/notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb
index 6a26471a7..0d4755848 100644
--- a/notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb
+++ b/notebooks/code_samples/custom_tests/integrate_external_test_providers.ipynb
@@ -408,7 +408,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import plotly_express as px\n",
+    "import plotly.express as px\n",
     "\n",
     "\n",
     "@test(\"my_custom_tests.ParameterExample\")\n",
@@ -427,7 +427,7 @@
     "    )\n",
     "\n",
     "\n",
-    "parameter_example.save(tests_folder, imports=[\"import plotly_express as px\"])"
+    "parameter_example.save(tests_folder, imports=[\"import plotly.express as px\"])"
    ]
   },
   {
@@ -437,7 +437,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import plotly_express as px\n",
+    "import plotly.express as px\n",
     "\n",
     "\n",
     "@test(\"my_custom_tests.ComplexOutput\")\n",
@@ -466,7 +466,7 @@
     "\n",
     "\n",
     "complex_output.save(\n",
-    "    tests_folder, imports=[\"import numpy as np\", \"import plotly_express as px\"]\n",
+    "    tests_folder, imports=[\"import numpy as np\", \"import plotly.express as px\"]\n",
     ")"
    ]
   },
diff --git a/notebooks/code_sharing/post_processing_functions.ipynb b/notebooks/code_sharing/post_processing_functions.ipynb
index 3e8724eaf..555a5671a 100644
--- a/notebooks/code_sharing/post_processing_functions.ipynb
+++ b/notebooks/code_sharing/post_processing_functions.ipynb
@@ -277,7 +277,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from plotly_express import bar\n",
+    "from plotly.express import bar\n",
     "\n",
     "\n",
     "def create_figure(result: TestResult):\n",
diff --git a/notebooks/quickstart/quickstart_model_documentation.ipynb b/notebooks/quickstart/quickstart_model_documentation.ipynb
index b32baae8a..4b939c320 100644
--- a/notebooks/quickstart/quickstart_model_documentation.ipynb
+++ b/notebooks/quickstart/quickstart_model_documentation.ipynb
@@ -173,6 +173,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "57bdd8f8",
    "metadata": {},
    "source": [
     "<a id='toc3_1_'></a>\n",
@@ -210,6 +211,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "bc5c8bd4",
    "metadata": {},
    "source": [
     "<a id='toc3_2_1_'></a>\n",
@@ -262,12 +264,36 @@
     "\n",
     "import validmind as vm\n",
     "\n",
+    "\n",
+    "import os\n",
+    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
+    "import validmind as vm\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
+    "import validmind as vm\n",
+    "\n",
     "vm.init(\n",
-    "    # api_host=\"...\",\n",
-    "    # api_key=\"...\",\n",
-    "    # api_secret=\"...\",\n",
-    "    # model=\"...\",\n",
-    ")"
+    "  api_host = \"https://api.dev.vm.validmind.ai/api/v1/tracking\",\n",
+    "  api_key = \"dea7a1c03b4bff0e4b969c9719d89492\",\n",
+    "  api_secret = \"65c3deb6f1c579a5e8a2aa3989c04068e77c42d5ab5e7c89ffd17aff25b291cc\",\n",
+    "  model = \"cmafl60cm000u1fcseem2u3j6\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "import logging\n",
+    "import sys\n",
+    "import site\n",
+    "\n",
+    "logger = logging.getLogger()\n",
+    "\n",
+    "# Print the path to the current Python interpreter\n",
+    "logger.info(\"Python executable path: \" + sys.executable)\n",
+    "\n",
+    "# Print the path to the site-packages directory\n",
+    "logger.info(\"Site-packages path: \" + str(site.getsitepackages()))\n",
+    "\n"
    ]
   },
   {
@@ -455,6 +481,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "541466ce",
    "metadata": {},
    "source": [
     "<a id='toc7_'></a>\n",
@@ -508,6 +535,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "78c85786",
    "metadata": {},
    "source": [
     "<a id='toc7_2_'></a>\n",
@@ -537,6 +565,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c6662e6b",
    "metadata": {},
    "source": [
     "<a id='toc8_'></a>\n",
@@ -585,6 +614,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5da79af3",
    "metadata": {},
    "source": [
     "<a id='toc9_'></a>\n",
@@ -611,6 +641,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5fa481bf",
    "metadata": {},
    "source": [
     "<a id='toc9_1_'></a>\n",
@@ -643,6 +674,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a5ade4b3",
    "metadata": {},
    "source": [
     "<a id='toc10_'></a>\n",
@@ -782,6 +814,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "790f47cb",
    "metadata": {},
    "source": [
     "<a id='toc13_'></a>\n",
diff --git a/notebooks/quickstart/quickstart_model_documentation.ipynb.backup b/notebooks/quickstart/quickstart_model_documentation.ipynb.backup
new file mode 100644
index 000000000..cc4dca5eb
--- /dev/null
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2c17b2d",
+   "metadata": {},
+   "source": [
+    "# Quickstart for model documentation\n",
+    "\n",
+    "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.\n",
+    "\n",
+    "To document a model with the ValidMind Library, we'll:\n",
+    "\n",
+    "1. Import a sample dataset and preprocess it\n",
+    "2. Split the datasets and initialize them for use with ValidMind\n",
+    "3. Initialize a model object for use with testing\n",
+    "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05dce32a",
+   "metadata": {},
+   "source": [
+    "::: {.content-hidden when-format=\"html\"}\n",
+    "## Contents    \n",
+    "- [Introduction](#toc1_)    \n",
+    "- [About ValidMind](#toc2_)    \n",
+    "  - [Before you begin](#toc2_1_)    \n",
+    "  - [New to ValidMind?](#toc2_2_)    \n",
+    "  - [Key concepts](#toc2_3_)    \n",
+    "- [Setting up](#toc3_)    \n",
+    "  - [Install the ValidMind Library](#toc3_1_)    \n",
+    "  - [Initialize the ValidMind Library](#toc3_2_)    \n",
+    "    - [Get your code snippet](#toc3_2_1_)    \n",
+    "  - [Initialize the Python environment](#toc3_3_)    \n",
+    "- [Getting to know ValidMind](#toc4_)    \n",
+    "  - [Preview the documentation template](#toc4_1_)    \n",
+    "  - [View model documentation in the ValidMind Platform](#toc4_2_)    \n",
+    "- [Import the sample dataset](#toc5_)    \n",
+    "- [Preprocessing the raw dataset](#toc6_)    \n",
+    "  - [Split the dataset](#toc6_1_)    \n",
+    "  - [Separate features and targets](#toc6_2_)    \n",
+    "- [Training an XGBoost classifier model](#toc7_)    \n",
+    "  - [Set evaluation metrics](#toc7_1_)    \n",
+    "  - [Fit the model](#toc7_2_)    \n",
+    "- [Initialize the ValidMind datasets](#toc8_)    \n",
+    "- [Initialize a model object](#toc9_)    \n",
+    "  - [Assign predictions](#toc9_1_)    \n",
+    "- [Run the full suite of tests](#toc10_)    \n",
+    "- [In summary](#toc11_)    \n",
+    "- [Next steps](#toc12_)    \n",
+    "  - [Work with your model documentation](#toc12_1_)    \n",
+    "  - [Discover more learning resources](#toc12_2_)    \n",
+    "- [Upgrade ValidMind](#toc13_)    \n",
+    "\n",
+    ":::\n",
+    "<!-- jn-toc-notebook-config\n",
+    "\tnumbering=false\n",
+    "\tanchor=true\n",
+    "\tflat=false\n",
+    "\tminLevel=2\n",
+    "\tmaxLevel=4\n",
+    "\t/jn-toc-notebook-config -->\n",
+    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1919918",
+   "metadata": {},
+   "source": [
+    "<a id='toc1_'></a>\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "Model development aims to produce a fit-for-purpose *champion model* by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.\n",
+    "\n",
+    "A *binary classification model* is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.\n",
+    "\n",
+    "- This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.\n",
+    "- Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8f85783",
+   "metadata": {},
+   "source": [
+    "<a id='toc2_'></a>\n",
+    "\n",
+    "## About ValidMind\n",
+    "\n",
+    "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n",
+    "\n",
+    "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87fbc9d8",
+   "metadata": {},
+   "source": [
+    "<a id='toc2_1_'></a>\n",
+    "\n",
+    "### Before you begin\n",
+    "\n",
+    "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n",
+    "\n",
+    "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f368277",
+   "metadata": {},
+   "source": [
+    "<a id='toc2_2_'></a>\n",
+    "\n",
+    "### New to ValidMind?\n",
+    "\n",
+    "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, you'll need access to a ValidMind account.</b></span>\n",
+    "<br></br>\n",
+    "<a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec08fd19",
+   "metadata": {},
+   "source": [
+    "<a id='toc2_3_'></a>\n",
+    "\n",
+    "### Key concepts\n",
+    "\n",
+    "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
+    "\n",
+    "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
+    "\n",
+    "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
+    "\n",
+    "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n",
+    "\n",
+    "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n",
+    "\n",
+    "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
+    "\n",
+    "  - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
+    "  - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
+    "  - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n",
+    "  - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html))\n",
+    "\n",
+    "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n",
+    "\n",
+    "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
+    "\n",
+    "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
+    "\n",
+    "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8883927",
+   "metadata": {},
+   "source": [
+    "<a id='toc3_'></a>\n",
+    "\n",
+    "## Setting up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57bdd8f8",
+   "metadata": {},
+   "source": [
+    "<a id='toc3_1_'></a>\n",
+    "\n",
+    "### Install the ValidMind Library\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>Recommended Python versions</b></span>\n",
+    "<br></br>\n",
+    "Python 3.8 <= x <= 3.11</div>\n",
+    "\n",
+    "To install the library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1f6dbed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q validmind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "797eb7f0",
+   "metadata": {},
+   "source": [
+    "<a id='toc3_2_'></a>\n",
+    "\n",
+    "### Initialize the ValidMind Library\n",
+    "\n",
+    "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc5c8bd4",
+   "metadata": {},
+   "source": [
+    "<a id='toc3_2_1_'></a>\n",
+    "\n",
+    "#### Get your code snippet\n",
+    "\n",
+    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
+    "\n",
+    "2. In the left sidebar, navigate to **Inventory** and click **+ Register Model**.\n",
+    "\n",
+    "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
+    "\n",
+    "   For example, to register a model for use with this notebook, select:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74d329e8",
+   "metadata": {},
+   "source": [
+    "   - Documentation template: `Binary classification`\n",
+    "   - Use case: `Marketing/Sales - Attrition/Churn Management`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "install-credentials-4c404843-3474-4618-8898-f3bcce33fadd",
+   "metadata": {},
+   "source": [
+    "   You can fill in other options according to your preference.\n",
+    "   \n",
+    "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n",
+    "\n",
+    "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2c1dd22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load your model identifier credentials from an `.env` file\n",
+    "\n",
+    "%load_ext dotenv\n",
+    "%dotenv .env\n",
+    "\n",
+    "# Or replace with your code snippet\n",
+    "\n",
+    "import validmind as vm\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
+    "import validmind as vm\n",
+    "\n",
+    "vm.init(\n",
+    "  api_host = \"https://api.dev.vm.validmind.ai/api/v1/tracking\",\n",
+    "  api_key = \"None\",\n",
+    "  api_secret = \"None\",\n",
+    "  model = \"cltnl28x600001omg9wu8wfty\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "import logging\n",
+    "import sys\n",
+    "import site\n",
+    "\n",
+    "logger = logging.getLogger()\n",
+    "\n",
+    "# Print the path to the current Python interpreter\n",
+    "logger.info(\"Python executable path: \" + sys.executable)\n",
+    "\n",
+    "# Print the path to the site-packages directory\n",
+    "logger.info(\"Site-packages path: \" + str(site.getsitepackages()))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44bf926b",
+   "metadata": {},
+   "source": [
+    "<a id='toc3_3_'></a>\n",
+    "\n",
+    "### Initialize the Python environment\n",
+    "\n",
+    "Then, let's import the necessary libraries and set up your Python environment for data analysis:\n",
+    "\n",
+    "- Import **Extreme Gradient Boosting** (XGBoost) with an alias so that we can reference its functions in later calls. XGBoost is a powerful machine learning library designed for speed and performance, especially in handling structured or tabular data.\n",
+    "- Enable **`matplotlib`**, a plotting library used for visualizing data. Ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62d7c2c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41474c53",
+   "metadata": {},
+   "source": [
+    "<a id='toc4_'></a>\n",
+    "\n",
+    "## Getting to know ValidMind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d87ae6f",
+   "metadata": {},
+   "source": [
+    "<a id='toc4_1_'></a>\n",
+    "\n",
+    "### Preview the documentation template\n",
+    "\n",
+    "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
+    "\n",
+    "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2bce375",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vm.preview_template()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa3b815d",
+   "metadata": {},
+   "source": [
+    "<a id='toc4_2_'></a>\n",
+    "\n",
+    "### View model documentation in the ValidMind Platform\n",
+    "\n",
+    "Next, let's head to the ValidMind Platform to see the template in action:\n",
+    "\n",
+    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
+    "\n",
+    "2. In the left sidebar, navigate to **Inventory** and select the model you registered for this notebook.\n",
+    "\n",
+    "3. Click **Documentation** under Documents for your model and note how the structure of the documentation matches our preview above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde500ce",
+   "metadata": {},
+   "source": [
+    "<a id='toc5_'></a>\n",
+    "\n",
+    "## Import the sample dataset\n",
+    "\n",
+    "First, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n",
+    "\n",
+    "In our below example, note that: \n",
+    "\n",
+    "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
+    "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58d1c94b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from validmind.datasets.classification import customer_churn\n",
+    "\n",
+    "print(\n",
+    "    f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n",
+    ")\n",
+    "\n",
+    "raw_df = customer_churn.load_data()\n",
+    "raw_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52d0aaa8",
+   "metadata": {},
+   "source": [
+    "<a id='toc6_'></a>\n",
+    "\n",
+    "## Preprocessing the raw dataset\n",
+    "\n",
+    "Before running tests with Validmind, we'll need to preprocess our imported dataset. This involves splitting the data and separating the features (inputs) from the targets (outputs)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83545763",
+   "metadata": {},
+   "source": [
+    "<a id='toc6_1_'></a>\n",
+    "\n",
+    "### Split the dataset\n",
+    "\n",
+    "Splitting our dataset helps assess how well the model generalizes to unseen data.\n",
+    "\n",
+    "Use [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess) to split our dataset into three subsets:\n",
+    "\n",
+    "1. **train_df** — Used to train the model.\n",
+    "2. **validation_df** — Used to evaluate the model's performance during training.\n",
+    "3. **test_df** — Used later on to asses the model's performance on new, unseen data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "418cb5aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ae3dc5",
+   "metadata": {},
+   "source": [
+    "<a id='toc6_2_'></a>\n",
+    "\n",
+    "### Separate features and targets\n",
+    "\n",
+    "To train the model, we need to provide it with:\n",
+    "\n",
+    "1. **Inputs** — Features such as customer age, usage, etc.\n",
+    "2. **Outputs (Expected answers/labels)** — in our case, we would like to know whether the customer churned or not.\n",
+    "\n",
+    "Here, we'll use `x_train` and `x_val` to hold the input data (features), and `y_train` and `y_val` to hold the answers (the target we want to predict):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fd365fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = train_df.drop(customer_churn.target_column, axis=1)\n",
+    "y_train = train_df[customer_churn.target_column]\n",
+    "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n",
+    "y_val = validation_df[customer_churn.target_column]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "541466ce",
+   "metadata": {},
+   "source": [
+    "<a id='toc7_'></a>\n",
+    "\n",
+    "## Training an XGBoost classifier model\n",
+    "\n",
+    "Next, let's create an XGBoost classifier model that will automatically stop training if it doesn’t improve after 10 tries.\n",
+    "\n",
+    "Setting a threshold avoids wasting time and helps prevent overfitting by stopping training when further improvement isn’t happening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3296cac6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = xgb.XGBClassifier(early_stopping_rounds=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "826d8adf",
+   "metadata": {},
+   "source": [
+    "<a id='toc7_1_'></a>\n",
+    "\n",
+    "### Set evaluation metrics\n",
+    "\n",
+    "Then, we'll set the evaluation metrics, which tells the model to use three different ways to measure its performance:\n",
+    "\n",
+    "1. **error** — Measures how often the model makes incorrect predictions.\n",
+    "2. **logloss** — Indicates how confident the predictions are.\n",
+    "3. **auc** — Evaluates how well the model distinguishes between churn and not churn.\n",
+    "\n",
+    "Using multiple metrics gives a more complete picture of how good (or bad) the model is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32d3c3f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.set_params(\n",
+    "    eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78c85786",
+   "metadata": {},
+   "source": [
+    "<a id='toc7_2_'></a>\n",
+    "\n",
+    "### Fit the model\n",
+    "\n",
+    "Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:\n",
+    "\n",
+    "- The model is trained on `x_train` and `y_train`, and evaluates its performance using `x_val` and `y_val` to check if it’s learning well.\n",
+    "- To turn off printed output while training, we'll set `verbose` to `False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb95ce4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(\n",
+    "    x_train,\n",
+    "    y_train,\n",
+    "    eval_set=[(x_val, y_val)],\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6662e6b",
+   "metadata": {},
+   "source": [
+    "<a id='toc8_'></a>\n",
+    "\n",
+    "## Initialize the ValidMind datasets\n",
+    "\n",
+    "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n",
+    "\n",
+    "For this example, we'll pass in the following arguments:\n",
+    "\n",
+    "- **`dataset`** — The raw dataset that you want to provide as input to tests.\n",
+    "- **`input_id`** — A unique identifier that allows tracking what inputs are used when running each individual test.\n",
+    "- **`target_column`** — A required argument if tests require access to true values. This is the name of the target column in the dataset.\n",
+    "- **`class_labels`** — An optional value to map predicted classes to class labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb6ad06a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the raw dataset\n",
+    "vm_raw_dataset = vm.init_dataset(\n",
+    "    dataset=raw_df,\n",
+    "    input_id=\"raw_dataset\",\n",
+    "    target_column=customer_churn.target_column,\n",
+    "    class_labels=customer_churn.class_labels,\n",
+    ")\n",
+    "\n",
+    "# Initialize the training dataset\n",
+    "vm_train_ds = vm.init_dataset(\n",
+    "    dataset=train_df,\n",
+    "    input_id=\"train_dataset\",\n",
+    "    target_column=customer_churn.target_column,\n",
+    ")\n",
+    "\n",
+    "# Initialize the testing dataset\n",
+    "vm_test_ds = vm.init_dataset(\n",
+    "    dataset=test_df,\n",
+    "    input_id=\"test_dataset\",\n",
+    "    target_column=customer_churn.target_column\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5da79af3",
+   "metadata": {},
+   "source": [
+    "<a id='toc9_'></a>\n",
+    "\n",
+    "## Initialize a model object\n",
+    "\n",
+    "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n",
+    "\n",
+    "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e44eebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vm_model = vm.init_model(\n",
+    "    model,\n",
+    "    input_id=\"model\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fa481bf",
+   "metadata": {},
+   "source": [
+    "<a id='toc9_1_'></a>\n",
+    "\n",
+    "### Assign predictions\n",
+    "\n",
+    "Once the model has been registered, you can assign model predictions to the training and testing datasets.\n",
+    "\n",
+    "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
+    "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
+    "\n",
+    "If no prediction values are passed, the method will compute predictions automatically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62bd94fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vm_train_ds.assign_predictions(\n",
+    "    model=vm_model,\n",
+    ")\n",
+    "\n",
+    "vm_test_ds.assign_predictions(\n",
+    "    model=vm_model,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ade4b3",
+   "metadata": {},
+   "source": [
+    "<a id='toc10_'></a>\n",
+    "\n",
+    "## Run the full suite of tests\n",
+    "\n",
+    "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n",
+    "\n",
+    "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform:\n",
+    "\n",
+    "- The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. \n",
+    "- It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n",
+    "\n",
+    "    ```python\n",
+    "    config = {\n",
+    "        \"<test-id>\": {\n",
+    "            \"params\": {\n",
+    "                \"param1\": \"value1\",\n",
+    "                \"param2\": \"value2\",\n",
+    "                ...\n",
+    "            },\n",
+    "            \"inputs\": {\n",
+    "                \"input1\": \"value1\",\n",
+    "                \"input2\": \"value2\",\n",
+    "                ...\n",
+    "            }\n",
+    "        },\n",
+    "        ...\n",
+    "    }\n",
+    "    ```\n",
+    "\n",
+    "    Each `<test-id>` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3d6741b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from validmind.utils import preview_test_config\n",
+    "\n",
+    "test_config = customer_churn.get_demo_test_config()\n",
+    "preview_test_config(test_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70830a61",
+   "metadata": {},
+   "source": [
+    "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests.\n",
+    "\n",
+    "The variable `full_suite` then holds the result of these tests:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae3accf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_suite = vm.run_documentation_tests(config=test_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79f1b475",
+   "metadata": {},
+   "source": [
+    "<a id='toc11_'></a>\n",
+    "\n",
+    "## In summary\n",
+    "\n",
+    "In this notebook, you learned how to:\n",
+    "\n",
+    "- [x] Register a model within the ValidMind Platform\n",
+    "- [x] Install and initialize the ValidMind Library\n",
+    "- [x] Preview the documentation template for your model\n",
+    "- [x] Import a sample dataset\n",
+    "- [x] Initialize ValidMind datasets and model objects\n",
+    "- [x] Assign model predictions to your ValidMind model objects\n",
+    "- [x] Run a full suite of documentation tests\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adcc9956",
+   "metadata": {},
+   "source": [
+    "<a id='toc12_'></a>\n",
+    "\n",
+    "## Next steps\n",
+    "\n",
+    "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b8e72f6",
+   "metadata": {},
+   "source": [
+    "<a id='toc12_1_'></a>\n",
+    "\n",
+    "### Work with your model documentation\n",
+    "\n",
+    "1. From the **Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n",
+    "\n",
+    "2. In the left sidebar that appears for your model, click **Documentation**.\n",
+    "\n",
+    "    What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/working-with-model-documentation.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "585b79fd",
+   "metadata": {},
+   "source": [
+    "<a id='toc12_2_'></a>\n",
+    "\n",
+    "### Discover more learning resources\n",
+    "\n",
+    "For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:\n",
+    "\n",
+    "- **[ValidMind for model development](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)**\n",
+    "- **[Developer Fundamentals](https://docs.validmind.ai/training/developer-fundamentals/developer-fundamentals-register.html)**\n",
+    "\n",
+    "We also offer many interactive notebooks to help you document models:\n",
+    "\n",
+    "- [Run tests & test suites](https://docs.validmind.ai/guide/testing-overview.html)\n",
+    "- [Code samples](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n",
+    "\n",
+    "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790f47cb",
+   "metadata": {},
+   "source": [
+    "<a id='toc13_'></a>\n",
+    "\n",
+    "## Upgrade ValidMind\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\">After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.</div>\n",
+    "\n",
+    "Retrieve the information for the currently installed version of ValidMind:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35955b6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip show validmind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upgrade-version-ab5a531d-4334-4c5b-b6aa-754e336f127f",
+   "metadata": {},
+   "source": [
+    "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n",
+    "\n",
+    "```bash\n",
+    "%pip install --upgrade validmind\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upgrade-restart-faf9c324-3332-4eaf-aef5-2c0ca650d5ca",
+   "metadata": {},
+   "source": [
+    "You may need to restart your kernel after running the upgrade package for changes to be applied."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ValidMind Library",
+   "language": "python",
+   "name": "validmind"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/quickstart/quickstart_model_documentation.out.ipynb b/notebooks/quickstart/quickstart_model_documentation.out.ipynb
new file mode 100644
index 000000000..d05bbdfea
--- /dev/null
+++ b/notebooks/quickstart/quickstart_model_documentation.out.ipynb
@@ -0,0 +1,1367 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f2c17b2d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007034,
+     "end_time": "2025-07-30T17:51:38.340583",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.333549",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Quickstart for model documentation\n",
+    "\n",
+    "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.\n",
+    "\n",
+    "To document a model with the ValidMind Library, we'll:\n",
+    "\n",
+    "1. Import a sample dataset and preprocess it\n",
+    "2. Split the datasets and initialize them for use with ValidMind\n",
+    "3. Initialize a model object for use with testing\n",
+    "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05dce32a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.061162,
+     "end_time": "2025-07-30T17:51:38.408885",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.347723",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "::: {.content-hidden when-format=\"html\"}\n",
+    "## Contents    \n",
+    "- [Introduction](#toc1_)    \n",
+    "- [About ValidMind](#toc2_)    \n",
+    "  - [Before you begin](#toc2_1_)    \n",
+    "  - [New to ValidMind?](#toc2_2_)    \n",
+    "  - [Key concepts](#toc2_3_)    \n",
+    "- [Setting up](#toc3_)    \n",
+    "  - [Install the ValidMind Library](#toc3_1_)    \n",
+    "  - [Initialize the ValidMind Library](#toc3_2_)    \n",
+    "    - [Get your code snippet](#toc3_2_1_)    \n",
+    "  - [Initialize the Python environment](#toc3_3_)    \n",
+    "- [Getting to know ValidMind](#toc4_)    \n",
+    "  - [Preview the documentation template](#toc4_1_)    \n",
+    "  - [View model documentation in the ValidMind Platform](#toc4_2_)    \n",
+    "- [Import the sample dataset](#toc5_)    \n",
+    "- [Preprocessing the raw dataset](#toc6_)    \n",
+    "  - [Split the dataset](#toc6_1_)    \n",
+    "  - [Separate features and targets](#toc6_2_)    \n",
+    "- [Training an XGBoost classifier model](#toc7_)    \n",
+    "  - [Set evaluation metrics](#toc7_1_)    \n",
+    "  - [Fit the model](#toc7_2_)    \n",
+    "- [Initialize the ValidMind datasets](#toc8_)    \n",
+    "- [Initialize a model object](#toc9_)    \n",
+    "  - [Assign predictions](#toc9_1_)    \n",
+    "- [Run the full suite of tests](#toc10_)    \n",
+    "- [In summary](#toc11_)    \n",
+    "- [Next steps](#toc12_)    \n",
+    "  - [Work with your model documentation](#toc12_1_)    \n",
+    "  - [Discover more learning resources](#toc12_2_)    \n",
+    "- [Upgrade ValidMind](#toc13_)    \n",
+    "\n",
+    ":::\n",
+    "<!-- jn-toc-notebook-config\n",
+    "\tnumbering=false\n",
+    "\tanchor=true\n",
+    "\tflat=false\n",
+    "\tminLevel=2\n",
+    "\tmaxLevel=4\n",
+    "\t/jn-toc-notebook-config -->\n",
+    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1919918",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004919,
+     "end_time": "2025-07-30T17:51:38.418578",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.413659",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc1_'></a>\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "Model development aims to produce a fit-for-purpose *champion model* by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.\n",
+    "\n",
+    "A *binary classification model* is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.\n",
+    "\n",
+    "- This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.\n",
+    "- Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8f85783",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004634,
+     "end_time": "2025-07-30T17:51:38.428222",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.423588",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc2_'></a>\n",
+    "\n",
+    "## About ValidMind\n",
+    "\n",
+    "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n",
+    "\n",
+    "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87fbc9d8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005599,
+     "end_time": "2025-07-30T17:51:38.438933",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.433334",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc2_1_'></a>\n",
+    "\n",
+    "### Before you begin\n",
+    "\n",
+    "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n",
+    "\n",
+    "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f368277",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004848,
+     "end_time": "2025-07-30T17:51:38.449279",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.444431",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc2_2_'></a>\n",
+    "\n",
+    "### New to ValidMind?\n",
+    "\n",
+    "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, you'll need access to a ValidMind account.</b></span>\n",
+    "<br></br>\n",
+    "<a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec08fd19",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005091,
+     "end_time": "2025-07-30T17:51:38.459919",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.454828",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc2_3_'></a>\n",
+    "\n",
+    "### Key concepts\n",
+    "\n",
+    "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
+    "\n",
+    "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
+    "\n",
+    "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
+    "\n",
+    "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n",
+    "\n",
+    "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n",
+    "\n",
+    "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
+    "\n",
+    "  - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
+    "  - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
+    "  - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n",
+    "  - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html))\n",
+    "\n",
+    "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n",
+    "\n",
+    "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
+    "\n",
+    "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
+    "\n",
+    "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8883927",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007581,
+     "end_time": "2025-07-30T17:51:38.472820",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.465239",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc3_'></a>\n",
+    "\n",
+    "## Setting up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57bdd8f8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005038,
+     "end_time": "2025-07-30T17:51:38.483109",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.478071",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc3_1_'></a>\n",
+    "\n",
+    "### Install the ValidMind Library\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>Recommended Python versions</b></span>\n",
+    "<br></br>\n",
+    "Python 3.8 <= x <= 3.11</div>\n",
+    "\n",
+    "To install the library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d1f6dbed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-30T17:51:38.494647Z",
+     "iopub.status.busy": "2025-07-30T17:51:38.494395Z",
+     "iopub.status.idle": "2025-07-30T17:51:40.257184Z",
+     "shell.execute_reply": "2025-07-30T17:51:40.256656Z"
+    },
+    "papermill": {
+     "duration": 1.76978,
+     "end_time": "2025-07-30T17:51:40.258435",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:38.488655",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -q validmind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "797eb7f0",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004717,
+     "end_time": "2025-07-30T17:51:40.268705",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:40.263988",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc3_2_'></a>\n",
+    "\n",
+    "### Initialize the ValidMind Library\n",
+    "\n",
+    "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc5c8bd4",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004651,
+     "end_time": "2025-07-30T17:51:40.278141",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:40.273490",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc3_2_1_'></a>\n",
+    "\n",
+    "#### Get your code snippet\n",
+    "\n",
+    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
+    "\n",
+    "2. In the left sidebar, navigate to **Inventory** and click **+ Register Model**.\n",
+    "\n",
+    "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
+    "\n",
+    "   For example, to register a model for use with this notebook, select:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74d329e8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004702,
+     "end_time": "2025-07-30T17:51:40.287362",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:40.282660",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "   - Documentation template: `Binary classification`\n",
+    "   - Use case: `Marketing/Sales - Attrition/Churn Management`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "install-credentials-4c404843-3474-4618-8898-f3bcce33fadd",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004587,
+     "end_time": "2025-07-30T17:51:40.296531",
+     "exception": false,
+     "start_time": "2025-07-30T17:51:40.291944",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "   You can fill in other options according to your preference.\n",
+    "   \n",
+    "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n",
+    "\n",
+    "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2c1dd22",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": false,
+     "start_time": "2025-07-30T17:51:40.301021",
+     "status": "running"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Load your model identifier credentials from an `.env` file\n",
+    "\n",
+    "%load_ext dotenv\n",
+    "%dotenv .env\n",
+    "\n",
+    "# Or replace with your code snippet\n",
+    "\n",
+    "import validmind as vm\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
+    "import validmind as vm\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
+    "import validmind as vm\n",
+    "\n",
+    "vm.init(\n",
+    "  api_host = \"https://api.dev.vm.validmind.ai/api/v1/tracking\",\n",
+    "  api_key = \"dea7a1c03b4bff0e4b969c9719d89492\",\n",
+    "  api_secret = \"65c3deb6f1c579a5e8a2aa3989c04068e77c42d5ab5e7c89ffd17aff25b291cc\",\n",
+    "  model = \"cmafl60cm000u1fcseem2u3j6\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "import logging\n",
+    "import sys\n",
+    "import site\n",
+    "\n",
+    "logger = logging.getLogger()\n",
+    "\n",
+    "# Print the path to the current Python interpreter\n",
+    "logger.info(\"Python executable path: \" + sys.executable)\n",
+    "\n",
+    "# Print the path to the site-packages directory\n",
+    "logger.info(\"Site-packages path: \" + str(site.getsitepackages()))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44bf926b",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc3_3_'></a>\n",
+    "\n",
+    "### Initialize the Python environment\n",
+    "\n",
+    "Then, let's import the necessary libraries and set up your Python environment for data analysis:\n",
+    "\n",
+    "- Import **Extreme Gradient Boosting** (XGBoost) with an alias so that we can reference its functions in later calls. XGBoost is a powerful machine learning library designed for speed and performance, especially in handling structured or tabular data.\n",
+    "- Enable **`matplotlib`**, a plotting library used for visualizing data. Ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62d7c2c1",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41474c53",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc4_'></a>\n",
+    "\n",
+    "## Getting to know ValidMind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d87ae6f",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc4_1_'></a>\n",
+    "\n",
+    "### Preview the documentation template\n",
+    "\n",
+    "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
+    "\n",
+    "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2bce375",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vm.preview_template()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa3b815d",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc4_2_'></a>\n",
+    "\n",
+    "### View model documentation in the ValidMind Platform\n",
+    "\n",
+    "Next, let's head to the ValidMind Platform to see the template in action:\n",
+    "\n",
+    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
+    "\n",
+    "2. In the left sidebar, navigate to **Inventory** and select the model you registered for this notebook.\n",
+    "\n",
+    "3. Click **Documentation** under Documents for your model and note how the structure of the documentation matches our preview above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde500ce",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc5_'></a>\n",
+    "\n",
+    "## Import the sample dataset\n",
+    "\n",
+    "First, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n",
+    "\n",
+    "In our below example, note that: \n",
+    "\n",
+    "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
+    "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58d1c94b",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from validmind.datasets.classification import customer_churn\n",
+    "\n",
+    "print(\n",
+    "    f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n",
+    ")\n",
+    "\n",
+    "raw_df = customer_churn.load_data()\n",
+    "raw_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52d0aaa8",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc6_'></a>\n",
+    "\n",
+    "## Preprocessing the raw dataset\n",
+    "\n",
+    "Before running tests with Validmind, we'll need to preprocess our imported dataset. This involves splitting the data and separating the features (inputs) from the targets (outputs)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83545763",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc6_1_'></a>\n",
+    "\n",
+    "### Split the dataset\n",
+    "\n",
+    "Splitting our dataset helps assess how well the model generalizes to unseen data.\n",
+    "\n",
+    "Use [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess) to split our dataset into three subsets:\n",
+    "\n",
+    "1. **train_df** — Used to train the model.\n",
+    "2. **validation_df** — Used to evaluate the model's performance during training.\n",
+    "3. **test_df** — Used later on to asses the model's performance on new, unseen data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "418cb5aa",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ae3dc5",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc6_2_'></a>\n",
+    "\n",
+    "### Separate features and targets\n",
+    "\n",
+    "To train the model, we need to provide it with:\n",
+    "\n",
+    "1. **Inputs** — Features such as customer age, usage, etc.\n",
+    "2. **Outputs (Expected answers/labels)** — in our case, we would like to know whether the customer churned or not.\n",
+    "\n",
+    "Here, we'll use `x_train` and `x_val` to hold the input data (features), and `y_train` and `y_val` to hold the answers (the target we want to predict):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fd365fd",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x_train = train_df.drop(customer_churn.target_column, axis=1)\n",
+    "y_train = train_df[customer_churn.target_column]\n",
+    "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n",
+    "y_val = validation_df[customer_churn.target_column]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "541466ce",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc7_'></a>\n",
+    "\n",
+    "## Training an XGBoost classifier model\n",
+    "\n",
+    "Next, let's create an XGBoost classifier model that will automatically stop training if it doesn’t improve after 10 tries.\n",
+    "\n",
+    "Setting a threshold avoids wasting time and helps prevent overfitting by stopping training when further improvement isn’t happening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3296cac6",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model = xgb.XGBClassifier(early_stopping_rounds=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "826d8adf",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc7_1_'></a>\n",
+    "\n",
+    "### Set evaluation metrics\n",
+    "\n",
+    "Then, we'll set the evaluation metrics, which tells the model to use three different ways to measure its performance:\n",
+    "\n",
+    "1. **error** — Measures how often the model makes incorrect predictions.\n",
+    "2. **logloss** — Indicates how confident the predictions are.\n",
+    "3. **auc** — Evaluates how well the model distinguishes between churn and not churn.\n",
+    "\n",
+    "Using multiple metrics gives a more complete picture of how good (or bad) the model is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32d3c3f4",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model.set_params(\n",
+    "    eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78c85786",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc7_2_'></a>\n",
+    "\n",
+    "### Fit the model\n",
+    "\n",
+    "Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:\n",
+    "\n",
+    "- The model is trained on `x_train` and `y_train`, and evaluates its performance using `x_val` and `y_val` to check if it’s learning well.\n",
+    "- To turn off printed output while training, we'll set `verbose` to `False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb95ce4",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model.fit(\n",
+    "    x_train,\n",
+    "    y_train,\n",
+    "    eval_set=[(x_val, y_val)],\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6662e6b",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc8_'></a>\n",
+    "\n",
+    "## Initialize the ValidMind datasets\n",
+    "\n",
+    "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n",
+    "\n",
+    "For this example, we'll pass in the following arguments:\n",
+    "\n",
+    "- **`dataset`** — The raw dataset that you want to provide as input to tests.\n",
+    "- **`input_id`** — A unique identifier that allows tracking what inputs are used when running each individual test.\n",
+    "- **`target_column`** — A required argument if tests require access to true values. This is the name of the target column in the dataset.\n",
+    "- **`class_labels`** — An optional value to map predicted classes to class labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb6ad06a",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize the raw dataset\n",
+    "vm_raw_dataset = vm.init_dataset(\n",
+    "    dataset=raw_df,\n",
+    "    input_id=\"raw_dataset\",\n",
+    "    target_column=customer_churn.target_column,\n",
+    "    class_labels=customer_churn.class_labels,\n",
+    ")\n",
+    "\n",
+    "# Initialize the training dataset\n",
+    "vm_train_ds = vm.init_dataset(\n",
+    "    dataset=train_df,\n",
+    "    input_id=\"train_dataset\",\n",
+    "    target_column=customer_churn.target_column,\n",
+    ")\n",
+    "\n",
+    "# Initialize the testing dataset\n",
+    "vm_test_ds = vm.init_dataset(\n",
+    "    dataset=test_df,\n",
+    "    input_id=\"test_dataset\",\n",
+    "    target_column=customer_churn.target_column\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5da79af3",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc9_'></a>\n",
+    "\n",
+    "## Initialize a model object\n",
+    "\n",
+    "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n",
+    "\n",
+    "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e44eebd",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vm_model = vm.init_model(\n",
+    "    model,\n",
+    "    input_id=\"model\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fa481bf",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc9_1_'></a>\n",
+    "\n",
+    "### Assign predictions\n",
+    "\n",
+    "Once the model has been registered, you can assign model predictions to the training and testing datasets.\n",
+    "\n",
+    "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
+    "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
+    "\n",
+    "If no prediction values are passed, the method will compute predictions automatically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62bd94fc",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vm_train_ds.assign_predictions(\n",
+    "    model=vm_model,\n",
+    ")\n",
+    "\n",
+    "vm_test_ds.assign_predictions(\n",
+    "    model=vm_model,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ade4b3",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc10_'></a>\n",
+    "\n",
+    "## Run the full suite of tests\n",
+    "\n",
+    "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n",
+    "\n",
+    "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform:\n",
+    "\n",
+    "- The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. \n",
+    "- It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n",
+    "\n",
+    "    ```python\n",
+    "    config = {\n",
+    "        \"<test-id>\": {\n",
+    "            \"params\": {\n",
+    "                \"param1\": \"value1\",\n",
+    "                \"param2\": \"value2\",\n",
+    "                ...\n",
+    "            },\n",
+    "            \"inputs\": {\n",
+    "                \"input1\": \"value1\",\n",
+    "                \"input2\": \"value2\",\n",
+    "                ...\n",
+    "            }\n",
+    "        },\n",
+    "        ...\n",
+    "    }\n",
+    "    ```\n",
+    "\n",
+    "    Each `<test-id>` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3d6741b",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from validmind.utils import preview_test_config\n",
+    "\n",
+    "test_config = customer_churn.get_demo_test_config()\n",
+    "preview_test_config(test_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70830a61",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests.\n",
+    "\n",
+    "The variable `full_suite` then holds the result of these tests:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae3accf7",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "full_suite = vm.run_documentation_tests(config=test_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79f1b475",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc11_'></a>\n",
+    "\n",
+    "## In summary\n",
+    "\n",
+    "In this notebook, you learned how to:\n",
+    "\n",
+    "- [x] Register a model within the ValidMind Platform\n",
+    "- [x] Install and initialize the ValidMind Library\n",
+    "- [x] Preview the documentation template for your model\n",
+    "- [x] Import a sample dataset\n",
+    "- [x] Initialize ValidMind datasets and model objects\n",
+    "- [x] Assign model predictions to your ValidMind model objects\n",
+    "- [x] Run a full suite of documentation tests\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adcc9956",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc12_'></a>\n",
+    "\n",
+    "## Next steps\n",
+    "\n",
+    "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b8e72f6",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc12_1_'></a>\n",
+    "\n",
+    "### Work with your model documentation\n",
+    "\n",
+    "1. From the **Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n",
+    "\n",
+    "2. In the left sidebar that appears for your model, click **Documentation**.\n",
+    "\n",
+    "    What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/working-with-model-documentation.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "585b79fd",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc12_2_'></a>\n",
+    "\n",
+    "### Discover more learning resources\n",
+    "\n",
+    "For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:\n",
+    "\n",
+    "- **[ValidMind for model development](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)**\n",
+    "- **[Developer Fundamentals](https://docs.validmind.ai/training/developer-fundamentals/developer-fundamentals-register.html)**\n",
+    "\n",
+    "We also offer many interactive notebooks to help you document models:\n",
+    "\n",
+    "- [Run tests & test suites](https://docs.validmind.ai/guide/testing-overview.html)\n",
+    "- [Code samples](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n",
+    "\n",
+    "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790f47cb",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='toc13_'></a>\n",
+    "\n",
+    "## Upgrade ValidMind\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\">After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.</div>\n",
+    "\n",
+    "Retrieve the information for the currently installed version of ValidMind:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35955b6b",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%pip show validmind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upgrade-version-ab5a531d-4334-4c5b-b6aa-754e336f127f",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n",
+    "\n",
+    "```bash\n",
+    "%pip install --upgrade validmind\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upgrade-restart-faf9c324-3332-4eaf-aef5-2c0ca650d5ca",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "You may need to restart your kernel after running the upgrade package for changes to be applied."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ValidMind Library",
+   "language": "python",
+   "name": "validmind"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": null,
+   "end_time": null,
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "/Users/alexiamharger/Code/validmind-library/notebooks/quickstart/quickstart_model_documentation.ipynb",
+   "output_path": "/Users/alexiamharger/Code/validmind-library/notebooks/quickstart/quickstart_model_documentation.out.ipynb",
+   "parameters": {},
+   "start_time": "2025-07-30T17:51:37.183297",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file

From f6cf46857eb5bf9a7eb7e8a38b7e943a1974012b Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 14:19:26 -0400
Subject: [PATCH 04/47] refactor: replace pkg_resources with importlib.metadata
 for version checking

---
 validmind/__init__.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/validmind/__init__.py b/validmind/__init__.py
index 216c26d20..7bc2cd953 100644
--- a/validmind/__init__.py
+++ b/validmind/__init__.py
@@ -32,8 +32,8 @@
 """
 import threading
 import warnings
+from importlib import metadata
 
-import pkg_resources
 from IPython.display import HTML, display
 
 # Ignore Numba warnings. We are not requiring this package directly
@@ -81,7 +81,12 @@ def check_version():
     # get the installed vs running version of validmind
     # to make sure we are using the latest installed version
     # in case user has updated the package but forgot to restart the kernel
-    installed = pkg_resources.get_distribution("validmind").version
+    try:
+        installed = metadata.version("validmind")
+    except metadata.PackageNotFoundError:
+        # Package metadata not found, skip version check
+        return
+    
     running = __version__
 
     if parse_version(installed) > parse_version(running):

From cd62556323f58b086b07966c68a0f2fc4da3bd15 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 14:47:44 -0400
Subject: [PATCH 05/47] fix: update tox command in dependency testing workflow
 to streamline dependency type handling

---
 .github/workflows/dependency-testing.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index dd31f4618..21fd24d15 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -74,7 +74,7 @@ jobs:
 
       - name: Run dependency tests
         run: |
-          tox -e py$(echo ${{ matrix.python-version }} | tr -d .)-deps-${{ matrix.deps-type }}
+          tox -e py$(echo ${{ matrix.python-version }} | tr -d .)-${{ matrix.deps-type }}
         env:
           # Limit OpenMP threads to prevent CI resource issues
           OMP_NUM_THREADS: 1

From 22eeffe6ea313f9baefc4c432b2e6211e24193a4 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 14:49:04 -0400
Subject: [PATCH 06/47] chore: simplify dependency installation in CI workflow
 by using all-extras option

---
 .github/workflows/python.yaml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 2476f0f62..3623e74cb 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -56,8 +56,7 @@ jobs:
       - name: Install Dependencies
         run: |
           poetry env use python3.9
-          poetry run pip install pyarrow==17.0.0
-          poetry install -E huggingface -E llm
+          poetry install --all-extras
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
 

From e453cb59fabd477806dc219c55424c2dc8081f33 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 14:50:14 -0400
Subject: [PATCH 07/47] chore: remove outdated backup and output notebooks for
 quickstart model documentation

---
 ...uickstart_model_documentation.ipynb.backup |  867 -----------
 .../quickstart_model_documentation.out.ipynb  | 1367 -----------------
 2 files changed, 2234 deletions(-)
 delete mode 100644 notebooks/quickstart/quickstart_model_documentation.ipynb.backup
 delete mode 100644 notebooks/quickstart/quickstart_model_documentation.out.ipynb

diff --git a/notebooks/quickstart/quickstart_model_documentation.ipynb.backup b/notebooks/quickstart/quickstart_model_documentation.ipynb.backup
deleted file mode 100644
index cc4dca5eb..000000000
--- a/notebooks/quickstart/quickstart_model_documentation.ipynb.backup
+++ /dev/null
@@ -1,867 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f2c17b2d",
-   "metadata": {},
-   "source": [
-    "# Quickstart for model documentation\n",
-    "\n",
-    "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.\n",
-    "\n",
-    "To document a model with the ValidMind Library, we'll:\n",
-    "\n",
-    "1. Import a sample dataset and preprocess it\n",
-    "2. Split the datasets and initialize them for use with ValidMind\n",
-    "3. Initialize a model object for use with testing\n",
-    "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05dce32a",
-   "metadata": {},
-   "source": [
-    "::: {.content-hidden when-format=\"html\"}\n",
-    "## Contents    \n",
-    "- [Introduction](#toc1_)    \n",
-    "- [About ValidMind](#toc2_)    \n",
-    "  - [Before you begin](#toc2_1_)    \n",
-    "  - [New to ValidMind?](#toc2_2_)    \n",
-    "  - [Key concepts](#toc2_3_)    \n",
-    "- [Setting up](#toc3_)    \n",
-    "  - [Install the ValidMind Library](#toc3_1_)    \n",
-    "  - [Initialize the ValidMind Library](#toc3_2_)    \n",
-    "    - [Get your code snippet](#toc3_2_1_)    \n",
-    "  - [Initialize the Python environment](#toc3_3_)    \n",
-    "- [Getting to know ValidMind](#toc4_)    \n",
-    "  - [Preview the documentation template](#toc4_1_)    \n",
-    "  - [View model documentation in the ValidMind Platform](#toc4_2_)    \n",
-    "- [Import the sample dataset](#toc5_)    \n",
-    "- [Preprocessing the raw dataset](#toc6_)    \n",
-    "  - [Split the dataset](#toc6_1_)    \n",
-    "  - [Separate features and targets](#toc6_2_)    \n",
-    "- [Training an XGBoost classifier model](#toc7_)    \n",
-    "  - [Set evaluation metrics](#toc7_1_)    \n",
-    "  - [Fit the model](#toc7_2_)    \n",
-    "- [Initialize the ValidMind datasets](#toc8_)    \n",
-    "- [Initialize a model object](#toc9_)    \n",
-    "  - [Assign predictions](#toc9_1_)    \n",
-    "- [Run the full suite of tests](#toc10_)    \n",
-    "- [In summary](#toc11_)    \n",
-    "- [Next steps](#toc12_)    \n",
-    "  - [Work with your model documentation](#toc12_1_)    \n",
-    "  - [Discover more learning resources](#toc12_2_)    \n",
-    "- [Upgrade ValidMind](#toc13_)    \n",
-    "\n",
-    ":::\n",
-    "<!-- jn-toc-notebook-config\n",
-    "\tnumbering=false\n",
-    "\tanchor=true\n",
-    "\tflat=false\n",
-    "\tminLevel=2\n",
-    "\tmaxLevel=4\n",
-    "\t/jn-toc-notebook-config -->\n",
-    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1919918",
-   "metadata": {},
-   "source": [
-    "<a id='toc1_'></a>\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "Model development aims to produce a fit-for-purpose *champion model* by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.\n",
-    "\n",
-    "A *binary classification model* is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.\n",
-    "\n",
-    "- This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.\n",
-    "- Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8f85783",
-   "metadata": {},
-   "source": [
-    "<a id='toc2_'></a>\n",
-    "\n",
-    "## About ValidMind\n",
-    "\n",
-    "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n",
-    "\n",
-    "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87fbc9d8",
-   "metadata": {},
-   "source": [
-    "<a id='toc2_1_'></a>\n",
-    "\n",
-    "### Before you begin\n",
-    "\n",
-    "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n",
-    "\n",
-    "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f368277",
-   "metadata": {},
-   "source": [
-    "<a id='toc2_2_'></a>\n",
-    "\n",
-    "### New to ValidMind?\n",
-    "\n",
-    "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, you'll need access to a ValidMind account.</b></span>\n",
-    "<br></br>\n",
-    "<a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec08fd19",
-   "metadata": {},
-   "source": [
-    "<a id='toc2_3_'></a>\n",
-    "\n",
-    "### Key concepts\n",
-    "\n",
-    "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
-    "\n",
-    "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
-    "\n",
-    "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
-    "\n",
-    "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n",
-    "\n",
-    "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n",
-    "\n",
-    "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
-    "\n",
-    "  - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
-    "  - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
-    "  - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n",
-    "  - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html))\n",
-    "\n",
-    "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n",
-    "\n",
-    "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
-    "\n",
-    "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
-    "\n",
-    "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8883927",
-   "metadata": {},
-   "source": [
-    "<a id='toc3_'></a>\n",
-    "\n",
-    "## Setting up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57bdd8f8",
-   "metadata": {},
-   "source": [
-    "<a id='toc3_1_'></a>\n",
-    "\n",
-    "### Install the ValidMind Library\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>Recommended Python versions</b></span>\n",
-    "<br></br>\n",
-    "Python 3.8 <= x <= 3.11</div>\n",
-    "\n",
-    "To install the library:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1f6dbed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q validmind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "797eb7f0",
-   "metadata": {},
-   "source": [
-    "<a id='toc3_2_'></a>\n",
-    "\n",
-    "### Initialize the ValidMind Library\n",
-    "\n",
-    "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc5c8bd4",
-   "metadata": {},
-   "source": [
-    "<a id='toc3_2_1_'></a>\n",
-    "\n",
-    "#### Get your code snippet\n",
-    "\n",
-    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
-    "\n",
-    "2. In the left sidebar, navigate to **Inventory** and click **+ Register Model**.\n",
-    "\n",
-    "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
-    "\n",
-    "   For example, to register a model for use with this notebook, select:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74d329e8",
-   "metadata": {},
-   "source": [
-    "   - Documentation template: `Binary classification`\n",
-    "   - Use case: `Marketing/Sales - Attrition/Churn Management`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "install-credentials-4c404843-3474-4618-8898-f3bcce33fadd",
-   "metadata": {},
-   "source": [
-    "   You can fill in other options according to your preference.\n",
-    "   \n",
-    "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n",
-    "\n",
-    "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2c1dd22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load your model identifier credentials from an `.env` file\n",
-    "\n",
-    "%load_ext dotenv\n",
-    "%dotenv .env\n",
-    "\n",
-    "# Or replace with your code snippet\n",
-    "\n",
-    "import validmind as vm\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
-    "import validmind as vm\n",
-    "\n",
-    "vm.init(\n",
-    "  api_host = \"https://api.dev.vm.validmind.ai/api/v1/tracking\",\n",
-    "  api_key = \"None\",\n",
-    "  api_secret = \"None\",\n",
-    "  model = \"cltnl28x600001omg9wu8wfty\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "import logging\n",
-    "import sys\n",
-    "import site\n",
-    "\n",
-    "logger = logging.getLogger()\n",
-    "\n",
-    "# Print the path to the current Python interpreter\n",
-    "logger.info(\"Python executable path: \" + sys.executable)\n",
-    "\n",
-    "# Print the path to the site-packages directory\n",
-    "logger.info(\"Site-packages path: \" + str(site.getsitepackages()))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44bf926b",
-   "metadata": {},
-   "source": [
-    "<a id='toc3_3_'></a>\n",
-    "\n",
-    "### Initialize the Python environment\n",
-    "\n",
-    "Then, let's import the necessary libraries and set up your Python environment for data analysis:\n",
-    "\n",
-    "- Import **Extreme Gradient Boosting** (XGBoost) with an alias so that we can reference its functions in later calls. XGBoost is a powerful machine learning library designed for speed and performance, especially in handling structured or tabular data.\n",
-    "- Enable **`matplotlib`**, a plotting library used for visualizing data. Ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62d7c2c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xgboost as xgb\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41474c53",
-   "metadata": {},
-   "source": [
-    "<a id='toc4_'></a>\n",
-    "\n",
-    "## Getting to know ValidMind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d87ae6f",
-   "metadata": {},
-   "source": [
-    "<a id='toc4_1_'></a>\n",
-    "\n",
-    "### Preview the documentation template\n",
-    "\n",
-    "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
-    "\n",
-    "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2bce375",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm.preview_template()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa3b815d",
-   "metadata": {},
-   "source": [
-    "<a id='toc4_2_'></a>\n",
-    "\n",
-    "### View model documentation in the ValidMind Platform\n",
-    "\n",
-    "Next, let's head to the ValidMind Platform to see the template in action:\n",
-    "\n",
-    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
-    "\n",
-    "2. In the left sidebar, navigate to **Inventory** and select the model you registered for this notebook.\n",
-    "\n",
-    "3. Click **Documentation** under Documents for your model and note how the structure of the documentation matches our preview above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bde500ce",
-   "metadata": {},
-   "source": [
-    "<a id='toc5_'></a>\n",
-    "\n",
-    "## Import the sample dataset\n",
-    "\n",
-    "First, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n",
-    "\n",
-    "In our below example, note that: \n",
-    "\n",
-    "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
-    "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58d1c94b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from validmind.datasets.classification import customer_churn\n",
-    "\n",
-    "print(\n",
-    "    f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n",
-    ")\n",
-    "\n",
-    "raw_df = customer_churn.load_data()\n",
-    "raw_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d0aaa8",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_'></a>\n",
-    "\n",
-    "## Preprocessing the raw dataset\n",
-    "\n",
-    "Before running tests with Validmind, we'll need to preprocess our imported dataset. This involves splitting the data and separating the features (inputs) from the targets (outputs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83545763",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_1_'></a>\n",
-    "\n",
-    "### Split the dataset\n",
-    "\n",
-    "Splitting our dataset helps assess how well the model generalizes to unseen data.\n",
-    "\n",
-    "Use [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess) to split our dataset into three subsets:\n",
-    "\n",
-    "1. **train_df** — Used to train the model.\n",
-    "2. **validation_df** — Used to evaluate the model's performance during training.\n",
-    "3. **test_df** — Used later on to asses the model's performance on new, unseen data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "418cb5aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0ae3dc5",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_2_'></a>\n",
-    "\n",
-    "### Separate features and targets\n",
-    "\n",
-    "To train the model, we need to provide it with:\n",
-    "\n",
-    "1. **Inputs** — Features such as customer age, usage, etc.\n",
-    "2. **Outputs (Expected answers/labels)** — in our case, we would like to know whether the customer churned or not.\n",
-    "\n",
-    "Here, we'll use `x_train` and `x_val` to hold the input data (features), and `y_train` and `y_val` to hold the answers (the target we want to predict):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fd365fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train = train_df.drop(customer_churn.target_column, axis=1)\n",
-    "y_train = train_df[customer_churn.target_column]\n",
-    "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n",
-    "y_val = validation_df[customer_churn.target_column]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "541466ce",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_'></a>\n",
-    "\n",
-    "## Training an XGBoost classifier model\n",
-    "\n",
-    "Next, let's create an XGBoost classifier model that will automatically stop training if it doesn’t improve after 10 tries.\n",
-    "\n",
-    "Setting a threshold avoids wasting time and helps prevent overfitting by stopping training when further improvement isn’t happening."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3296cac6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = xgb.XGBClassifier(early_stopping_rounds=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "826d8adf",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_1_'></a>\n",
-    "\n",
-    "### Set evaluation metrics\n",
-    "\n",
-    "Then, we'll set the evaluation metrics, which tells the model to use three different ways to measure its performance:\n",
-    "\n",
-    "1. **error** — Measures how often the model makes incorrect predictions.\n",
-    "2. **logloss** — Indicates how confident the predictions are.\n",
-    "3. **auc** — Evaluates how well the model distinguishes between churn and not churn.\n",
-    "\n",
-    "Using multiple metrics gives a more complete picture of how good (or bad) the model is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32d3c3f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.set_params(\n",
-    "    eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78c85786",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_2_'></a>\n",
-    "\n",
-    "### Fit the model\n",
-    "\n",
-    "Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:\n",
-    "\n",
-    "- The model is trained on `x_train` and `y_train`, and evaluates its performance using `x_val` and `y_val` to check if it’s learning well.\n",
-    "- To turn off printed output while training, we'll set `verbose` to `False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fb95ce4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(\n",
-    "    x_train,\n",
-    "    y_train,\n",
-    "    eval_set=[(x_val, y_val)],\n",
-    "    verbose=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6662e6b",
-   "metadata": {},
-   "source": [
-    "<a id='toc8_'></a>\n",
-    "\n",
-    "## Initialize the ValidMind datasets\n",
-    "\n",
-    "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n",
-    "\n",
-    "For this example, we'll pass in the following arguments:\n",
-    "\n",
-    "- **`dataset`** — The raw dataset that you want to provide as input to tests.\n",
-    "- **`input_id`** — A unique identifier that allows tracking what inputs are used when running each individual test.\n",
-    "- **`target_column`** — A required argument if tests require access to true values. This is the name of the target column in the dataset.\n",
-    "- **`class_labels`** — An optional value to map predicted classes to class labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb6ad06a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize the raw dataset\n",
-    "vm_raw_dataset = vm.init_dataset(\n",
-    "    dataset=raw_df,\n",
-    "    input_id=\"raw_dataset\",\n",
-    "    target_column=customer_churn.target_column,\n",
-    "    class_labels=customer_churn.class_labels,\n",
-    ")\n",
-    "\n",
-    "# Initialize the training dataset\n",
-    "vm_train_ds = vm.init_dataset(\n",
-    "    dataset=train_df,\n",
-    "    input_id=\"train_dataset\",\n",
-    "    target_column=customer_churn.target_column,\n",
-    ")\n",
-    "\n",
-    "# Initialize the testing dataset\n",
-    "vm_test_ds = vm.init_dataset(\n",
-    "    dataset=test_df,\n",
-    "    input_id=\"test_dataset\",\n",
-    "    target_column=customer_churn.target_column\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5da79af3",
-   "metadata": {},
-   "source": [
-    "<a id='toc9_'></a>\n",
-    "\n",
-    "## Initialize a model object\n",
-    "\n",
-    "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n",
-    "\n",
-    "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0e44eebd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm_model = vm.init_model(\n",
-    "    model,\n",
-    "    input_id=\"model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fa481bf",
-   "metadata": {},
-   "source": [
-    "<a id='toc9_1_'></a>\n",
-    "\n",
-    "### Assign predictions\n",
-    "\n",
-    "Once the model has been registered, you can assign model predictions to the training and testing datasets.\n",
-    "\n",
-    "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
-    "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
-    "\n",
-    "If no prediction values are passed, the method will compute predictions automatically:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62bd94fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm_train_ds.assign_predictions(\n",
-    "    model=vm_model,\n",
-    ")\n",
-    "\n",
-    "vm_test_ds.assign_predictions(\n",
-    "    model=vm_model,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5ade4b3",
-   "metadata": {},
-   "source": [
-    "<a id='toc10_'></a>\n",
-    "\n",
-    "## Run the full suite of tests\n",
-    "\n",
-    "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n",
-    "\n",
-    "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform:\n",
-    "\n",
-    "- The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. \n",
-    "- It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n",
-    "\n",
-    "    ```python\n",
-    "    config = {\n",
-    "        \"<test-id>\": {\n",
-    "            \"params\": {\n",
-    "                \"param1\": \"value1\",\n",
-    "                \"param2\": \"value2\",\n",
-    "                ...\n",
-    "            },\n",
-    "            \"inputs\": {\n",
-    "                \"input1\": \"value1\",\n",
-    "                \"input2\": \"value2\",\n",
-    "                ...\n",
-    "            }\n",
-    "        },\n",
-    "        ...\n",
-    "    }\n",
-    "    ```\n",
-    "\n",
-    "    Each `<test-id>` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3d6741b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from validmind.utils import preview_test_config\n",
-    "\n",
-    "test_config = customer_churn.get_demo_test_config()\n",
-    "preview_test_config(test_config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70830a61",
-   "metadata": {},
-   "source": [
-    "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests.\n",
-    "\n",
-    "The variable `full_suite` then holds the result of these tests:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae3accf7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full_suite = vm.run_documentation_tests(config=test_config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79f1b475",
-   "metadata": {},
-   "source": [
-    "<a id='toc11_'></a>\n",
-    "\n",
-    "## In summary\n",
-    "\n",
-    "In this notebook, you learned how to:\n",
-    "\n",
-    "- [x] Register a model within the ValidMind Platform\n",
-    "- [x] Install and initialize the ValidMind Library\n",
-    "- [x] Preview the documentation template for your model\n",
-    "- [x] Import a sample dataset\n",
-    "- [x] Initialize ValidMind datasets and model objects\n",
-    "- [x] Assign model predictions to your ValidMind model objects\n",
-    "- [x] Run a full suite of documentation tests\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adcc9956",
-   "metadata": {},
-   "source": [
-    "<a id='toc12_'></a>\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b8e72f6",
-   "metadata": {},
-   "source": [
-    "<a id='toc12_1_'></a>\n",
-    "\n",
-    "### Work with your model documentation\n",
-    "\n",
-    "1. From the **Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n",
-    "\n",
-    "2. In the left sidebar that appears for your model, click **Documentation**.\n",
-    "\n",
-    "    What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/working-with-model-documentation.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "585b79fd",
-   "metadata": {},
-   "source": [
-    "<a id='toc12_2_'></a>\n",
-    "\n",
-    "### Discover more learning resources\n",
-    "\n",
-    "For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:\n",
-    "\n",
-    "- **[ValidMind for model development](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)**\n",
-    "- **[Developer Fundamentals](https://docs.validmind.ai/training/developer-fundamentals/developer-fundamentals-register.html)**\n",
-    "\n",
-    "We also offer many interactive notebooks to help you document models:\n",
-    "\n",
-    "- [Run tests & test suites](https://docs.validmind.ai/guide/testing-overview.html)\n",
-    "- [Code samples](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n",
-    "\n",
-    "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "790f47cb",
-   "metadata": {},
-   "source": [
-    "<a id='toc13_'></a>\n",
-    "\n",
-    "## Upgrade ValidMind\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\">After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.</div>\n",
-    "\n",
-    "Retrieve the information for the currently installed version of ValidMind:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "35955b6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip show validmind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "upgrade-version-ab5a531d-4334-4c5b-b6aa-754e336f127f",
-   "metadata": {},
-   "source": [
-    "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n",
-    "\n",
-    "```bash\n",
-    "%pip install --upgrade validmind\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "upgrade-restart-faf9c324-3332-4eaf-aef5-2c0ca650d5ca",
-   "metadata": {},
-   "source": [
-    "You may need to restart your kernel after running the upgrade package for changes to be applied."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ValidMind Library",
-   "language": "python",
-   "name": "validmind"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/quickstart/quickstart_model_documentation.out.ipynb b/notebooks/quickstart/quickstart_model_documentation.out.ipynb
deleted file mode 100644
index d05bbdfea..000000000
--- a/notebooks/quickstart/quickstart_model_documentation.out.ipynb
+++ /dev/null
@@ -1,1367 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f2c17b2d",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007034,
-     "end_time": "2025-07-30T17:51:38.340583",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.333549",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "# Quickstart for model documentation\n",
-    "\n",
-    "Learn the basics of using ValidMind to document models as part of a model development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.\n",
-    "\n",
-    "To document a model with the ValidMind Library, we'll:\n",
-    "\n",
-    "1. Import a sample dataset and preprocess it\n",
-    "2. Split the datasets and initialize them for use with ValidMind\n",
-    "3. Initialize a model object for use with testing\n",
-    "4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05dce32a",
-   "metadata": {
-    "papermill": {
-     "duration": 0.061162,
-     "end_time": "2025-07-30T17:51:38.408885",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.347723",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "::: {.content-hidden when-format=\"html\"}\n",
-    "## Contents    \n",
-    "- [Introduction](#toc1_)    \n",
-    "- [About ValidMind](#toc2_)    \n",
-    "  - [Before you begin](#toc2_1_)    \n",
-    "  - [New to ValidMind?](#toc2_2_)    \n",
-    "  - [Key concepts](#toc2_3_)    \n",
-    "- [Setting up](#toc3_)    \n",
-    "  - [Install the ValidMind Library](#toc3_1_)    \n",
-    "  - [Initialize the ValidMind Library](#toc3_2_)    \n",
-    "    - [Get your code snippet](#toc3_2_1_)    \n",
-    "  - [Initialize the Python environment](#toc3_3_)    \n",
-    "- [Getting to know ValidMind](#toc4_)    \n",
-    "  - [Preview the documentation template](#toc4_1_)    \n",
-    "  - [View model documentation in the ValidMind Platform](#toc4_2_)    \n",
-    "- [Import the sample dataset](#toc5_)    \n",
-    "- [Preprocessing the raw dataset](#toc6_)    \n",
-    "  - [Split the dataset](#toc6_1_)    \n",
-    "  - [Separate features and targets](#toc6_2_)    \n",
-    "- [Training an XGBoost classifier model](#toc7_)    \n",
-    "  - [Set evaluation metrics](#toc7_1_)    \n",
-    "  - [Fit the model](#toc7_2_)    \n",
-    "- [Initialize the ValidMind datasets](#toc8_)    \n",
-    "- [Initialize a model object](#toc9_)    \n",
-    "  - [Assign predictions](#toc9_1_)    \n",
-    "- [Run the full suite of tests](#toc10_)    \n",
-    "- [In summary](#toc11_)    \n",
-    "- [Next steps](#toc12_)    \n",
-    "  - [Work with your model documentation](#toc12_1_)    \n",
-    "  - [Discover more learning resources](#toc12_2_)    \n",
-    "- [Upgrade ValidMind](#toc13_)    \n",
-    "\n",
-    ":::\n",
-    "<!-- jn-toc-notebook-config\n",
-    "\tnumbering=false\n",
-    "\tanchor=true\n",
-    "\tflat=false\n",
-    "\tminLevel=2\n",
-    "\tmaxLevel=4\n",
-    "\t/jn-toc-notebook-config -->\n",
-    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1919918",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004919,
-     "end_time": "2025-07-30T17:51:38.418578",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.413659",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc1_'></a>\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "Model development aims to produce a fit-for-purpose *champion model* by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.\n",
-    "\n",
-    "A *binary classification model* is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.\n",
-    "\n",
-    "- This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.\n",
-    "- Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8f85783",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004634,
-     "end_time": "2025-07-30T17:51:38.428222",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.423588",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc2_'></a>\n",
-    "\n",
-    "## About ValidMind\n",
-    "\n",
-    "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models. \n",
-    "\n",
-    "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87fbc9d8",
-   "metadata": {
-    "papermill": {
-     "duration": 0.005599,
-     "end_time": "2025-07-30T17:51:38.438933",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.433334",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc2_1_'></a>\n",
-    "\n",
-    "### Before you begin\n",
-    "\n",
-    "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n",
-    "\n",
-    "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f368277",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004848,
-     "end_time": "2025-07-30T17:51:38.449279",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.444431",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc2_2_'></a>\n",
-    "\n",
-    "### New to ValidMind?\n",
-    "\n",
-    "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, you'll need access to a ValidMind account.</b></span>\n",
-    "<br></br>\n",
-    "<a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec08fd19",
-   "metadata": {
-    "papermill": {
-     "duration": 0.005091,
-     "end_time": "2025-07-30T17:51:38.459919",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.454828",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc2_3_'></a>\n",
-    "\n",
-    "### Key concepts\n",
-    "\n",
-    "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
-    "\n",
-    "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
-    "\n",
-    "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
-    "\n",
-    "**Metrics**: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.\n",
-    "\n",
-    "**Custom metrics**: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.\n",
-    "\n",
-    "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
-    "\n",
-    "  - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
-    "  - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
-    "  - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.\n",
-    "  - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: [Run tests with multiple datasets](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html))\n",
-    "\n",
-    "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.\n",
-    "\n",
-    "**Outputs**: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
-    "\n",
-    "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
-    "\n",
-    "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8883927",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007581,
-     "end_time": "2025-07-30T17:51:38.472820",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.465239",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc3_'></a>\n",
-    "\n",
-    "## Setting up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57bdd8f8",
-   "metadata": {
-    "papermill": {
-     "duration": 0.005038,
-     "end_time": "2025-07-30T17:51:38.483109",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.478071",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc3_1_'></a>\n",
-    "\n",
-    "### Install the ValidMind Library\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>Recommended Python versions</b></span>\n",
-    "<br></br>\n",
-    "Python 3.8 <= x <= 3.11</div>\n",
-    "\n",
-    "To install the library:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d1f6dbed",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-07-30T17:51:38.494647Z",
-     "iopub.status.busy": "2025-07-30T17:51:38.494395Z",
-     "iopub.status.idle": "2025-07-30T17:51:40.257184Z",
-     "shell.execute_reply": "2025-07-30T17:51:40.256656Z"
-    },
-    "papermill": {
-     "duration": 1.76978,
-     "end_time": "2025-07-30T17:51:40.258435",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:38.488655",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -q validmind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "797eb7f0",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004717,
-     "end_time": "2025-07-30T17:51:40.268705",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:40.263988",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc3_2_'></a>\n",
-    "\n",
-    "### Initialize the ValidMind Library\n",
-    "\n",
-    "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc5c8bd4",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004651,
-     "end_time": "2025-07-30T17:51:40.278141",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:40.273490",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc3_2_1_'></a>\n",
-    "\n",
-    "#### Get your code snippet\n",
-    "\n",
-    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
-    "\n",
-    "2. In the left sidebar, navigate to **Inventory** and click **+ Register Model**.\n",
-    "\n",
-    "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
-    "\n",
-    "   For example, to register a model for use with this notebook, select:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74d329e8",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004702,
-     "end_time": "2025-07-30T17:51:40.287362",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:40.282660",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "   - Documentation template: `Binary classification`\n",
-    "   - Use case: `Marketing/Sales - Attrition/Churn Management`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "install-credentials-4c404843-3474-4618-8898-f3bcce33fadd",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004587,
-     "end_time": "2025-07-30T17:51:40.296531",
-     "exception": false,
-     "start_time": "2025-07-30T17:51:40.291944",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "   You can fill in other options according to your preference.\n",
-    "   \n",
-    "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n",
-    "\n",
-    "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2c1dd22",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": false,
-     "start_time": "2025-07-30T17:51:40.301021",
-     "status": "running"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Load your model identifier credentials from an `.env` file\n",
-    "\n",
-    "%load_ext dotenv\n",
-    "%dotenv .env\n",
-    "\n",
-    "# Or replace with your code snippet\n",
-    "\n",
-    "import validmind as vm\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
-    "import validmind as vm\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
-    "import validmind as vm\n",
-    "\n",
-    "vm.init(\n",
-    "  api_host = \"https://api.dev.vm.validmind.ai/api/v1/tracking\",\n",
-    "  api_key = \"dea7a1c03b4bff0e4b969c9719d89492\",\n",
-    "  api_secret = \"65c3deb6f1c579a5e8a2aa3989c04068e77c42d5ab5e7c89ffd17aff25b291cc\",\n",
-    "  model = \"cmafl60cm000u1fcseem2u3j6\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "import logging\n",
-    "import sys\n",
-    "import site\n",
-    "\n",
-    "logger = logging.getLogger()\n",
-    "\n",
-    "# Print the path to the current Python interpreter\n",
-    "logger.info(\"Python executable path: \" + sys.executable)\n",
-    "\n",
-    "# Print the path to the site-packages directory\n",
-    "logger.info(\"Site-packages path: \" + str(site.getsitepackages()))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44bf926b",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc3_3_'></a>\n",
-    "\n",
-    "### Initialize the Python environment\n",
-    "\n",
-    "Then, let's import the necessary libraries and set up your Python environment for data analysis:\n",
-    "\n",
-    "- Import **Extreme Gradient Boosting** (XGBoost) with an alias so that we can reference its functions in later calls. XGBoost is a powerful machine learning library designed for speed and performance, especially in handling structured or tabular data.\n",
-    "- Enable **`matplotlib`**, a plotting library used for visualizing data. Ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62d7c2c1",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import xgboost as xgb\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41474c53",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc4_'></a>\n",
-    "\n",
-    "## Getting to know ValidMind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d87ae6f",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc4_1_'></a>\n",
-    "\n",
-    "### Preview the documentation template\n",
-    "\n",
-    "Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate *template* is selected for your model. A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
-    "\n",
-    "You will upload documentation and test results unique to your model based on this template later on. For now, **take a look at the default structure that the template provides with [the `vm.preview_template()` function](https://docs.validmind.ai/validmind/validmind.html#preview_template)** from the ValidMind library and note the empty sections:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2bce375",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "vm.preview_template()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa3b815d",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc4_2_'></a>\n",
-    "\n",
-    "### View model documentation in the ValidMind Platform\n",
-    "\n",
-    "Next, let's head to the ValidMind Platform to see the template in action:\n",
-    "\n",
-    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
-    "\n",
-    "2. In the left sidebar, navigate to **Inventory** and select the model you registered for this notebook.\n",
-    "\n",
-    "3. Click **Documentation** under Documents for your model and note how the structure of the documentation matches our preview above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bde500ce",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc5_'></a>\n",
-    "\n",
-    "## Import the sample dataset\n",
-    "\n",
-    "First, let's import the public [Bank Customer Churn Prediction](https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction) dataset from Kaggle so that we have something to work with.\n",
-    "\n",
-    "In our below example, note that: \n",
-    "\n",
-    "- The target column, `Exited` has a value of `1` when a customer has churned and `0` otherwise.\n",
-    "- The ValidMind Library provides a wrapper to automatically load the dataset as a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58d1c94b",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from validmind.datasets.classification import customer_churn\n",
-    "\n",
-    "print(\n",
-    "    f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{customer_churn.target_column}' \\n\\t• Class labels: {customer_churn.class_labels}\"\n",
-    ")\n",
-    "\n",
-    "raw_df = customer_churn.load_data()\n",
-    "raw_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d0aaa8",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc6_'></a>\n",
-    "\n",
-    "## Preprocessing the raw dataset\n",
-    "\n",
-    "Before running tests with Validmind, we'll need to preprocess our imported dataset. This involves splitting the data and separating the features (inputs) from the targets (outputs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83545763",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc6_1_'></a>\n",
-    "\n",
-    "### Split the dataset\n",
-    "\n",
-    "Splitting our dataset helps assess how well the model generalizes to unseen data.\n",
-    "\n",
-    "Use [`preprocess()`](https://docs.validmind.ai/validmind/validmind/datasets/classification/customer_churn.html#preprocess) to split our dataset into three subsets:\n",
-    "\n",
-    "1. **train_df** — Used to train the model.\n",
-    "2. **validation_df** — Used to evaluate the model's performance during training.\n",
-    "3. **test_df** — Used later on to asses the model's performance on new, unseen data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "418cb5aa",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "train_df, validation_df, test_df = customer_churn.preprocess(raw_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0ae3dc5",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc6_2_'></a>\n",
-    "\n",
-    "### Separate features and targets\n",
-    "\n",
-    "To train the model, we need to provide it with:\n",
-    "\n",
-    "1. **Inputs** — Features such as customer age, usage, etc.\n",
-    "2. **Outputs (Expected answers/labels)** — in our case, we would like to know whether the customer churned or not.\n",
-    "\n",
-    "Here, we'll use `x_train` and `x_val` to hold the input data (features), and `y_train` and `y_val` to hold the answers (the target we want to predict):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fd365fd",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "x_train = train_df.drop(customer_churn.target_column, axis=1)\n",
-    "y_train = train_df[customer_churn.target_column]\n",
-    "x_val = validation_df.drop(customer_churn.target_column, axis=1)\n",
-    "y_val = validation_df[customer_churn.target_column]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "541466ce",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc7_'></a>\n",
-    "\n",
-    "## Training an XGBoost classifier model\n",
-    "\n",
-    "Next, let's create an XGBoost classifier model that will automatically stop training if it doesn’t improve after 10 tries.\n",
-    "\n",
-    "Setting a threshold avoids wasting time and helps prevent overfitting by stopping training when further improvement isn’t happening."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3296cac6",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model = xgb.XGBClassifier(early_stopping_rounds=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "826d8adf",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc7_1_'></a>\n",
-    "\n",
-    "### Set evaluation metrics\n",
-    "\n",
-    "Then, we'll set the evaluation metrics, which tells the model to use three different ways to measure its performance:\n",
-    "\n",
-    "1. **error** — Measures how often the model makes incorrect predictions.\n",
-    "2. **logloss** — Indicates how confident the predictions are.\n",
-    "3. **auc** — Evaluates how well the model distinguishes between churn and not churn.\n",
-    "\n",
-    "Using multiple metrics gives a more complete picture of how good (or bad) the model is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32d3c3f4",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model.set_params(\n",
-    "    eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78c85786",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc7_2_'></a>\n",
-    "\n",
-    "### Fit the model\n",
-    "\n",
-    "Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:\n",
-    "\n",
-    "- The model is trained on `x_train` and `y_train`, and evaluates its performance using `x_val` and `y_val` to check if it’s learning well.\n",
-    "- To turn off printed output while training, we'll set `verbose` to `False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fb95ce4",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model.fit(\n",
-    "    x_train,\n",
-    "    y_train,\n",
-    "    eval_set=[(x_val, y_val)],\n",
-    "    verbose=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6662e6b",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc8_'></a>\n",
-    "\n",
-    "## Initialize the ValidMind datasets\n",
-    "\n",
-    "Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind `Dataset` object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module. **This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind,** but you only need to do it once per dataset.\n",
-    "\n",
-    "For this example, we'll pass in the following arguments:\n",
-    "\n",
-    "- **`dataset`** — The raw dataset that you want to provide as input to tests.\n",
-    "- **`input_id`** — A unique identifier that allows tracking what inputs are used when running each individual test.\n",
-    "- **`target_column`** — A required argument if tests require access to true values. This is the name of the target column in the dataset.\n",
-    "- **`class_labels`** — An optional value to map predicted classes to class labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb6ad06a",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Initialize the raw dataset\n",
-    "vm_raw_dataset = vm.init_dataset(\n",
-    "    dataset=raw_df,\n",
-    "    input_id=\"raw_dataset\",\n",
-    "    target_column=customer_churn.target_column,\n",
-    "    class_labels=customer_churn.class_labels,\n",
-    ")\n",
-    "\n",
-    "# Initialize the training dataset\n",
-    "vm_train_ds = vm.init_dataset(\n",
-    "    dataset=train_df,\n",
-    "    input_id=\"train_dataset\",\n",
-    "    target_column=customer_churn.target_column,\n",
-    ")\n",
-    "\n",
-    "# Initialize the testing dataset\n",
-    "vm_test_ds = vm.init_dataset(\n",
-    "    dataset=test_df,\n",
-    "    input_id=\"test_dataset\",\n",
-    "    target_column=customer_churn.target_column\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5da79af3",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc9_'></a>\n",
-    "\n",
-    "## Initialize a model object\n",
-    "\n",
-    "You'll also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data for our model.\n",
-    "\n",
-    "You simply initialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0e44eebd",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "vm_model = vm.init_model(\n",
-    "    model,\n",
-    "    input_id=\"model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fa481bf",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc9_1_'></a>\n",
-    "\n",
-    "### Assign predictions\n",
-    "\n",
-    "Once the model has been registered, you can assign model predictions to the training and testing datasets.\n",
-    "\n",
-    "- The [`assign_predictions()` method](https://docs.validmind.ai/validmind/validmind/vm_models.html#assign_predictions) from the `Dataset` object can link existing predictions to any number of models.\n",
-    "- This method links the model's class prediction values and probabilities to our `vm_train_ds` and `vm_test_ds` datasets.\n",
-    "\n",
-    "If no prediction values are passed, the method will compute predictions automatically:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62bd94fc",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "vm_train_ds.assign_predictions(\n",
-    "    model=vm_model,\n",
-    ")\n",
-    "\n",
-    "vm_test_ds.assign_predictions(\n",
-    "    model=vm_model,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5ade4b3",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc10_'></a>\n",
-    "\n",
-    "## Run the full suite of tests\n",
-    "\n",
-    "This is where it all comes together — you are now ready to **run the documentation tests for the model as defined by the documentation template** you looked at earlier.\n",
-    "\n",
-    "The [`vm.run_documentation_tests`](https://docs.validmind.ai/validmind/validmind.html#run_documentation_tests) function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform:\n",
-    "\n",
-    "- The function requires information about the inputs to use on every test. These inputs can be passed as an `inputs` argument if we want to use the same inputs for all tests. \n",
-    "- It's also possible to pass a `config` argument that has information about the `params` and `inputs` that each test requires. The `config` parameter is a dictionary with the following structure:\n",
-    "\n",
-    "    ```python\n",
-    "    config = {\n",
-    "        \"<test-id>\": {\n",
-    "            \"params\": {\n",
-    "                \"param1\": \"value1\",\n",
-    "                \"param2\": \"value2\",\n",
-    "                ...\n",
-    "            },\n",
-    "            \"inputs\": {\n",
-    "                \"input1\": \"value1\",\n",
-    "                \"input2\": \"value2\",\n",
-    "                ...\n",
-    "            }\n",
-    "        },\n",
-    "        ...\n",
-    "    }\n",
-    "    ```\n",
-    "\n",
-    "    Each `<test-id>` above corresponds to the test driven block identifiers shown by `vm.preview_template()`. For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method `get_demo_test_config()` below constructs the default input configuration for our demo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3d6741b",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from validmind.utils import preview_test_config\n",
-    "\n",
-    "test_config = customer_churn.get_demo_test_config()\n",
-    "preview_test_config(test_config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70830a61",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "Now we can pass the input configuration to `vm.run_documentation_tests()` and run the full suite of tests.\n",
-    "\n",
-    "The variable `full_suite` then holds the result of these tests:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae3accf7",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "full_suite = vm.run_documentation_tests(config=test_config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79f1b475",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc11_'></a>\n",
-    "\n",
-    "## In summary\n",
-    "\n",
-    "In this notebook, you learned how to:\n",
-    "\n",
-    "- [x] Register a model within the ValidMind Platform\n",
-    "- [x] Install and initialize the ValidMind Library\n",
-    "- [x] Preview the documentation template for your model\n",
-    "- [x] Import a sample dataset\n",
-    "- [x] Initialize ValidMind datasets and model objects\n",
-    "- [x] Assign model predictions to your ValidMind model objects\n",
-    "- [x] Run a full suite of documentation tests\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adcc9956",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc12_'></a>\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b8e72f6",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc12_1_'></a>\n",
-    "\n",
-    "### Work with your model documentation\n",
-    "\n",
-    "1. From the **Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n",
-    "\n",
-    "2. In the left sidebar that appears for your model, click **Documentation**.\n",
-    "\n",
-    "    What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready. [Learn more ...](https://docs.validmind.ai/guide/working-with-model-documentation.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "585b79fd",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc12_2_'></a>\n",
-    "\n",
-    "### Discover more learning resources\n",
-    "\n",
-    "For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:\n",
-    "\n",
-    "- **[ValidMind for model development](https://docs.validmind.ai/developer/validmind-library.html#for-model-development)**\n",
-    "- **[Developer Fundamentals](https://docs.validmind.ai/training/developer-fundamentals/developer-fundamentals-register.html)**\n",
-    "\n",
-    "We also offer many interactive notebooks to help you document models:\n",
-    "\n",
-    "- [Run tests & test suites](https://docs.validmind.ai/guide/testing-overview.html)\n",
-    "- [Code samples](https://docs.validmind.ai/guide/samples-jupyter-notebooks.html)\n",
-    "\n",
-    "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "790f47cb",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "<a id='toc13_'></a>\n",
-    "\n",
-    "## Upgrade ValidMind\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\">After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.</div>\n",
-    "\n",
-    "Retrieve the information for the currently installed version of ValidMind:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "35955b6b",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip show validmind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "upgrade-version-ab5a531d-4334-4c5b-b6aa-754e336f127f",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n",
-    "\n",
-    "```bash\n",
-    "%pip install --upgrade validmind\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "upgrade-restart-faf9c324-3332-4eaf-aef5-2c0ca650d5ca",
-   "metadata": {
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "source": [
-    "You may need to restart your kernel after running the upgrade package for changes to be applied."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ValidMind Library",
-   "language": "python",
-   "name": "validmind"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.13"
-  },
-  "papermill": {
-   "default_parameters": {},
-   "duration": null,
-   "end_time": null,
-   "environment_variables": {},
-   "exception": null,
-   "input_path": "/Users/alexiamharger/Code/validmind-library/notebooks/quickstart/quickstart_model_documentation.ipynb",
-   "output_path": "/Users/alexiamharger/Code/validmind-library/notebooks/quickstart/quickstart_model_documentation.out.ipynb",
-   "parameters": {},
-   "start_time": "2025-07-30T17:51:37.183297",
-   "version": "2.6.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file

From 57e128785d24b4653ae9ce513bae501cfc354ec8 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 16:38:18 -0400
Subject: [PATCH 08/47] fix: enhance dependency testing workflow to
 conditionally run tox based on dependency type

---
 .github/workflows/dependency-testing.yaml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 21fd24d15..5f7bfb467 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -74,7 +74,11 @@ jobs:
 
       - name: Run dependency tests
         run: |
-          tox -e py$(echo ${{ matrix.python-version }} | tr -d .)-${{ matrix.deps-type }}
+          if [[ "${{ matrix.deps-type }}" == "default" ]]; then
+            tox -e py$(echo ${{ matrix.python-version }} | tr -d .)
+          else
+            tox -e py$(echo ${{ matrix.python-version }} | tr -d .)-${{ matrix.deps-type }}
+          fi
         env:
           # Limit OpenMP threads to prevent CI resource issues
           OMP_NUM_THREADS: 1

From 86c00f24220496cdae444af1893ac9283dfee263 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 17:11:40 -0400
Subject: [PATCH 09/47] chore: update system dependencies in CI workflows and
 clean up quickstart model documentation

---
 .github/workflows/dependency-testing.yaml     | 12 +++----
 .github/workflows/python.yaml                 |  5 +++
 .../quickstart_model_documentation.ipynb      | 34 +++----------------
 3 files changed, 16 insertions(+), 35 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 5f7bfb467..c33f8a9a3 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -65,7 +65,7 @@ jobs:
       - name: Install system dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y build-essential
+          sudo apt-get install -y build-essential libomp-dev
 
       - name: Install tox
         run: |
@@ -101,7 +101,7 @@ jobs:
       - name: Install system dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y build-essential
+          sudo apt-get install -y build-essential libomp-dev
 
       - name: Install tox
         run: |
@@ -110,7 +110,7 @@ jobs:
 
       - name: Test pip freeze environments
         run: |
-          tox -e pip-freeze-test
+          tox -e freeze
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
@@ -132,7 +132,7 @@ jobs:
       - name: Install system dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y build-essential
+          sudo apt-get install -y build-essential libomp-dev
 
       - name: Install tox
         run: |
@@ -163,7 +163,7 @@ jobs:
       - name: Install system dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y build-essential
+          sudo apt-get install -y build-essential libomp-dev
 
       - name: Install tox
         run: |
@@ -194,7 +194,7 @@ jobs:
       - name: Install system dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y build-essential
+          sudo apt-get install -y build-essential libomp-dev
 
       - name: Install tox
         run: |
diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 3623e74cb..221b989e7 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -44,6 +44,11 @@ jobs:
 
       - uses: actions/checkout@v4
 
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential libomp-dev
+
       - name: Install poetry
         run: pipx install poetry
 
diff --git a/notebooks/quickstart/quickstart_model_documentation.ipynb b/notebooks/quickstart/quickstart_model_documentation.ipynb
index 4b939c320..6a104035d 100644
--- a/notebooks/quickstart/quickstart_model_documentation.ipynb
+++ b/notebooks/quickstart/quickstart_model_documentation.ipynb
@@ -264,36 +264,12 @@
     "\n",
     "import validmind as vm\n",
     "\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
-    "import validmind as vm\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"VALIDMIND_LLM_DESCRIPTIONS_ENABLED\"] = \"0\"\n",
-    "import validmind as vm\n",
-    "\n",
     "vm.init(\n",
-    "  api_host = \"https://api.dev.vm.validmind.ai/api/v1/tracking\",\n",
-    "  api_key = \"dea7a1c03b4bff0e4b969c9719d89492\",\n",
-    "  api_secret = \"65c3deb6f1c579a5e8a2aa3989c04068e77c42d5ab5e7c89ffd17aff25b291cc\",\n",
-    "  model = \"cmafl60cm000u1fcseem2u3j6\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "import logging\n",
-    "import sys\n",
-    "import site\n",
-    "\n",
-    "logger = logging.getLogger()\n",
-    "\n",
-    "# Print the path to the current Python interpreter\n",
-    "logger.info(\"Python executable path: \" + sys.executable)\n",
-    "\n",
-    "# Print the path to the site-packages directory\n",
-    "logger.info(\"Site-packages path: \" + str(site.getsitepackages()))\n",
-    "\n"
+    "    api_host=\"https://api.validmind.ai/api/v1/tracking\",\n",
+    "    api_key=\"<your-api-key>\",\n",
+    "    api_secret=\"<your-api-secret>\",\n",
+    "    model=\"<your-model-id>\"\n",
+    ")"
    ]
   },
   {

From 2d5d0c566b563054c3c4bebd923b8f0d43f07f00 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 30 Jul 2025 17:25:10 -0400
Subject: [PATCH 10/47] chore: update numpy and scikit-learn versions in tox
 configuration

---
 tox.ini | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tox.ini b/tox.ini
index 21c1a8d5a..08b40f983 100644
--- a/tox.ini
+++ b/tox.ini
@@ -35,9 +35,9 @@ commands_pre =
     poetry config virtualenvs.create false
     poetry install --all-extras
     # Install minimum versions of key dependencies
-    pip install pandas==2.0.3 numpy==1.21.0 scikit-learn==0.24.0 matplotlib==3.5.0
+    pip install pandas==2.0.3 numpy==1.23.2 scikit-learn==1.1.0 matplotlib==3.5.0
     pip install transformers==4.32.0 torch==2.0.0 datasets==2.10.0 xgboost==1.5.2
-    pip install nltk==3.8.1 plotly==5.0.0 scipy==1.7.0 statsmodels==0.13.0
+    pip install nltk==3.8.1 plotly==5.0.0 scipy==1.9.0 statsmodels==0.13.0
 
 [testenv:py{39,310,311,312}-max]
 description = Test with latest dependency versions

From 821c70cef2bb269016504876f23bd9d2bc5346cb Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Thu, 31 Jul 2025 12:06:13 -0400
Subject: [PATCH 11/47] chore: update torch version in pyproject.toml and
 tox.ini for improved compatibility

---
 pyproject.toml | 2 +-
 tox.ini        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 939a49c3b..89ed68839 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,7 +47,7 @@ statsmodels = { version = "*", optional = true }
 tabulate = "^0.8.9"
 textblob = "^0.18.0.post0"
 tiktoken = "*"
-torch = { version = "2.7.0", optional = true }
+torch = { version = ">=2.0.0", optional = true }
 tqdm = "*"
 transformers = { version = "^4.32.0", optional = true }
 xgboost = { version = ">=1.5.2,<3", optional = true }
diff --git a/tox.ini b/tox.ini
index 08b40f983..0eadd4939 100644
--- a/tox.ini
+++ b/tox.ini
@@ -36,7 +36,7 @@ commands_pre =
     poetry install --all-extras
     # Install minimum versions of key dependencies
     pip install pandas==2.0.3 numpy==1.23.2 scikit-learn==1.1.0 matplotlib==3.5.0
-    pip install transformers==4.32.0 torch==2.0.0 datasets==2.10.0 xgboost==1.5.2
+    pip install transformers==4.32.0 torch==2.4.0 datasets==2.10.0 xgboost==1.5.2
     pip install nltk==3.8.1 plotly==5.0.0 scipy==1.9.0 statsmodels==0.13.0
 
 [testenv:py{39,310,311,312}-max]

From e125f3c8a582d36f8b93507238cd0101169e75b8 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 1 Aug 2025 11:39:55 -0400
Subject: [PATCH 12/47] chore: update content hash in poetry.lock to reflect
 dependency changes

---
 poetry.lock | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index c9c82197a..ff6e5392e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -9069,4 +9069,4 @@ xgboost = ["xgboost"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9.0,<3.13"
-content-hash = "08fa1ad9af71a111947dffbbf5e3e2c09475ed680238415cf2d65b3d0e0002d9"
+content-hash = "20d2140290eba9e4d465d813dde25497a2aa06334bc847b2218e01c4cb6055e1"

From 7ccaec54593698338f5bc9ed5008ebbdef89403e Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Mon, 4 Aug 2025 11:53:38 -0400
Subject: [PATCH 13/47] chore: update numpy version in tox.ini for
 compatibility with latest dependencies

---
 tox.ini | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tox.ini b/tox.ini
index 0eadd4939..66a35eaab 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,5 +1,5 @@
 [tox]
-envlist = 
+envlist =
     py{39,310,311,312}
     py{39,310,311,312}-min
     py{39,310,311,312}-max
@@ -9,13 +9,13 @@ isolated_build = true
 skip_missing_interpreters = true
 
 [testenv]
-deps = 
+deps =
     poetry
 setenv =
     # Limit OpenMP on Mac to prevent segfaults
     OMP_NUM_THREADS = 1
     MKL_NUM_THREADS = 1
-allowlist_externals = 
+allowlist_externals =
     poetry
     pip
 commands_pre =
@@ -35,7 +35,7 @@ commands_pre =
     poetry config virtualenvs.create false
     poetry install --all-extras
     # Install minimum versions of key dependencies
-    pip install pandas==2.0.3 numpy==1.23.2 scikit-learn==1.1.0 matplotlib==3.5.0
+    pip install pandas==2.0.3 numpy==1.26.0 scikit-learn==1.1.0 matplotlib==3.5.0
     pip install transformers==4.32.0 torch==2.4.0 datasets==2.10.0 xgboost==1.5.2
     pip install nltk==3.8.1 plotly==5.0.0 scipy==1.9.0 statsmodels==0.13.0
 
@@ -62,7 +62,7 @@ commands =
 [testenv:lint]
 commands_pre =
     python -m pip install --upgrade pip
-    pip install poetry  
+    pip install poetry
     poetry config virtualenvs.create false
     poetry install --all-extras
 commands =
@@ -72,7 +72,7 @@ commands =
 
 [testenv:freeze]
 description = Test with a specific pip freeze file (specify via FREEZE_FILE env var)
-allowlist_externals = 
+allowlist_externals =
     poetry
     pip
     bash
@@ -83,4 +83,4 @@ commands_pre =
     bash -c 'if [ -n "$FREEZE_FILE" ] && [ -f "$FREEZE_FILE" ]; then pip install -r "$FREEZE_FILE"; else echo "Set FREEZE_FILE env var to pip freeze file path"; exit 1; fi'
     poetry install --no-deps
 commands =
-    poetry run python -m unittest discover tests -v 
\ No newline at end of file
+    poetry run python -m unittest discover tests -v

From cbc807ab2990dc5f6ae022a04643e2120d2fa6a8 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 6 Aug 2025 11:25:17 -0400
Subject: [PATCH 14/47] fix: remove unnecessary blank line in __init__.py

---
 validmind/__init__.py        | 2 +-
 validmind/tests/__types__.py | 8 ++++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/validmind/__init__.py b/validmind/__init__.py
index 7bc2cd953..9b19df45a 100644
--- a/validmind/__init__.py
+++ b/validmind/__init__.py
@@ -86,7 +86,7 @@ def check_version():
     except metadata.PackageNotFoundError:
         # Package metadata not found, skip version check
         return
-    
+
     running = __version__
 
     if parse_version(installed) > parse_version(running):
diff --git a/validmind/tests/__types__.py b/validmind/tests/__types__.py
index 43346c41d..3979aa58c 100644
--- a/validmind/tests/__types__.py
+++ b/validmind/tests/__types__.py
@@ -187,6 +187,10 @@
         "validmind.ongoing_monitoring.ScoreBandsDrift",
         "validmind.ongoing_monitoring.ScorecardHistogramDrift",
         "validmind.ongoing_monitoring.TargetPredictionDistributionPlot",
+        "validmind.plots.BoxPlot",
+        "validmind.plots.CorrelationHeatmap",
+        "validmind.plots.HistogramPlot",
+        "validmind.plots.ViolinPlot",
         "validmind.prompt_validation.Bias",
         "validmind.prompt_validation.Clarity",
         "validmind.prompt_validation.Conciseness",
@@ -194,6 +198,10 @@
         "validmind.prompt_validation.NegativeInstruction",
         "validmind.prompt_validation.Robustness",
         "validmind.prompt_validation.Specificity",
+        "validmind.stats.CorrelationAnalysis",
+        "validmind.stats.DescriptiveStats",
+        "validmind.stats.NormalityTests",
+        "validmind.stats.OutlierDetection",
         "validmind.unit_metrics.classification.Accuracy",
         "validmind.unit_metrics.classification.F1",
         "validmind.unit_metrics.classification.Precision",

From ae9b2230365d092ff40a54cff43bdfc1616fe6eb Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Wed, 6 Aug 2025 11:50:16 -0400
Subject: [PATCH 15/47] chore: clean up dependency testing workflows by
 removing unnecessary blank lines and disabling pip caching

---
 .github/workflows/dependency-testing.yaml | 29 ++++++++++++-----------
 .github/workflows/python.yaml             |  2 +-
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index c33f8a9a3..56bb4ab16 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -52,7 +52,7 @@ jobs:
             deps-type: 'default'
           - python-version: '3.11'
             deps-type: 'default'
-    
+
     steps:
       - uses: actions/checkout@v4
 
@@ -60,7 +60,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
 
       - name: Install system dependencies
         run: |
@@ -83,12 +82,14 @@ jobs:
           # Limit OpenMP threads to prevent CI resource issues
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
+          # Disable pip caching to prevent matrix job conflicts
+          PIP_NO_CACHE_DIR: 1
 
   pip-freeze-testing:
     name: Test Client Pip Freeze Environments
     runs-on: ubuntu-latest
     if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'pip-freeze' || github.event_name == 'schedule'
-    
+
     steps:
       - uses: actions/checkout@v4
 
@@ -96,7 +97,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: '3.9'
-          cache: 'pip'
 
       - name: Install system dependencies
         run: |
@@ -114,12 +114,13 @@ jobs:
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
+          PIP_NO_CACHE_DIR: 1
 
   min-versions-only:
     name: Test Minimum Dependency Versions
     runs-on: ubuntu-latest
     if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'min-versions'
-    
+
     steps:
       - uses: actions/checkout@v4
 
@@ -127,7 +128,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: '3.9'
-          cache: 'pip'
 
       - name: Install system dependencies
         run: |
@@ -145,12 +145,13 @@ jobs:
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
+          PIP_NO_CACHE_DIR: 1
 
   max-versions-only:
-    name: Test Maximum Dependency Versions  
+    name: Test Maximum Dependency Versions
     runs-on: ubuntu-latest
     if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'max-versions'
-    
+
     steps:
       - uses: actions/checkout@v4
 
@@ -158,7 +159,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: '3.12'
-          cache: 'pip'
 
       - name: Install system dependencies
         run: |
@@ -176,12 +176,13 @@ jobs:
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
+          PIP_NO_CACHE_DIR: 1
 
   python-versions-only:
     name: Test All Python Versions with Default Dependencies
     runs-on: ubuntu-latest
     if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'python-versions'
-    
+
     steps:
       - uses: actions/checkout@v4
 
@@ -189,7 +190,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: '3.9'
-          cache: 'pip'
 
       - name: Install system dependencies
         run: |
@@ -207,22 +207,23 @@ jobs:
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
+          PIP_NO_CACHE_DIR: 1
 
   report-status:
     name: Report Dependency Testing Status
     runs-on: ubuntu-latest
     needs: [dependency-matrix]
     if: always()
-    
+
     steps:
       - name: Report results
         run: |
           echo "Dependency testing completed"
           echo "Matrix job status: ${{ needs.dependency-matrix.result }}"
-          
+
           if [[ "${{ needs.dependency-matrix.result }}" == "failure" ]]; then
             echo "❌ Some dependency combinations failed"
             exit 1
           else
             echo "✅ All dependency combinations passed"
-          fi 
\ No newline at end of file
+          fi
diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 221b989e7..6d9cba067 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -84,7 +84,7 @@ jobs:
         run: |
           poetry run tox -e py39-min
 
-      - name: Test Latest Dependencies (Python 3.9)  
+      - name: Test Latest Dependencies (Python 3.9)
         run: |
           poetry run tox -e py39-max
 

From 394ce682ddeeb2dcaa5847568854c932177c6f7b Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 14:22:45 -0400
Subject: [PATCH 16/47] chore: update tox configuration for minimum dependency
 testing across Python versions and add constraint files

---
 .github/workflows/dependency-testing.yaml |  6 ++--
 requirements/constraints/py310-min.txt    | 14 ++++++++
 requirements/constraints/py311-min.txt    | 14 ++++++++
 requirements/constraints/py312-min.txt    | 17 ++++++++++
 requirements/constraints/py39-min.txt     | 14 ++++++++
 tox.ini                                   | 41 ++++++++++++++++++-----
 6 files changed, 95 insertions(+), 11 deletions(-)
 create mode 100644 requirements/constraints/py310-min.txt
 create mode 100644 requirements/constraints/py311-min.txt
 create mode 100644 requirements/constraints/py312-min.txt
 create mode 100644 requirements/constraints/py39-min.txt

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 56bb4ab16..4f6b7a56b 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -141,7 +141,7 @@ jobs:
 
       - name: Test minimum versions
         run: |
-          make test-deps-min
+          tox -e py39-min
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
@@ -172,7 +172,7 @@ jobs:
 
       - name: Test maximum versions
         run: |
-          make test-deps-max
+          tox -e py39-max,py310-max,py311-max,py312-max
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
@@ -203,7 +203,7 @@ jobs:
 
       - name: Test all Python versions
         run: |
-          make test-python-versions
+          tox -e py39,py310,py311,py312
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
diff --git a/requirements/constraints/py310-min.txt b/requirements/constraints/py310-min.txt
new file mode 100644
index 000000000..3ba332a76
--- /dev/null
+++ b/requirements/constraints/py310-min.txt
@@ -0,0 +1,14 @@
+# Minimum compatible pins for Python 3.10
+pandas==2.0.3
+numpy==1.26.4
+scikit-learn==1.1.0
+matplotlib==3.5.0
+transformers==4.32.0
+datasets==2.10.0
+nltk==3.8.1
+plotly==5.0.0
+scipy==1.9.0
+statsmodels==0.13.0
+xgboost==1.5.2
+torch==2.0.1
+sentencepiece==0.2.0
diff --git a/requirements/constraints/py311-min.txt b/requirements/constraints/py311-min.txt
new file mode 100644
index 000000000..f6d3f53b9
--- /dev/null
+++ b/requirements/constraints/py311-min.txt
@@ -0,0 +1,14 @@
+# Minimum compatible pins for Python 3.11
+pandas==2.0.3
+numpy==1.26.4
+scikit-learn==1.1.0
+matplotlib==3.5.0
+transformers==4.32.0
+datasets==2.10.0
+nltk==3.8.1
+plotly==5.0.0
+scipy==1.9.0
+statsmodels==0.13.0
+xgboost==1.5.2
+torch==2.1.0
+sentencepiece==0.2.0
diff --git a/requirements/constraints/py312-min.txt b/requirements/constraints/py312-min.txt
new file mode 100644
index 000000000..81d02730c
--- /dev/null
+++ b/requirements/constraints/py312-min.txt
@@ -0,0 +1,17 @@
+# Minimum compatible pins for Python 3.12
+# Avoid packages that don't yet have Py3.12 wheels at older pins.
+# Use the oldest working set known to pass for 3.12.
+pandas==2.1.4
+numpy==1.26.4
+scikit-learn==1.3.0
+matplotlib==3.8.0
+transformers==4.35.0
+datasets==2.14.0
+nltk==3.8.1
+plotly==5.18.0
+scipy==1.11.3
+statsmodels==0.14.0
+xgboost==2.0.3
+# torch min for 3.12 typically 2.2+
+torch==2.2.2
+sentencepiece==0.2.0
diff --git a/requirements/constraints/py39-min.txt b/requirements/constraints/py39-min.txt
new file mode 100644
index 000000000..eac1bc1c5
--- /dev/null
+++ b/requirements/constraints/py39-min.txt
@@ -0,0 +1,14 @@
+# Minimum compatible pins for Python 3.9
+pandas==2.0.3
+numpy==1.26.4
+scikit-learn==1.1.0
+matplotlib==3.5.0
+transformers==4.32.0
+datasets==2.10.0
+nltk==3.8.1
+plotly==5.0.0
+scipy==1.9.0
+statsmodels==0.13.0
+xgboost==1.5.2
+torch==2.0.1
+sentencepiece==0.2.0
diff --git a/tox.ini b/tox.ini
index 66a35eaab..e8a0be5ee 100644
--- a/tox.ini
+++ b/tox.ini
@@ -27,17 +27,41 @@ commands =
     poetry run python -m unittest discover tests
     poetry run python -m unittest tests.test_unit_tests
 
-[testenv:py{39,310,311,312}-min]
-description = Test with minimum supported dependency versions
+[testenv:py39-min]
+description = Test with minimum supported dependency versions (Python 3.9)
 commands_pre =
     python -m pip install --upgrade pip
     pip install poetry
     poetry config virtualenvs.create false
-    poetry install --all-extras
-    # Install minimum versions of key dependencies
-    pip install pandas==2.0.3 numpy==1.26.0 scikit-learn==1.1.0 matplotlib==3.5.0
-    pip install transformers==4.32.0 torch==2.4.0 datasets==2.10.0 xgboost==1.5.2
-    pip install nltk==3.8.1 plotly==5.0.0 scipy==1.9.0 statsmodels==0.13.0
+    poetry install
+    pip install -r requirements/constraints/py39-min.txt
+
+[testenv:py310-min]
+description = Test with minimum supported dependency versions (Python 3.10)
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install
+    pip install -r requirements/constraints/py310-min.txt
+
+[testenv:py311-min]
+description = Test with minimum supported dependency versions (Python 3.11)
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install
+    pip install -r requirements/constraints/py311-min.txt
+
+[testenv:py312-min]
+description = Test with minimum supported dependency versions (Python 3.12)
+commands_pre =
+    python -m pip install --upgrade pip
+    pip install poetry
+    poetry config virtualenvs.create false
+    poetry install
+    pip install -r requirements/constraints/py312-min.txt
 
 [testenv:py{39,310,311,312}-max]
 description = Test with latest dependency versions
@@ -47,7 +71,8 @@ commands_pre =
     poetry config virtualenvs.create false
     poetry install --all-extras
     # Upgrade to latest versions of key dependencies
-    pip install --upgrade pandas numpy scikit-learn matplotlib transformers
+    # Temporarily cap numpy to <2.0 to avoid ecosystem breakages in deps (numba, shap, etc.)
+    pip install --upgrade 'numpy<2.0' pandas scikit-learn matplotlib transformers
     pip install --upgrade torch datasets xgboost nltk plotly scipy statsmodels
 
 [testenv:docs]

From ca502120f1672232287b8ed48247927966d679f1 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 14:24:45 -0400
Subject: [PATCH 17/47] chore: remove deprecated dependency testing jobs for
 minimum, maximum, and all Python versions

---
 .github/workflows/dependency-testing.yaml | 93 -----------------------
 1 file changed, 93 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 4f6b7a56b..9484093d0 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -116,99 +116,6 @@ jobs:
           MKL_NUM_THREADS: 1
           PIP_NO_CACHE_DIR: 1
 
-  min-versions-only:
-    name: Test Minimum Dependency Versions
-    runs-on: ubuntu-latest
-    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'min-versions'
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python 3.9
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.9'
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential libomp-dev
-
-      - name: Install tox
-        run: |
-          python -m pip install --upgrade pip
-          pip install tox
-
-      - name: Test minimum versions
-        run: |
-          tox -e py39-min
-        env:
-          OMP_NUM_THREADS: 1
-          MKL_NUM_THREADS: 1
-          PIP_NO_CACHE_DIR: 1
-
-  max-versions-only:
-    name: Test Maximum Dependency Versions
-    runs-on: ubuntu-latest
-    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'max-versions'
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python 3.12
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential libomp-dev
-
-      - name: Install tox
-        run: |
-          python -m pip install --upgrade pip
-          pip install tox
-
-      - name: Test maximum versions
-        run: |
-          tox -e py39-max,py310-max,py311-max,py312-max
-        env:
-          OMP_NUM_THREADS: 1
-          MKL_NUM_THREADS: 1
-          PIP_NO_CACHE_DIR: 1
-
-  python-versions-only:
-    name: Test All Python Versions with Default Dependencies
-    runs-on: ubuntu-latest
-    if: github.event_name == 'workflow_dispatch' && github.event.inputs.test_type == 'python-versions'
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python 3.9
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.9'
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential libomp-dev
-
-      - name: Install tox
-        run: |
-          python -m pip install --upgrade pip
-          pip install tox
-
-      - name: Test all Python versions
-        run: |
-          tox -e py39,py310,py311,py312
-        env:
-          OMP_NUM_THREADS: 1
-          MKL_NUM_THREADS: 1
-          PIP_NO_CACHE_DIR: 1
-
   report-status:
     name: Report Dependency Testing Status
     runs-on: ubuntu-latest

From 2d23af06ca71d867b1137ba4ead7915055a08d59 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 14:26:12 -0400
Subject: [PATCH 18/47] chore: update dependencies in poetry.lock to latest
 versions for aiohttp, triton, and virtualenv

---
 poetry.lock | 2558 ++++++++++++++++++++++++++-------------------------
 1 file changed, 1290 insertions(+), 1268 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index ff6e5392e..82181efea 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -30,98 +30,98 @@ markers = {dev = "python_version == \"3.12\""}
 
 [[package]]
 name = "aiohttp"
-version = "3.12.14"
+version = "3.12.15"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "dev"]
 files = [
-    {file = "aiohttp-3.12.14-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:906d5075b5ba0dd1c66fcaaf60eb09926a9fef3ca92d912d2a0bbdbecf8b1248"},
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 markers = {dev = "python_version == \"3.12\""}
 
@@ -196,14 +196,14 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.9.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
+version = "4.10.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "dev"]
 files = [
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-    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+    {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"},
+    {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"},
 ]
 
 [package.dependencies]
@@ -213,8 +213,6 @@ sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -326,41 +324,42 @@ argon2-cffi-bindings = "*"
 
 [[package]]
 name = "argon2-cffi-bindings"
-version = "21.2.0"
+version = "25.1.0"
 description = "Low-level CFFI bindings for Argon2"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.25"
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
 docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
-mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.15.0)", "types-Pillow"]
+mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.17.0)", "types-Pillow"]
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
@@ -1340,50 +1309,50 @@ test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "cryptography"
-version = "45.0.5"
+version = "45.0.6"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
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 ]
 
 [package.dependencies]
@@ -1396,7 +1365,7 @@ nox = ["nox (>=2024.4.15)", "nox[uv] (>=2024.3.2) ; python_full_version >= \"3.8
 pep8test = ["check-sdist ; python_full_version >= \"3.8.0\"", "click (>=8.0.1)", "mypy (>=1.4)", "ruff (>=0.3.6)"]
 sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi (>=2024)", "cryptography-vectors (==45.0.5)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
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 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1531,38 +1500,38 @@ vision = ["Pillow (>=9.4.0)"]
 
 [[package]]
 name = "debugpy"
-version = "1.8.15"
+version = "1.8.16"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
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+docs = ["pydoctor (>=25.4.0)"]
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 [[package]]
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 version = "0.18.1"
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 [package.extras]
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-test = ["objgraph", "psutil"]
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+    {file = "hf_xet-1.1.7-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:713f2bff61b252f8523739969f247aa354ad8e6d869b8281e174e2ea1bb8d604"},
+    {file = "hf_xet-1.1.7-cp37-abi3-win_amd64.whl", hash = "sha256:2e356da7d284479ae0f1dea3cf5a2f74fdf925d6dca84ac4341930d892c7cb34"},
+    {file = "hf_xet-1.1.7.tar.gz", hash = "sha256:20cec8db4561338824a3b5f8c19774055b04a8df7fff0cb1ff2cb1a0c1607b80"},
 ]
 
 [package.extras]
@@ -2228,20 +2202,20 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.33.4"
+version = "0.34.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 groups = ["main"]
 files = [
-    {file = "huggingface_hub-0.33.4-py3-none-any.whl", hash = "sha256:09f9f4e7ca62547c70f8b82767eefadd2667f4e116acba2e3e62a5a81815a7bb"},
-    {file = "huggingface_hub-0.33.4.tar.gz", hash = "sha256:6af13478deae120e765bfd92adad0ae1aec1ad8c439b46f23058ad5956cbca0a"},
+    {file = "huggingface_hub-0.34.4-py3-none-any.whl", hash = "sha256:9b365d781739c93ff90c359844221beef048403f1bc1f1c123c191257c3c890a"},
+    {file = "huggingface_hub-0.34.4.tar.gz", hash = "sha256:a4228daa6fb001be3f4f4bdaf9a0db00e1739235702848df00885c9b5742c85c"},
 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = ">=2023.5.0"
-hf-xet = {version = ">=1.1.2,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
+hf-xet = {version = ">=1.1.3,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -2249,16 +2223,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
 hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
 inference = ["aiohttp"]
 mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
 oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
-quality = ["libcst (==1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)"]
+quality = ["libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
 testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
@@ -2313,11 +2287,12 @@ version = "8.7.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.9"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"},
     {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"},
 ]
+markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.10\""}
 
 [package.dependencies]
 zipp = ">=3.20"
@@ -2357,37 +2332,37 @@ type = ["pytest-mypy"]
 
 [[package]]
 name = "ipykernel"
-version = "6.29.5"
+version = "6.30.1"
 description = "IPython Kernel for Jupyter"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
-    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
+    {file = "ipykernel-6.30.1-py3-none-any.whl", hash = "sha256:aa6b9fb93dca949069d8b85b6c79b2518e32ac583ae9c7d37c51d119e18b3fb4"},
+    {file = "ipykernel-6.30.1.tar.gz", hash = "sha256:6abb270161896402e76b91394fcdce5d1be5d45f456671e5080572f8505be39b"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+appnope = {version = ">=0.1.2", markers = "platform_system == \"Darwin\""}
 comm = ">=0.1.1"
 debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
-jupyter-client = ">=6.1.12"
+jupyter-client = ">=8.0.0"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 matplotlib-inline = ">=0.1"
-nest-asyncio = "*"
-packaging = "*"
-psutil = "*"
-pyzmq = ">=24"
-tornado = ">=6.1"
+nest-asyncio = ">=1.4"
+packaging = ">=22"
+psutil = ">=5.7"
+pyzmq = ">=25"
+tornado = ">=6.2"
 traitlets = ">=5.4.0"
 
 [package.extras]
-cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+cov = ["coverage[toml]", "matplotlib", "pytest-cov", "trio"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0,<9)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
@@ -2954,19 +2929,19 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.5"
+version = "2.2.6"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
-    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
+    {file = "jupyter_lsp-2.2.6-py3-none-any.whl", hash = "sha256:283783752bf0b459ee7fa88effa72104d87dd343b82d5c06cf113ef755b15b6d"},
+    {file = "jupyter_lsp-2.2.6.tar.gz", hash = "sha256:0566bd9bb04fd9e6774a937ed01522b555ba78be37bebef787c8ab22de4c0361"},
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
-jupyter-server = ">=1.1.2"
+importlib_metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter_server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
@@ -3027,14 +3002,14 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.4.4"
+version = "4.4.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "jupyterlab-4.4.4-py3-none-any.whl", hash = "sha256:711611e4f59851152eb93316c3547c3ec6291f16bb455f1f4fa380d25637e0dd"},
-    {file = "jupyterlab-4.4.4.tar.gz", hash = "sha256:163fee1ef702e0a057f75d2eed3ed1da8a986d59eb002cbeb6f0c2779e6cd153"},
+    {file = "jupyterlab-4.4.5-py3-none-any.whl", hash = "sha256:e76244cceb2d1fb4a99341f3edc866f2a13a9e14c50368d730d75d8017be0863"},
+    {file = "jupyterlab-4.4.5.tar.gz", hash = "sha256:0bd6c18e6a3c3d91388af6540afa3d0bb0b2e76287a7b88ddf20ab41b336e595"},
 ]
 
 [package.dependencies]
@@ -3378,21 +3353,21 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.3.26"
+version = "0.3.27"
 description = "Building applications with LLMs through composability"
 optional = true
-python-versions = ">=3.9"
+python-versions = "<4.0,>=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
-    {file = "langchain-0.3.26-py3-none-any.whl", hash = "sha256:361bb2e61371024a8c473da9f9c55f4ee50f269c5ab43afdb2b1309cb7ac36cf"},
-    {file = "langchain-0.3.26.tar.gz", hash = "sha256:8ff034ee0556d3e45eff1f1e96d0d745ced57858414dba7171c8ebdbeb5580c9"},
+    {file = "langchain-0.3.27-py3-none-any.whl", hash = "sha256:7b20c4f338826acb148d885b20a73a16e410ede9ee4f19bb02011852d5f98798"},
+    {file = "langchain-0.3.27.tar.gz", hash = "sha256:aa6f1e6274ff055d0fd36254176770f356ed0a8994297d1df47df341953cec62"},
 ]
 
 [package.dependencies]
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
-langchain-core = ">=0.3.66,<1.0.0"
-langchain-text-splitters = ">=0.3.8,<1.0.0"
+langchain-core = ">=0.3.72,<1.0.0"
+langchain-text-splitters = ">=0.3.9,<1.0.0"
 langsmith = ">=0.1.17"
 pydantic = ">=2.7.4,<3.0.0"
 PyYAML = ">=5.3"
@@ -3447,15 +3422,15 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.69"
+version = "0.3.74"
 description = "Building applications with LLMs through composability"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
-    {file = "langchain_core-0.3.69-py3-none-any.whl", hash = "sha256:383e9cb4919f7ef4b24bf8552ef42e4323c064924fea88b28dd5d7ddb740d3b8"},
-    {file = "langchain_core-0.3.69.tar.gz", hash = "sha256:c132961117cc7f0227a4c58dd3e209674a6dd5b7e74abc61a0df93b0d736e283"},
+    {file = "langchain_core-0.3.74-py3-none-any.whl", hash = "sha256:088338b5bc2f6a66892f9afc777992c24ee3188f41cbc603d09181e34a228ce7"},
+    {file = "langchain_core-0.3.74.tar.gz", hash = "sha256:ff604441aeade942fbcc0a3860a592daba7671345230c2078ba2eb5f82b6ba76"},
 ]
 
 [package.dependencies]
@@ -3469,37 +3444,37 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-openai"
-version = "0.3.28"
+version = "0.3.29"
 description = "An integration package connecting OpenAI and LangChain"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
-    {file = "langchain_openai-0.3.28-py3-none-any.whl", hash = "sha256:4cd6d80a5b2ae471a168017bc01b2e0f01548328d83532400a001623624ede67"},
-    {file = "langchain_openai-0.3.28.tar.gz", hash = "sha256:6c669548dbdea325c034ae5ef699710e2abd054c7354fdb3ef7bf909dc739d9e"},
+    {file = "langchain_openai-0.3.29-py3-none-any.whl", hash = "sha256:71ae6791b3e017ec892a8062f993edc882c6665fd8385aa66e9dc3bff8205996"},
+    {file = "langchain_openai-0.3.29.tar.gz", hash = "sha256:83a0455f8ce874aa1806131ca3b4db08e482be037b7457a9b3ca21a213d2ab47"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.68,<1.0.0"
+langchain-core = ">=0.3.74,<1.0.0"
 openai = ">=1.86.0,<2.0.0"
 tiktoken = ">=0.7,<1"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.8"
+version = "0.3.9"
 description = "LangChain text splitting utilities"
 optional = true
-python-versions = "<4.0,>=3.9"
+python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
-    {file = "langchain_text_splitters-0.3.8-py3-none-any.whl", hash = "sha256:e75cc0f4ae58dcf07d9f18776400cf8ade27fadd4ff6d264df6278bb302f6f02"},
-    {file = "langchain_text_splitters-0.3.8.tar.gz", hash = "sha256:116d4b9f2a22dda357d0b79e30acf005c5518177971c66a9f1ab0edfdb0f912e"},
+    {file = "langchain_text_splitters-0.3.9-py3-none-any.whl", hash = "sha256:cee0bb816211584ea79cc79927317c358543f40404bcfdd69e69ba3ccde54401"},
+    {file = "langchain_text_splitters-0.3.9.tar.gz", hash = "sha256:7cd1e5a3aaf609979583eeca2eb34177622570b8fa8f586a605c6b1c34e7ebdb"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.51,<1.0.0"
+langchain-core = ">=0.3.72,<1.0.0"
 
 [[package]]
 name = "langdetect"
@@ -3518,31 +3493,32 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.4.7"
+version = "0.4.13"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"all\" or extra == \"llm\""
 files = [
-    {file = "langsmith-0.4.7-py3-none-any.whl", hash = "sha256:de91f1abdd65da369996f8eedb5201f442110c9c3bde5babc6f5300f07da65df"},
-    {file = "langsmith-0.4.7.tar.gz", hash = "sha256:3864cf29295c2565c578e93d1533f5b39e2b4af616545ace30f069635a319890"},
+    {file = "langsmith-0.4.13-py3-none-any.whl", hash = "sha256:dab7b16ee16986995007bf5a777f45c18f8bf7453f67ae2ebcb46ce43c214297"},
+    {file = "langsmith-0.4.13.tar.gz", hash = "sha256:1ae7dbb5d8150647406f49885a2dd16ab12bd990254b5dc23718838b3d086fde"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
-orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
+orjson = {version = ">=3.9.14", markers = "platform_python_implementation != \"PyPy\""}
 packaging = ">=23.2"
 pydantic = ">=1,<3"
-requests = ">=2,<3"
-requests-toolbelt = ">=1.0.0,<2.0.0"
-zstandard = ">=0.23.0,<0.24.0"
+requests = ">=2.0.0"
+requests-toolbelt = ">=1.0.0"
+zstandard = ">=0.23.0"
 
 [package.extras]
-langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
-openai-agents = ["openai-agents (>=0.0.3,<0.1)"]
-otel = ["opentelemetry-api (>=1.30.0,<2.0.0)", "opentelemetry-exporter-otlp-proto-http (>=1.30.0,<2.0.0)", "opentelemetry-sdk (>=1.30.0,<2.0.0)"]
-pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2)"]
+openai-agents = ["openai-agents (>=0.0.3)"]
+otel = ["opentelemetry-api (>=1.30.0)", "opentelemetry-exporter-otlp-proto-http (>=1.30.0)", "opentelemetry-sdk (>=1.30.0)"]
+pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4)", "vcrpy (>=7.0.0)"]
+vcr = ["vcrpy (>=7.0.0)"]
 
 [[package]]
 name = "lark"
@@ -3824,47 +3800,68 @@ dev = ["meson-python (>=0.13.1,<0.17.0)", "numpy (>=1.25)", "pybind11 (>=2.6,!=2
 
 [[package]]
 name = "matplotlib"
-version = "3.10.3"
+version = "3.10.5"
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 [package.dependencies]
@@ -4146,14 +4143,14 @@ markers = {main = "extra == \"all\" or extra == \"llm\""}
 
 [[package]]
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 ]
 
 [package.extras]
@@ -4382,19 +4379,19 @@ files = [
 
 [[package]]
 name = "notebook"
-version = "7.4.4"
+version = "7.4.5"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
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-    {file = "notebook-7.4.4.tar.gz", hash = "sha256:392fd501e266f2fb3466c6fcd3331163a2184968cb5c5accf90292e01dfe528c"},
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 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.4.4,<4.5"
+jupyterlab = ">=4.4.5,<4.5"
 jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
@@ -4618,76 +4615,72 @@ files = [
 
 [[package]]
 name = "nvidia-cublas-cu12"
-version = "12.6.4.1"
+version = "12.8.4.1"
 description = "CUBLAS native runtime libraries"
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 python-versions = ">=3"
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 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 ]
 
 [[package]]
 name = "nvidia-cuda-cupti-cu12"
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+version = "12.8.90"
 description = "CUDA profiling tools runtime libs."
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 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 ]
 
 [[package]]
 name = "nvidia-cuda-nvrtc-cu12"
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+version = "12.8.93"
 description = "NVRTC native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 ]
 
 [[package]]
 name = "nvidia-cuda-runtime-cu12"
-version = "12.6.77"
+version = "12.8.90"
 description = "CUDA Runtime native Libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 ]
 
 [[package]]
 name = "nvidia-cudnn-cu12"
-version = "9.5.1.17"
+version = "9.10.2.21"
 description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8"},
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8"},
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-win_amd64.whl", hash = "sha256:c6288de7d63e6cf62988f0923f96dc339cea362decb1bf5b3141883392a7d65e"},
 ]
 
 [package.dependencies]
@@ -4695,18 +4688,16 @@ nvidia-cublas-cu12 = "*"
 
 [[package]]
 name = "nvidia-cufft-cu12"
-version = "11.3.0.4"
+version = "11.3.3.83"
 description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a"},
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 ]
 
 [package.dependencies]
@@ -4714,47 +4705,43 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-cufile-cu12"
-version = "1.11.1.6"
+version = "1.13.1.3"
 description = "cuFile GPUDirect libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
 
 [[package]]
 name = "nvidia-curand-cu12"
-version = "10.3.7.77"
+version = "10.3.9.90"
 description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
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 ]
 
 [[package]]
 name = "nvidia-cusolver-cu12"
-version = "11.7.1.2"
+version = "11.7.3.90"
 description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0"},
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450"},
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 ]
 
 [package.dependencies]
@@ -4764,18 +4751,16 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-cusparse-cu12"
-version = "12.5.4.2"
+version = "12.5.8.93"
 description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
 
 [package.dependencies]
@@ -4783,71 +4768,69 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
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-version = "0.6.3"
+version = "0.7.1"
 description = "NVIDIA cuSPARSELt"
 optional = false
 python-versions = "*"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.26.2"
+version = "2.27.3"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\" and (platform_machine == \"x86_64\" or extra == \"all\" or extra == \"xgboost\")"
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 ]
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.6.85"
+version = "12.8.93"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
 
 [[package]]
 name = "nvidia-nvtx-cu12"
-version = "12.6.77"
+version = "12.8.90"
 description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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 ]
 
 [[package]]
 name = "openai"
-version = "1.97.0"
+version = "1.99.5"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -4868,84 +4851,95 @@ voice-helpers = ["numpy (>=2.0.2)", "sounddevice (>=0.5.1)"]
 
 [[package]]
 name = "orjson"
-version = "3.11.0"
+version = "3.11.1"
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 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 [package.dependencies]
@@ -7728,70 +7747,70 @@ test = ["pytest"]
 
 [[package]]
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 description = "Database Abstraction Library"
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 ]
 
 [package.dependencies]
@@ -8181,28 +8200,29 @@ filelock = "*"
 fsspec = "*"
 jinja2 = "*"
 networkx = "*"
-nvidia-cublas-cu12 = {version = "12.6.4.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu12 = {version = "12.6.80", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu12 = {version = "9.5.1.17", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufft-cu12 = {version = "11.3.0.4", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufile-cu12 = {version = "1.11.1.6", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu12 = {version = "10.3.7.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu12 = {version = "11.7.1.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu12 = {version = "12.5.4.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparselt-cu12 = {version = "0.6.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.26.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvjitlink-cu12 = {version = "12.6.85", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cublas-cu12 = {version = "12.8.4.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "9.10.2.21", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.3.3.83", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufile-cu12 = {version = "1.13.1.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.9.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.7.3.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.5.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparselt-cu12 = {version = "0.7.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.27.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvjitlink-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 setuptools = {version = "*", markers = "python_version >= \"3.12\""}
 sympy = ">=1.13.3"
-triton = {version = "3.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+triton = {version = "3.4.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 typing-extensions = ">=4.10.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.13.0)"]
+pyyaml = ["pyyaml"]
 
 [[package]]
 name = "tornado"
@@ -8228,14 +8248,14 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.28.3"
+version = "4.28.4"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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-    {file = "tox-4.28.3.tar.gz", hash = "sha256:b91db7219e5242002cf4040a299c8852026d6af35fcd21274d456fb62dafee7b"},
+    {file = "tox-4.28.4-py3-none-any.whl", hash = "sha256:8d4ad9ee916ebbb59272bb045e154a10fa12e3bbdcf94cc5185cbdaf9b241f99"},
+    {file = "tox-4.28.4.tar.gz", hash = "sha256:b5b14c6307bd8994ff1eba5074275826620325ee1a4f61316959d562bfd70b9d"},
 ]
 
 [package.dependencies]
@@ -8291,19 +8311,19 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.53.2"
+version = "4.55.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.9.0"
 groups = ["main"]
 files = [
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+    {file = "transformers-4.55.0-py3-none-any.whl", hash = "sha256:29d9b8800e32a4a831bb16efb5f762f6a9742fef9fce5d693ed018d19b106490"},
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 ]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.30.0,<1.0"
+huggingface-hub = ">=0.34.0,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
@@ -8315,22 +8335,23 @@ tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.26.0)"]
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 codecarbon = ["codecarbon (>=2.8.1)"]
 deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
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-dev-tensorflow = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<0.7)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "optuna", "parameterized (>=0.9)", "protobuf", "psutil", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<=0.9)", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<=0.9)", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
-hf-xet = ["hf_xet"]
-hub-kernels = ["kernels (>=0.6.1,<0.7)"]
-integrations = ["kernels (>=0.6.1,<0.7)", "optuna", "ray[tune] (>=2.7.0)", "sigopt"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)"]
+hf-xet = ["hf-xet"]
+hub-kernels = ["kernels (>=0.6.1,<=0.9)"]
+integrations = ["kernels (>=0.6.1,<=0.9)", "optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+mistral-common = ["mistral-common[opencv] (>=1.6.3)"]
 modelcreation = ["cookiecutter (==1.7.3)"]
 natten = ["natten (>=0.14.6,<0.15.0)"]
 num2words = ["num2words"]
@@ -8338,52 +8359,53 @@ onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 open-telemetry = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (>=2.15.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
-retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+retrieval = ["datasets (>=2.15.0)", "faiss-cpu"]
 ruff = ["ruff (==0.11.2)"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+serving = ["accelerate (>=0.26.0)", "fastapi", "openai (>=1.98.0)", "pydantic (>=2)", "starlette", "torch (>=2.1)", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "parameterized (>=0.9)", "psutil", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 tiktoken = ["blobfile", "tiktoken"]
-timm = ["timm (<=1.0.11)"]
+timm = ["timm (!=1.0.18,<=1.0.19)"]
 tokenizers = ["tokenizers (>=0.21,<0.22)"]
 torch = ["accelerate (>=0.26.0)", "torch (>=2.1)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.30.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "tqdm (>=4.27)"]
 video = ["av"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "3.3.0"
+version = "3.4.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
-python-versions = "*"
+python-versions = "<3.14,>=3.9"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "triton-3.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fad99beafc860501d7fcc1fb7045d9496cbe2c882b1674640304949165a916e7"},
-    {file = "triton-3.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3161a2bf073d6b22c4e2f33f951f3e5e3001462b2570e6df9cd57565bdec2984"},
-    {file = "triton-3.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b68c778f6c4218403a6bd01be7484f6dc9e20fe2083d22dd8aef33e3b87a10a3"},
-    {file = "triton-3.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47bc87ad66fa4ef17968299acacecaab71ce40a238890acc6ad197c3abe2b8f1"},
-    {file = "triton-3.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce4700fc14032af1e049005ae94ba908e71cd6c2df682239aed08e49bc71b742"},
-    {file = "triton-3.3.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1f41403bfa0cbb3e24fd958ca7fee04e9681e55e539296db9aca30c42acae693"},
+    {file = "triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7ff2785de9bc02f500e085420273bb5cc9c9bb767584a4aa28d6e360cec70128"},
+    {file = "triton-3.4.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7b70f5e6a41e52e48cfc087436c8a28c17ff98db369447bcaff3b887a3ab4467"},
+    {file = "triton-3.4.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31c1d84a5c0ec2c0f8e8a072d7fd150cab84a9c239eaddc6706c081bfae4eb04"},
+    {file = "triton-3.4.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00be2964616f4c619193cb0d1b29a99bd4b001d7dc333816073f92cf2a8ccdeb"},
+    {file = "triton-3.4.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7936b18a3499ed62059414d7df563e6c163c5e16c3773678a3ee3d417865035d"},
+    {file = "triton-3.4.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:98e5c1442eaeabae2e2452ae765801bd53cd4ce873cab0d1bdd59a32ab2d9397"},
 ]
 
 [package.dependencies]
+importlib-metadata = {version = "*", markers = "python_version < \"3.10\""}
 setuptools = ">=40.8.0"
 
 [package.extras]
-build = ["cmake (>=3.20)", "lit"]
+build = ["cmake (>=3.20,<4.0)", "lit"]
 tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
@@ -8538,14 +8560,14 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.31.2"
+version = "20.33.1"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "virtualenv-20.31.2-py3-none-any.whl", hash = "sha256:36efd0d9650ee985f0cad72065001e66d49a6f24eb44d98980f630686243cf11"},
-    {file = "virtualenv-20.31.2.tar.gz", hash = "sha256:e10c0a9d02835e592521be48b332b6caee6887f332c111aa79a09b9e79efc2af"},
+    {file = "virtualenv-20.33.1-py3-none-any.whl", hash = "sha256:07c19bc66c11acab6a5958b815cbcee30891cd1c2ccf53785a28651a0d8d8a67"},
+    {file = "virtualenv-20.33.1.tar.gz", hash = "sha256:1b44478d9e261b3fb8baa5e74a0ca3bc0e05f21aa36167bf9cbf850e542765b8"},
 ]
 
 [package.dependencies]

From f9b4152b62c7c81bfa8349d7cf0db2d4517fcea5 Mon Sep 17 00:00:00 2001
From: Andres Rodriguez <andres@validmind.ai>
Date: Wed, 6 Aug 2025 07:53:57 -0700
Subject: [PATCH 19/47] Install kaleido

---
 .github/workflows/python.yaml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 6d9cba067..ac53a2cbd 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -5,7 +5,7 @@ name: Code Quality and Unit Testing
 
 on:
   push:
-    branches: [main]
+    branches: ['*']
     paths-ignore:
       - 'docs/**'
       - 'notebooks/**'
@@ -64,6 +64,7 @@ jobs:
           poetry install --all-extras
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
+          poetry run pip install -U kaleido
 
       - name: Ensure notebooks have no printed outputs
         run: make ensure-clean-notebooks

From 44d8ebc3f4c28e8e081fa1afdfe72a28cdc2ba20 Mon Sep 17 00:00:00 2001
From: Andres Rodriguez <andres@validmind.ai>
Date: Wed, 6 Aug 2025 08:31:41 -0700
Subject: [PATCH 20/47] Run integration tests

---
 .github/workflows/integration.yaml | 1 +
 .github/workflows/python.yaml      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/integration.yaml b/.github/workflows/integration.yaml
index def6dfbc1..daffd87f8 100644
--- a/.github/workflows/integration.yaml
+++ b/.github/workflows/integration.yaml
@@ -9,6 +9,7 @@ on:
       - main
       - prod
       - release-v1
+      - '*'
     paths-ignore:
       - 'docs/**'
 
diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index ac53a2cbd..d9538510f 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -5,7 +5,7 @@ name: Code Quality and Unit Testing
 
 on:
   push:
-    branches: ['*']
+    branches: [main]
     paths-ignore:
       - 'docs/**'
       - 'notebooks/**'

From 2fe6043f6afac6d430336400f457923eee6255f3 Mon Sep 17 00:00:00 2001
From: Andres Rodriguez <andres@validmind.ai>
Date: Wed, 6 Aug 2025 08:49:55 -0700
Subject: [PATCH 21/47] Fix branch name

---
 .github/workflows/integration.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/integration.yaml b/.github/workflows/integration.yaml
index daffd87f8..fd25f6f25 100644
--- a/.github/workflows/integration.yaml
+++ b/.github/workflows/integration.yaml
@@ -9,7 +9,7 @@ on:
       - main
       - prod
       - release-v1
-      - '*'
+      - cachafla/fix-tests
     paths-ignore:
       - 'docs/**'
 

From 965cde33a7e1c4c6993bed29554d5495402fb6df Mon Sep 17 00:00:00 2001
From: Andres Rodriguez <andres@validmind.ai>
Date: Wed, 6 Aug 2025 09:17:16 -0700
Subject: [PATCH 22/47] Missing change

---
 .github/workflows/integration.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/integration.yaml b/.github/workflows/integration.yaml
index fd25f6f25..c6c368dcf 100644
--- a/.github/workflows/integration.yaml
+++ b/.github/workflows/integration.yaml
@@ -40,6 +40,7 @@ jobs:
           poetry install -E huggingface -E llm
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
+          poetry run pip install -U kaleido
 
       - name: Install R Dependencies
         run: |

From 11e1ab18714bb78db5a3679dcf7b1c9a7d0f884e Mon Sep 17 00:00:00 2001
From: Andres Rodriguez <andres@validmind.ai>
Date: Wed, 6 Aug 2025 09:47:16 -0700
Subject: [PATCH 23/47] Update kaleido version

---
 .github/workflows/integration.yaml | 1 -
 .github/workflows/python.yaml      | 1 -
 poetry.lock                        | 4 ++--
 pyproject.toml                     | 2 +-
 4 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/integration.yaml b/.github/workflows/integration.yaml
index c6c368dcf..fd25f6f25 100644
--- a/.github/workflows/integration.yaml
+++ b/.github/workflows/integration.yaml
@@ -40,7 +40,6 @@ jobs:
           poetry install -E huggingface -E llm
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
-          poetry run pip install -U kaleido
 
       - name: Install R Dependencies
         run: |
diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index d9538510f..6d9cba067 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -64,7 +64,6 @@ jobs:
           poetry install --all-extras
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
-          poetry run pip install -U kaleido
 
       - name: Ensure notebooks have no printed outputs
         run: make ensure-clean-notebooks
diff --git a/poetry.lock b/poetry.lock
index 82181efea..7f5bba816 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -9090,5 +9090,5 @@ xgboost = ["xgboost"]
 
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.9.0,<3.13"
-content-hash = "20d2140290eba9e4d465d813dde25497a2aa06334bc847b2218e01c4cb6055e1"
+python-versions = ">=3.9.0,<3.12"
+content-hash = "195ff83105e9b2b1e966e9a32f3837ad592cfa9381eca23e241115017d7196c6"
diff --git a/pyproject.toml b/pyproject.toml
index 89ed68839..7bff239ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,7 @@ catboost = "*"
 datasets = "^2.10.0"
 evaluate = "*"
 ipywidgets = "*"
-kaleido = ">=0.2.1,!=0.2.1.post1"
+kaleido = ">=0.2.1,!=0.2.1.post1,<1.0.0"
 langchain-openai = { version = ">=0.1.8", optional = true }
 langdetect = "*"
 matplotlib = "*"

From 2e6c75cb2f2dc1ae58d0e02ce1ed6f1d5efa03b8 Mon Sep 17 00:00:00 2001
From: Andres Rodriguez <andres@validmind.ai>
Date: Wed, 6 Aug 2025 11:07:03 -0700
Subject: [PATCH 24/47] Undo branch change

---
 .github/workflows/integration.yaml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/integration.yaml b/.github/workflows/integration.yaml
index fd25f6f25..303a10f92 100644
--- a/.github/workflows/integration.yaml
+++ b/.github/workflows/integration.yaml
@@ -8,8 +8,6 @@ on:
     branches:
       - main
       - prod
-      - release-v1
-      - cachafla/fix-tests
     paths-ignore:
       - 'docs/**'
 

From 7669b66c8084dd320df7cedff99b214927dee70d Mon Sep 17 00:00:00 2001
From: Juan <juan@validmind.ai>
Date: Wed, 6 Aug 2025 13:47:46 +0200
Subject: [PATCH 25/47] Remove scorecard with GLM notebook

---
 .../application_scorecard_demo.ipynb          | 1169 -----------------
 1 file changed, 1169 deletions(-)
 delete mode 100644 notebooks/code_samples/credit_risk/application_scorecard_demo.ipynb

diff --git a/notebooks/code_samples/credit_risk/application_scorecard_demo.ipynb b/notebooks/code_samples/credit_risk/application_scorecard_demo.ipynb
deleted file mode 100644
index 1ab88d5e8..000000000
--- a/notebooks/code_samples/credit_risk/application_scorecard_demo.ipynb
+++ /dev/null
@@ -1,1169 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Document an application scorecard model\n",
-    "\n",
-    "Build and document an *application scorecard model* with the ValidMind Library by using Kaggle's [Lending Club](https://www.kaggle.com/datasets/devanshi23/loan-data-2007-2014/data) sample dataset to build a simple application scorecard.\n",
-    "\n",
-    "An application scorecard model is a type of statistical model used in credit scoring to evaluate the creditworthiness of potential borrowers by generating a score based on various characteristics of an applicant — such as credit history, income, employment status, and other relevant financial data. \n",
-    "\n",
-    "- This score helps lenders make decisions about whether to approve or reject loan applications, as well as determine the terms of the loan, including interest rates and credit limits. \n",
-    "- Application scorecard models enable lenders to manage risk efficiently while making the loan application process faster and more transparent for applicants.\n",
-    "\n",
-    "This interactive notebook provides a step-by-step guide for loading a demo dataset, preprocessing the raw data, training a model for testing, setting up test inputs, initializing the required ValidMind objects, running the test, and then logging the results to ValidMind."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Contents    \n",
-    "- [About ValidMind](#toc1_)    \n",
-    "  - [Before you begin](#toc1_1_)    \n",
-    "  - [New to ValidMind?](#toc1_2_)    \n",
-    "  - [Key concepts](#toc1_3_)    \n",
-    "- [Install the ValidMind Library](#toc2_)    \n",
-    "- [Initialize the ValidMind Library](#toc3_)    \n",
-    "  - [Get your code snippet](#toc3_1_)  \n",
-    "- [Initialize the Python environment](#toc4_)    \n",
-    "  - [Preview the documentation template](#toc4_1_)    \n",
-    "- [Load the sample dataset](#toc5_)    \n",
-    "  - [Prepocess the dataset](#toc5_1_)    \n",
-    "  - [Feature engineering](#toc5_2_)    \n",
-    "- [Train the model](#toc6_)    \n",
-    "  - [Compute probabilities](#toc6_1_)    \n",
-    "  - [Compute binary predictions](#toc6_2_)    \n",
-    "  - [Compute scores](#toc6_3_)    \n",
-    "- [Document the model](#toc7_)    \n",
-    "  - [Initialize the ValidMind datasets](#toc7_1_)    \n",
-    "  - [Initialize a model object](#toc7_2_)    \n",
-    "  - [Assign prediction values and probabilities to the datasets](#toc7_3_)    \n",
-    "  - [Assign scores to the datasets](#toc7_4_)    \n",
-    "  - [Data validation](#toc7_5_)    \n",
-    "    - [Run tests for raw data tests](#toc7_5_1_)    \n",
-    "    - [Run tests for preprocessed data](#toc7_5_2_)    \n",
-    "    - [Run tests for WoE analysis](#toc7_5_3_)    \n",
-    "  - [Model validation](#toc7_6_)    \n",
-    "    - [Run tests for model training](#toc7_6_1_)    \n",
-    "    - [Run tests for model evaluation](#toc7_6_2_)    \n",
-    "    - [Run tests for model explainability](#toc7_6_3_)    \n",
-    "- [Next steps](#toc8_)    \n",
-    "  - [Work with your model documentation](#toc8_1_)    \n",
-    "  - [Discover more learning resources](#toc8_2_)\n",
-    "- [Upgrade ValidMind](#toc9_)    \n",
-    "\n",
-    "<!-- vscode-jupyter-toc-config\n",
-    "\tnumbering=false\n",
-    "\tanchor=true\n",
-    "\tflat=false\n",
-    "\tminLevel=2\n",
-    "\tmaxLevel=4\n",
-    "\t/vscode-jupyter-toc-config -->\n",
-    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc1_'></a>\n",
-    "\n",
-    "## About ValidMind\n",
-    "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.\n",
-    "\n",
-    "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.\n",
-    "\n",
-    "<a id='toc1_1_'></a>\n",
-    "\n",
-    "### Before you begin\n",
-    "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.\n",
-    "\n",
-    "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html).\n",
-    "\n",
-    "<a id='toc1_2_'></a>\n",
-    "\n",
-    "### New to ValidMind?\n",
-    "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, you'll need access to a ValidMind account.</b></span>\n",
-    "<br></br>\n",
-    "<a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc1_3_'></a>\n",
-    "\n",
-    "### Key concepts\n",
-    "\n",
-    "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
-    "\n",
-    "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
-    "\n",
-    "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
-    "\n",
-    "**Custom tests**: Custom tests are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.\n",
-    "\n",
-    "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
-    "\n",
-    "- **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
-    "- **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
-    "- **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom test.\n",
-    "- **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom test. See this [example](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html) for more information.\n",
-    "\n",
-    "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.\n",
-    "\n",
-    "**Outputs**: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
-    "\n",
-    "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
-    "\n",
-    "Example: The [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc2_'></a>\n",
-    "\n",
-    "## Install the ValidMind Library\n",
-    "\n",
-    "To install the library:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q validmind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc3_'></a>\n",
-    "\n",
-    "## Initialize the ValidMind Library\n",
-    "\n",
-    "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.\n",
-    "\n",
-    "<a id='toc3_1_'></a>\n",
-    "\n",
-    "### Get your code snippet\n",
-    "\n",
-    "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
-    "\n",
-    "2. In the left sidebar, navigate to **Model Inventory** and click **+ Register Model**.\n",
-    "\n",
-    "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
-    "\n",
-    "   For example, to register a model for use with this notebook, select:\n",
-    "\n",
-    "   - Documentation template: `Credit Risk Scorecard`\n",
-    "   - Use case: `Credit Risk - CECL`\n",
-    "\n",
-    "   You can fill in other options according to your preference.\n",
-    "\n",
-    "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n",
-    "\n",
-    "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load your model identifier credentials from an `.env` file\n",
-    "\n",
-    "%load_ext dotenv\n",
-    "%dotenv .env\n",
-    "\n",
-    "# Or replace with your code snippet\n",
-    "\n",
-    "import validmind as vm\n",
-    "\n",
-    "vm.init(\n",
-    "    # api_host=\"...\",\n",
-    "    # api_key=\"...\",\n",
-    "    # api_secret=\"...\",\n",
-    "    # model=\"...\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc4_'></a>\n",
-    "\n",
-    "## Initialize the Python environment\n",
-    "\n",
-    "Next, let's import the necessary libraries and set up your Python environment for data analysis:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import statsmodels.api as sm\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc4_1_'></a>\n",
-    "\n",
-    "### Preview the documentation template\n",
-    "\n",
-    "A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.\n",
-    "\n",
-    "You'll upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the `vm.preview_template()` function from the ValidMind library and note the empty sections:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm.preview_template()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc5_'></a>\n",
-    "\n",
-    "## Load the sample dataset\n",
-    "\n",
-    "The sample dataset used here is provided by the ValidMind library. To be able to use it, you'll need to import the dataset and load it into a pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), a two-dimensional tabular data structure that makes use of rows and columns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the sample dataset from the library\n",
-    "\n",
-    "from validmind.datasets.credit_risk import lending_club\n",
-    "\n",
-    "df = lending_club.load_data(source=\"offline\")\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc5_1_'></a>\n",
-    "\n",
-    "### Prepocess the dataset\n",
-    "\n",
-    "In the preprocessing step we perform a number of operations to get ready for building our application scorecard. \n",
-    "\n",
-    "We use the `lending_club.preprocess` to simplify preprocessing. This function performs the following operations: \n",
-    "- Filters the dataset to include only loans for debt consolidation or credit card purposes\n",
-    "- Removes loans classified under the riskier grades \"F\" and \"G\"\n",
-    "- Excludes uncommon home ownership types and standardizes employment length and loan terms into numerical formats\n",
-    "- Discards unnecessary fields and any entries with missing information to maintain a clean and robust dataset for modeling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preprocess_df = lending_club.preprocess(df)\n",
-    "preprocess_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc5_2_'></a>\n",
-    "\n",
-    "### Feature engineering\n",
-    "\n",
-    "In the feature engineering phase, we apply specific transformations to optimize the dataset for predictive modeling in our application scorecard. \n",
-    "\n",
-    "Using the `ending_club.feature_engineering()` function, we conduct the following operations:\n",
-    "- **WoE encoding**: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model.\n",
-    "- **Integration of WoE bins**: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fe_df = lending_club.feature_engineering(preprocess_df)\n",
-    "fe_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_'></a>\n",
-    "\n",
-    "## Train the model\n",
-    "\n",
-    "In this section, we focus on constructing and refining our predictive model. \n",
-    "- We begin by dividing our data, which is based on Weight of Evidence (WoE) features, into training and testing sets (`train_df`, `test_df`). \n",
-    "- With `lending_club.split`, we employ a simple random split, randomly allocating data points to each set to ensure a mix of examples in both.\n",
-    "- Additionally, by setting `add_constant=True`, we include an intercept term in our model.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df, test_df = lending_club.split(fe_df, add_constant=True)\n",
-    "\n",
-    "x_train = train_df.drop(lending_club.target_column, axis=1)\n",
-    "y_train = train_df[lending_club.target_column]\n",
-    "x_test = test_df.drop(lending_club.target_column, axis=1)\n",
-    "y_test = test_df[lending_club.target_column]\n",
-    "\n",
-    "# Define the model\n",
-    "model = sm.GLM(y_train, x_train, family=sm.families.Binomial())\n",
-    "\n",
-    "# Fit the model\n",
-    "model = model.fit()\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_1_'></a>\n",
-    "\n",
-    "### Compute probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_probabilities = model.predict(x_train)\n",
-    "test_probabilities = model.predict(x_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_2_'></a>\n",
-    "\n",
-    "### Compute binary predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cut_off_threshold = 0.5\n",
-    "train_binary_predictions = (train_probabilities > cut_off_threshold).astype(int)\n",
-    "test_binary_predictions = (test_probabilities > cut_off_threshold).astype(int)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc6_3_'></a>\n",
-    "\n",
-    "### Compute scores\n",
-    "\n",
-    "In this phase, we translate model predictions into actionable scores using probability estimates generated by our trained model. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compute scores from the probabilities\n",
-    "train_scores = lending_club.compute_scores(train_probabilities)\n",
-    "test_scores = lending_club.compute_scores(test_probabilities)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_'></a>\n",
-    "\n",
-    "## Document the model\n",
-    "\n",
-    "To document the model with the ValidMind Library, you'll need to:\n",
-    "1. Preprocess the raw dataset\n",
-    "2. Initialize some training and test datasets\n",
-    "3. Initialize a model object you can use for testing\n",
-    "4. Run the full suite of tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_1_'></a>\n",
-    "\n",
-    "### Initialize the ValidMind datasets\n",
-    "\n",
-    "Before you can run tests, you must first initialize a ValidMind dataset object using the [`init_dataset`](https://docs.validmind.ai/validmind/validmind.html#init_dataset) function from the ValidMind (`vm`) module.\n",
-    "\n",
-    "This function takes a number of arguments:\n",
-    "\n",
-    "- `dataset`: The dataset that you want to provide as input to tests.\n",
-    "- `input_id`: A unique identifier that allows tracking what inputs are used when running each individual test.\n",
-    "- `target_column`: A required argument if tests require access to true values. This is the name of the target column in the dataset.\n",
-    "\n",
-    "With all datasets ready, you can now initialize the raw, processed, training and test datasets (`raw_df`, `preprocessed_df`, `fe_df`,  `train_df` and `test_df`) created earlier into their own dataset objects using [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm_raw_dataset = vm.init_dataset(\n",
-    "    dataset=df,\n",
-    "    input_id=\"raw_dataset\",\n",
-    "    target_column=lending_club.target_column,\n",
-    ")\n",
-    "\n",
-    "vm_preprocess_dataset = vm.init_dataset(\n",
-    "    dataset=preprocess_df,\n",
-    "    input_id=\"preprocess_dataset\",\n",
-    "    target_column=lending_club.target_column,\n",
-    ")\n",
-    "\n",
-    "vm_fe_dataset = vm.init_dataset(\n",
-    "    dataset=fe_df,\n",
-    "    input_id=\"fe_dataset\",\n",
-    "    target_column=lending_club.target_column,\n",
-    ")\n",
-    "\n",
-    "vm_train_ds = vm.init_dataset(\n",
-    "    dataset=train_df,\n",
-    "    input_id=\"train_dataset\",\n",
-    "    target_column=lending_club.target_column,\n",
-    ")\n",
-    "\n",
-    "vm_test_ds = vm.init_dataset(\n",
-    "    dataset=test_df,\n",
-    "    input_id=\"test_dataset\",\n",
-    "    target_column=lending_club.target_column,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_2_'></a>\n",
-    "\n",
-    "### Initialize a model object\n",
-    "\n",
-    "You will also need to initialize a ValidMind model object (`vm_model`) that can be passed to other functions for analysis and tests on the data. You simply intialize this model object with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm_model = vm.init_model(\n",
-    "    model,\n",
-    "    input_id=\"glm_model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_3_'></a>\n",
-    "\n",
-    "### Assign prediction values and probabilities to the datasets\n",
-    "\n",
-    "With our model now trained, we'll move on to assigning both the predictive probabilities coming directly from the model's predictions, and the binary prediction after applying the cutoff threshold described in the previous steps. \n",
-    "- These tasks are achieved through the use of the `assign_predictions()` method associated with the VM `dataset` object.\n",
-    "- This method links the model's class prediction values and probabilities to our VM train and test datasets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm_train_ds.assign_predictions(\n",
-    "    model=vm_model,\n",
-    "    prediction_values=train_binary_predictions,\n",
-    "    prediction_probabilities=train_probabilities,\n",
-    ")\n",
-    "\n",
-    "vm_test_ds.assign_predictions(\n",
-    "    model=vm_model,\n",
-    "    prediction_values=test_binary_predictions,\n",
-    "    prediction_probabilities=test_probabilities,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_4_'></a>\n",
-    "\n",
-    "### Assign scores to the datasets\n",
-    "\n",
-    "Credit scorecards revolve around scores computed from model predictions rather than raw predictions, so let's compute some scores!\n",
-    "\n",
-    "- To make this process auditable and ensure scores are properly integrated with our datasets, we use the `add_extra_column()` method from the VM `dataset` object.\n",
-    "- This approach allows us to append scores directly to our data, maintaining a streamlined and coherent dataset ready for analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vm_train_ds.add_extra_column(\"glm_scores\", train_scores)\n",
-    "vm_test_ds.add_extra_column(\"glm_scores\", test_scores)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_5_'></a>\n",
-    "\n",
-    "### Data validation\n",
-    "\n",
-    "During data validation, we ensure that the data we're working with is accurate, consistent, and ready for empirical analysis. We're looking for any anomalies that might skew our models or results. \n",
-    "\n",
-    "In this section, we use tests to collect evidence across raw, preprocessed, and feature-engineered datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_5_1_'></a>\n",
-    "\n",
-    "#### Run tests for raw data tests\n",
-    "\n",
-    "We perform initial validation of our raw data to understand its structure and detect any anomalies that could affect the modeling process. At this stage, we use a couple of tests that help us understand the data:\n",
-    "\n",
-    "- **Tabular description tables:** Statistical summary for each variable\n",
-    "- **Missing values bar plot:** Plots presence of any missing data\n",
-    "\n",
-    "To ensure we can run this test again against a different dataset, we'll label these test results with `<test_id>:raw_dataset`. This will make the results identifiable in the database, allowing us to include them in the ValidMind Platform."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.TabularDescriptionTables:raw_dataset\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_raw_dataset],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.MissingValuesBarPlot:raw_dataset\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_raw_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_5_2_'></a>\n",
-    "\n",
-    "#### Run tests for preprocessed data\n",
-    "\n",
-    "Before the modeling process begins, the data undergoes a final quality validation. To ensure the data is ready for the next data preparation step, we conduct the following tests:\n",
-    "\n",
-    "- **Tabular description tables:** Checking structural integrity after preprocessing\n",
-    "- **IQR outliers table:** Searching for statistical anomalies that may impact results\n",
-    "- **Class imbalance:** Investigating the proportionality of outcome classes\n",
-    "- **Tabular numerical histograms:** Visualizing distributions for numerical variables\n",
-    "- **Tabular categorical bar plots:** Analyzing frequencies of categorical variables\n",
-    "- **Target rate bar plots:** Examining the distribution of the target variable across categories\n",
-    "- **Pearson correlation matrix:** Identifying linear relationships between variables\n",
-    "\n",
-    "Similar to the previous step, we now want to run the same description tables against the preprocessed dataset to identify improvements and observe the effects of our changes on the data. This time, we'll do this by adding the `<test_id>:preprocess_dataset>` label to our results.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.TabularDescriptionTables:preprocess_dataset\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.IQROutliersTable\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.ClassImbalance\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.TabularNumericalHistograms\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.TabularCategoricalBarPlots\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.TargetRateBarPlots\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    "    params={\n",
-    "        \"default_column\": lending_club.target_column,\n",
-    "        \"columns\": None,\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.PearsonCorrelationMatrix\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.HighPearsonCorrelation\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_5_3_'></a>\n",
-    "\n",
-    "#### Run tests for WoE analysis\n",
-    "\n",
-    "To ensure data segmentation aligns with predictive value, we perform detailed tests on the following:\n",
-    "\n",
-    "- **WOE bin table:** This test checks the bins and their corresponding Weight of Evidence values, ensuring accuracy and relevance.\n",
-    "- **WOE bin plots:** These plots display the distribution of WoE across the data spectrum, providing a visual assessment of alignment and consistency."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.WOEBinTable\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    "    params={\n",
-    "        \"breaks_adj\": lending_club.breaks_adj,\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.WOEBinPlots\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_preprocess_dataset]\n",
-    "    },\n",
-    "    params={\n",
-    "        \"breaks_adj\": lending_club.breaks_adj,\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_6_'></a>\n",
-    "\n",
-    "### Model validation\n",
-    "\n",
-    "This phase verifies the model's predictive power and *generalizability*. Generalizability is the ability to accurately make future predictions based on past observations — how is this model going to perform when working with real data? \n",
-    "\n",
-    "Tests are applied to both training and evaluation stages to ensure robustness and accuracy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_6_1_'></a>\n",
-    "\n",
-    "#### Run tests for model training\n",
-    "\n",
-    "After training our model, we'll want to inspect the *model coefficients* and some of their statistical properties, such as probability values. A model coefficient is value that represents the weight or effect of an independent variable in a model. This step is crucial as it informs us about the relevance of the features used to train the model. We can also begin making decisions regarding the quality of our model. \n",
-    "\n",
-    "These tests assist us with this inspection:\n",
-    "\n",
-    "- **Dataset split:** Assesses the distribution of data across training and testing sets\n",
-    "- **Regression coeffs plot:** Visual inspection of the model's coefficients\n",
-    "- **Regression models coeffs:** Detailed evaluation of the regression coefficients' values\n",
-    "\n",
-    "Note that we use the `models` interface and pass a model as a list. This is because these tests support multiple models — if you have candidate models, you can use these tests to examine the differences in coefficients across multiple models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.data_validation.DatasetSplit\",\n",
-    "    inputs={\n",
-    "        \"datasets\": [vm_train_ds, vm_test_ds],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.statsmodels.RegressionCoeffs\",\n",
-    "    input_grid={\n",
-    "        \"model\": [vm_model]\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_6_2_'></a>\n",
-    "\n",
-    "#### Run tests for model evaluation\n",
-    "\n",
-    "Once we are satisfied with the trained model after inspecting the coefficients, we elevate our evaluation by examining more detailed performance metrics — such as the confusion matrix, Gini coefficient, and various visualizations. Here we also begin to examine the credit scores, comparing them across the test and training datasets to see how effectively they distinguish between defaulted and non-defaulted customers. \n",
-    "\n",
-    "These tests will help us to check whether the calibration of the scorecard is fit for purpose. Below is a list of tests we use to perform a thorough evaluation of both the binary classification model and the scorecard: \n",
-    "\n",
-    "- **Classifier performance:** Summarizing logistic regression metrics\n",
-    "- **GINI table:** Assessing the discriminatory power of the model\n",
-    "- **Confusion matrix:** Understanding classification accuracy\n",
-    "- **ROC curve:** Plotting the trade-off between sensitivity and specificity\n",
-    "- **Prediction probabilities histogram:** Distributing predictions to visualize outcomes\n",
-    "- **Cumulative prediction probabilities:** Cumulative probability analysis for predictions\n",
-    "- **Scorecard histogram:** Evaluating the distribution of scorecard points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.sklearn.ClassifierPerformance\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "        \"model\": [vm_model],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.statsmodels.GINITable\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "        \"model\": [vm_model],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.sklearn.ConfusionMatrix\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "        \"model\": [vm_model],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.sklearn.ROCCurve\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "        \"model\": [vm_model],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "        \"model\": [vm_model],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.statsmodels.CumulativePredictionProbabilities\",\n",
-    "    input_grid={\n",
-    "        \"model\": [vm_model],\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.statsmodels.ScorecardHistogram\",\n",
-    "    input_grid={\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "    },\n",
-    "    params={\n",
-    "        \"score_column\": \"glm_scores\",\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc7_6_3_'></a>\n",
-    "\n",
-    "#### Run tests for model explainability\n",
-    "\n",
-    "Following the detailed evaluation of the model through the performance metrics above, we then focus on *model explainability* to further understand the contributions of individual features. \n",
-    "Model explainability is the ability to understand and interpret the decisions made by a machine learning model. This analysis is crucial for ensuring that our credit scoring model is not only effective but also interpretable in practical scenarios.\n",
-    "\n",
-    "Here are the key tests we deploy to analyze the model's explainability after evaluating its overall performance:\n",
-    "\n",
-    "- **Regression permutation feature importance:** Identifies which features most significantly affect the model's predictions by observing changes in performance when feature values are shuffled\n",
-    "- **Features AUC:** Determines the discriminative power of each feature, showcasing how well each can independently predict the outcome"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance\",\n",
-    "    input_grid={\n",
-    "        \"model\": [vm_model],\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = vm.tests.run_test(\n",
-    "    \"validmind.model_validation.FeaturesAUC\",\n",
-    "    input_grid={\n",
-    "        \"model\": [vm_model],\n",
-    "        \"dataset\": [vm_train_ds, vm_test_ds],\n",
-    "    },\n",
-    ")\n",
-    "test.log()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc8_'></a>\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.\n",
-    "\n",
-    "<a id='toc8_1_'></a>\n",
-    "\n",
-    "### Work with your model documentation\n",
-    "\n",
-    "1. In the ValidMind Platform, click **Documentation** under Documents for the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html))\n",
-    "\n",
-    "2. Expand the following sections and take a look around:\n",
-    "\n",
-    "   - **2. Data Preparation**\n",
-    "   - **3. Model Development**\n",
-    "\n",
-    "What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation (hint: some of the tests in **2.3. Feature Selection and Engineering** look like they need some attention), view guidelines, collaborate with validators, and submit your model documentation for approval when it's ready.\n",
-    "\n",
-    "<a id='toc8_2_'></a>\n",
-    "\n",
-    "### Discover more learning resources\n",
-    "\n",
-    "We offer many interactive notebooks to help you document models:\n",
-    "\n",
-    "- [Run tests & test suites](https://docs.validmind.ai/developer/model-testing/testing-overview.html)\n",
-    "- [Code samples](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n",
-    "\n",
-    "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='toc9_'></a>\n",
-    "\n",
-    "## Upgrade ValidMind\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\">After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.</div>\n",
-    "\n",
-    "Retrieve the information for the currently installed version of ValidMind:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip show validmind"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n",
-    "\n",
-    "```bash\n",
-    "%pip install --upgrade validmind\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You may need to restart your kernel after running the upgrade package for changes to be applied."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 34097dd2d2ed73a7d6a8cea4161e0859ef64549e Mon Sep 17 00:00:00 2001
From: Juan <juan@validmind.ai>
Date: Wed, 6 Aug 2025 17:01:26 +0200
Subject: [PATCH 26/47] 2.8.30

---
 pyproject.toml           | 2 +-
 validmind/__version__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7bff239ff..1ace8430e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ description = "ValidMind Library"
 license = "Commercial License"
 name = "validmind"
 readme = "README.pypi.md"
-version = "2.8.29"
+version = "2.8.30"
 
 [tool.poetry.dependencies]
 aiohttp = { extras = ["speedups"], version = "*" }
diff --git a/validmind/__version__.py b/validmind/__version__.py
index e7d98bbba..839fa31f2 100644
--- a/validmind/__version__.py
+++ b/validmind/__version__.py
@@ -1 +1 @@
-__version__ = "2.8.28"
+__version__ = "2.8.30"

From 710e8fdc47adc99f8ab073d48091c259cfc5d78f Mon Sep 17 00:00:00 2001
From: Juan <juan@validmind.ai>
Date: Thu, 7 Aug 2025 23:45:33 +0200
Subject: [PATCH 27/47] Make timeout configurable and send figures in batches

---
 validmind/api_client.py              |  8 +++--
 validmind/logging.py                 | 48 ++++++++++++++++++++++++++++
 validmind/vm_models/result/result.py | 28 ++++++++++++++--
 3 files changed, 79 insertions(+), 5 deletions(-)

diff --git a/validmind/api_client.py b/validmind/api_client.py
index 7bee2290b..1eb1cd5c4 100644
--- a/validmind/api_client.py
+++ b/validmind/api_client.py
@@ -22,7 +22,7 @@
 
 from .client_config import client_config
 from .errors import MissingAPICredentialsError, MissingModelIdError, raise_api_error
-from .logging import get_logger, init_sentry, send_single_error
+from .logging import get_logger, init_sentry, log_api_operation, send_single_error
 from .utils import NumpyEncoder, is_html, md_to_html, run_async
 from .vm_models import Figure
 
@@ -85,7 +85,7 @@ def _get_session() -> aiohttp.ClientSession:
     if not __api_session or __api_session.closed:
         __api_session = aiohttp.ClientSession(
             headers=_get_api_headers(),
-            timeout=aiohttp.ClientTimeout(total=30),
+            timeout=aiohttp.ClientTimeout(total=int(os.getenv("VM_API_TIMEOUT", 30))),
         )
 
     return __api_session
@@ -304,6 +304,10 @@ async def alog_metadata(
         raise e
 
 
+@log_api_operation(
+    operation_name="Sending figure to ValidMind API",
+    extract_key=lambda figure: figure.key,
+)
 async def alog_figure(figure: Figure) -> Dict[str, Any]:
     """Logs a figure.
 
diff --git a/validmind/logging.py b/validmind/logging.py
index 1cb81ec73..3b0322b39 100644
--- a/validmind/logging.py
+++ b/validmind/logging.py
@@ -170,6 +170,54 @@ async def wrap(*args: Any, **kwargs: Any) -> Any:
     return wrap
 
 
+def log_api_operation(
+    operation_name: Optional[str] = None,
+    logger: Optional[logging.Logger] = None,
+    extract_key: Optional[Callable] = None,
+    force: bool = False,
+) -> Callable[[F], F]:
+    """Decorator to log API operations like figure uploads.
+
+    Args:
+        operation_name (str, optional): The name of the operation. Defaults to function name.
+        logger (logging.Logger, optional): The logger to use. Defaults to None.
+        extract_key (Callable, optional): Function to extract a key from args for logging.
+        force (bool, optional): Whether to force logging even if env var is off.
+
+    Returns:
+        Callable: The decorated function.
+    """
+
+    def decorator(func: F) -> F:
+        # check if log level is set to debug
+        if _get_log_level() != logging.DEBUG and not force:
+            return func
+
+        nonlocal logger
+        if logger is None:
+            logger = get_logger()
+
+        nonlocal operation_name
+        if operation_name is None:
+            operation_name = func.__name__
+
+        async def wrapped(*args: Any, **kwargs: Any) -> Any:
+            # Try to extract a meaningful identifier from the arguments
+            identifier = ""
+            if extract_key and args:
+                try:
+                    identifier = f": {extract_key(args[0])}"
+                except (AttributeError, IndexError):
+                    pass
+
+            logger.debug(f"{operation_name}{identifier}")
+            return await func(*args, **kwargs)
+
+        return wrapped
+
+    return decorator
+
+
 def send_single_error(error: Exception) -> None:
     """Send a single error to Sentry.
 
diff --git a/validmind/vm_models/result/result.py b/validmind/vm_models/result/result.py
index ecc763af4..6edee7bbe 100644
--- a/validmind/vm_models/result/result.py
+++ b/validmind/vm_models/result/result.py
@@ -7,6 +7,7 @@
 """
 import asyncio
 import json
+import os
 from abc import abstractmethod
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Union
@@ -20,7 +21,7 @@
 from ... import api_client
 from ...ai.utils import DescriptionFuture
 from ...errors import InvalidParameterError
-from ...logging import get_logger
+from ...logging import get_logger, log_api_operation
 from ...utils import (
     HumanReadableEncoder,
     NumpyEncoder,
@@ -476,9 +477,30 @@ async def log_async(
             )
 
         if self.figures:
-            tasks.extend(
-                [api_client.alog_figure(figure) for figure in (self.figures or [])]
+            batch_size = min(
+                len(self.figures), int(os.getenv("VM_FIGURE_MAX_BATCH_SIZE", 20))
             )
+            figure_batches = [
+                self.figures[i : i + batch_size]
+                for i in range(0, len(self.figures), batch_size)
+            ]
+
+            async def upload_figures_in_batches():
+                for batch in figure_batches:
+
+                    @log_api_operation(
+                        operation_name=f"Uploading batch of {len(batch)} figures"
+                    )
+                    async def process_batch():
+                        batch_tasks = [
+                            api_client.alog_figure(figure) for figure in batch
+                        ]
+                        return await asyncio.gather(*batch_tasks)
+
+                    await process_batch()
+
+            tasks.append(upload_figures_in_batches())
+
         if self.description:
             revision_name = (
                 AI_REVISION_NAME

From 16f3a262f20f7c8b15e4c1dda8e389d0c0ebaefc Mon Sep 17 00:00:00 2001
From: Juan <juan@validmind.ai>
Date: Fri, 8 Aug 2025 09:19:39 +0200
Subject: [PATCH 28/47] 2.8.31

---
 pyproject.toml           | 2 +-
 validmind/__version__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 1ace8430e..577ed59e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ description = "ValidMind Library"
 license = "Commercial License"
 name = "validmind"
 readme = "README.pypi.md"
-version = "2.8.30"
+version = "2.8.31"
 
 [tool.poetry.dependencies]
 aiohttp = { extras = ["speedups"], version = "*" }
diff --git a/validmind/__version__.py b/validmind/__version__.py
index 839fa31f2..3a5995d32 100644
--- a/validmind/__version__.py
+++ b/validmind/__version__.py
@@ -1 +1 @@
-__version__ = "2.8.30"
+__version__ = "2.8.31"

From 26e6431421bb68f3562908baef96dd98c4324c75 Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <30263958+AnilSorathiya@users.noreply.github.com>
Date: Fri, 8 Aug 2025 16:06:45 +0530
Subject: [PATCH 29/47] Support new interface assign_scores to VM dataset
 (#407)

* add assign score interface

* unit tests for assign score

* support list of value from unit metrics

* new tests and tutorial notebook

* rename from assign_score to assign_scores

* add text that this feature supports unit_metrics

* 2.9.0
---
 .../assign_score_complete_tutorial.ipynb      | 723 ++++++++++++++++++
 pyproject.toml                                |   2 +-
 tests/test_dataset.py                         | 295 +++++++
 validmind/__version__.py                      |   2 +-
 validmind/tests/output.py                     |   8 +-
 .../individual/AbsoluteError.py               |  42 +
 .../classification/individual/BrierScore.py   |  56 ++
 .../individual/CalibrationError.py            |  77 ++
 .../classification/individual/ClassBalance.py |  65 ++
 .../classification/individual/Confidence.py   |  52 ++
 .../classification/individual/Correctness.py  |  41 +
 .../classification/individual/LogLoss.py      |  61 ++
 .../classification/individual/OutlierScore.py |  86 +++
 .../individual/ProbabilityError.py            |  54 ++
 .../classification/individual/Uncertainty.py  |  60 ++
 .../classification/individual/__init__.py     |   0
 validmind/vm_models/dataset/dataset.py        | 148 +++-
 validmind/vm_models/result/result.py          |   2 +-
 18 files changed, 1769 insertions(+), 5 deletions(-)
 create mode 100644 notebooks/how_to/assign_score_complete_tutorial.ipynb
 create mode 100644 validmind/unit_metrics/classification/individual/AbsoluteError.py
 create mode 100644 validmind/unit_metrics/classification/individual/BrierScore.py
 create mode 100644 validmind/unit_metrics/classification/individual/CalibrationError.py
 create mode 100644 validmind/unit_metrics/classification/individual/ClassBalance.py
 create mode 100644 validmind/unit_metrics/classification/individual/Confidence.py
 create mode 100644 validmind/unit_metrics/classification/individual/Correctness.py
 create mode 100644 validmind/unit_metrics/classification/individual/LogLoss.py
 create mode 100644 validmind/unit_metrics/classification/individual/OutlierScore.py
 create mode 100644 validmind/unit_metrics/classification/individual/ProbabilityError.py
 create mode 100644 validmind/unit_metrics/classification/individual/Uncertainty.py
 create mode 100644 validmind/unit_metrics/classification/individual/__init__.py

diff --git a/notebooks/how_to/assign_score_complete_tutorial.ipynb b/notebooks/how_to/assign_score_complete_tutorial.ipynb
new file mode 100644
index 000000000..cbb1d14bd
--- /dev/null
+++ b/notebooks/how_to/assign_score_complete_tutorial.ipynb
@@ -0,0 +1,723 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "# Intro to Assign Scores\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "The `assign_scores()` method is a powerful feature that allows you to compute and add unit metric scores as new columns in your dataset. This method takes a model and metric(s) as input, computes the specified metrics from the ValidMind unit_metrics library, and adds them as new columns. The computed metrics can be scalar values that apply to the entire dataset or per-row values, providing flexibility in how performance is measured and tracked.\n",
+        "\n",
+        "In this interactive notebook, we demonstrate how to use the `assign_scores()` method effectively. We'll walk through a complete example using a customer churn dataset, showing how to compute and assign both dataset-level metrics (like overall F1 score) and row-level metrics (like prediction probabilities). You'll learn how to work with single and multiple unit metrics, pass custom parameters, and handle different metric types - all while maintaining a clean, organized dataset structure. Currently, assign_scores() supports all metrics available in the validmind.unit_metrics module.\n",
+        "\n",
+        "**The Power of Integrated Scoring**\n",
+        "\n",
+        "Traditional model evaluation workflows often involve computing metrics separately from your core dataset, leading to fragmented analysis and potential data misalignment. The `assign_scores()` method addresses this challenge by:\n",
+        "\n",
+        "- **Seamless Integration**: Directly embedding computed metrics as dataset columns using a consistent naming convention\n",
+        "- **Enhanced Traceability**: Maintaining clear links between model predictions and performance metrics\n",
+        "- **Simplified Analysis**: Enabling straightforward comparison of metrics across different models and datasets\n",
+        "- **Standardized Workflow**: Providing a unified approach to metric computation and storage\n",
+        "\n",
+        "**Understanding assign_scores()**\n",
+        "\n",
+        "The `assign_scores()` method computes unit metrics for a given model-dataset combination and adds the results as new columns to your dataset. Each new column follows the naming convention: `{model.input_id}_{metric_name}`, ensuring clear identification of which model and metric combination generated each score.\n",
+        "\n",
+        "Key features:\n",
+        "\n",
+        "- **Flexible Input**: Accepts single metrics or lists of metrics\n",
+        "- **Parameter Support**: Allows passing additional parameters to underlying metric implementations\n",
+        "- **Multi-Model Support**: Can assign scores from multiple models to the same dataset\n",
+        "- **Type Agnostic**: Works with classification, regression, and other model types\n",
+        "\n",
+        "This approach streamlines your model evaluation workflow, making performance metrics an integral part of your dataset rather than external calculations.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## Contents    \n",
+        "- [About ValidMind](#toc1_)    \n",
+        "  - [Before you begin](#toc1_1_)    \n",
+        "  - [New to ValidMind?](#toc1_2_)    \n",
+        "- [Install the ValidMind Library](#toc2_)    \n",
+        "- [Initialize the ValidMind Library](#toc3_)    \n",
+        "  - [Get your code snippet](#toc3_1_)   \n",
+        "- [Load the demo dataset](#toc4_)    \n",
+        "- [Train models for testing](#toc5_)    \n",
+        "- [Initialize ValidMind objects](#toc6_)    \n",
+        "- [Assign predictions](#toc7_)    \n",
+        "- [Using assign_scores()](#toc8_)    \n",
+        "  - [Basic Usage](#toc8_1_)    \n",
+        "  - [Single Metric Assignment](#toc8_2_)    \n",
+        "  - [Multiple Metrics Assignment](#toc8_3_)    \n",
+        "  - [Passing Parameters to Metrics](#toc8_4_)    \n",
+        "  - [Working with Different Metric Types](#toc8_5_)    \n",
+        "- [Advanced assign_scores() Usage](#toc9_)    \n",
+        "  - [Multi-Model Scoring](#toc9_1_)    \n",
+        "  - [Individual Metrics](#toc9_2_)      \n",
+        "- [Next steps](#toc12_)    \n",
+        "  - [Work with your model documentation](#toc12_1_)    \n",
+        "  - [Discover more learning resources](#toc12_2_)    \n",
+        "- [Upgrade ValidMind](#toc13_)    \n",
+        "\n",
+        "<!-- vscode-jupyter-toc-config\n",
+        "\tnumbering=false\n",
+        "\tanchor=true\n",
+        "\tflat=false\n",
+        "\tminLevel=2\n",
+        "\tmaxLevel=4\n",
+        "\t/vscode-jupyter-toc-config -->\n",
+        "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "\n",
+        "<a id='toc1_'></a>\n",
+        "\n",
+        "## About ValidMind \n",
+        "\n",
+        "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.\n",
+        "\n",
+        "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.\n",
+        "\n",
+        "<a id='toc1_1_'></a>\n",
+        "\n",
+        "### Before you begin \n",
+        "\n",
+        "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n",
+        "\n",
+        "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html).\n",
+        "\n",
+        "\n",
+        "\n",
+        "<a id='toc1_2_'></a>\n",
+        "\n",
+        "### New to ValidMind? \n",
+        "\n",
+        "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
+        "\n",
+        "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, you'll need access to a ValidMind account.</b></span>\n",
+        "<br></br>\n",
+        "<a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc2_'></a>\n",
+        "\n",
+        "## Install the ValidMind Library\n",
+        "\n",
+        "To install the library:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -q validmind\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc3_'></a>\n",
+        "\n",
+        "## Initialize the ValidMind Library \n",
+        "\n",
+        "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.\n",
+        "\n",
+        "<a id='toc3_1_'></a>\n",
+        "\n",
+        "### Get your code snippet\n",
+        "\n",
+        "1. In a browser, [log in to ValidMind](https://docs.validmind.ai/guide/configuration/log-in-to-validmind.html).\n",
+        "\n",
+        "2. In the left sidebar, navigate to **Model Inventory** and click **+ Register Model**.\n",
+        "\n",
+        "3. Enter the model details and click **Continue**. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/register-models-in-inventory.html))\n",
+        "\n",
+        "   For example, to register a model for use with this notebook, select:\n",
+        "\n",
+        "   - Documentation template: `Binary classification`\n",
+        "   - Use case: `Marketing/Sales - Analytics`\n",
+        "\n",
+        "   You can fill in other options according to your preference.\n",
+        "\n",
+        "4. Go to **Getting Started** and click **Copy snippet to clipboard**.\n",
+        "\n",
+        "Next, [load your model identifier credentials from an `.env` file](https://docs.validmind.ai/developer/model-documentation/store-credentials-in-env-file.html) or replace the placeholder with your own code snippet:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load your model identifier credentials from an `.env` file\n",
+        "\n",
+        "%load_ext dotenv\n",
+        "%dotenv .env\n",
+        "\n",
+        "# Or replace with your code snippet\n",
+        "\n",
+        "import validmind as vm\n",
+        "\n",
+        "vm.init(\n",
+        "    # api_host=\"...\",\n",
+        "    # api_key=\"...\",\n",
+        "    # api_secret=\"...\",\n",
+        "    # model=\"...\",\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc4_'></a>\n",
+        "\n",
+        "## Load the demo dataset \n",
+        "\n",
+        "In this example, we load a demo dataset to demonstrate the assign_scores functionality with customer churn prediction models.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from validmind.datasets.classification import customer_churn as demo_dataset\n",
+        "\n",
+        "print(\n",
+        "    f\"Loaded demo dataset with: \\n\\n\\t• Target column: '{demo_dataset.target_column}' \\n\\t• Class labels: {demo_dataset.class_labels}\"\n",
+        ")\n",
+        "\n",
+        "raw_df = demo_dataset.load_data()\n",
+        "raw_df.head()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc5_'></a>\n",
+        "\n",
+        "## Train models for testing \n",
+        "\n",
+        "We'll train two different customer churn models to demonstrate the assign_scores functionality with multiple models.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "# Preprocess the data\n",
+        "train_df, validation_df, test_df = demo_dataset.preprocess(raw_df)\n",
+        "\n",
+        "# Prepare training data\n",
+        "x_train = train_df.drop(demo_dataset.target_column, axis=1)\n",
+        "y_train = train_df[demo_dataset.target_column]\n",
+        "x_val = validation_df.drop(demo_dataset.target_column, axis=1)\n",
+        "y_val = validation_df[demo_dataset.target_column]\n",
+        "\n",
+        "# Train XGBoost model\n",
+        "xgb_model = xgb.XGBClassifier(early_stopping_rounds=10, random_state=42)\n",
+        "xgb_model.set_params(\n",
+        "    eval_metric=[\"error\", \"logloss\", \"auc\"],\n",
+        ")\n",
+        "xgb_model.fit(\n",
+        "    x_train,\n",
+        "    y_train,\n",
+        "    eval_set=[(x_val, y_val)],\n",
+        "    verbose=False,\n",
+        ")\n",
+        "\n",
+        "# Train Random Forest model\n",
+        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "rf_model.fit(x_train, y_train)\n",
+        "\n",
+        "print(\"Models trained successfully!\")\n",
+        "print(f\"XGBoost training accuracy: {xgb_model.score(x_train, y_train):.3f}\")\n",
+        "print(f\"Random Forest training accuracy: {rf_model.score(x_train, y_train):.3f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc6_'></a>\n",
+        "\n",
+        "## Initialize ValidMind objects \n",
+        "\n",
+        "We initialize ValidMind `dataset` and `model` objects. The `input_id` parameter is crucial for the assign_scores functionality as it determines the column naming convention for assigned scores.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Initialize datasets\n",
+        "vm_train_ds = vm.init_dataset(\n",
+        "    input_id=\"train_dataset\",\n",
+        "    dataset=train_df,\n",
+        "    target_column=demo_dataset.target_column,\n",
+        ")\n",
+        "vm_test_ds = vm.init_dataset(\n",
+        "    input_id=\"test_dataset\",\n",
+        "    dataset=test_df,\n",
+        "    target_column=demo_dataset.target_column,\n",
+        ")\n",
+        "\n",
+        "# Initialize models with descriptive input_ids\n",
+        "vm_xgb_model = vm.init_model(model=xgb_model, input_id=\"xgboost_model\")\n",
+        "vm_rf_model = vm.init_model(model=rf_model, input_id=\"random_forest_model\")\n",
+        "\n",
+        "print(\"ValidMind objects initialized successfully!\")\n",
+        "print(f\"XGBoost model ID: {vm_xgb_model.input_id}\")\n",
+        "print(f\"Random Forest model ID: {vm_rf_model.input_id}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc7_'></a>\n",
+        "\n",
+        "## Assign predictions \n",
+        "\n",
+        "Before we can use assign_scores(), we need to assign predictions to our datasets. This step is essential as many unit metrics require both actual and predicted values.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Assign predictions for both models to both datasets\n",
+        "vm_train_ds.assign_predictions(model=vm_xgb_model)\n",
+        "vm_train_ds.assign_predictions(model=vm_rf_model)\n",
+        "\n",
+        "vm_test_ds.assign_predictions(model=vm_xgb_model)\n",
+        "vm_test_ds.assign_predictions(model=vm_rf_model)\n",
+        "\n",
+        "print(\"Predictions assigned successfully!\")\n",
+        "print(f\"Test dataset now has {len(vm_test_ds.df.columns)} columns\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc8_'></a>\n",
+        "\n",
+        "## Using assign_scores()\n",
+        "\n",
+        "Now we'll explore the various ways to use the assign_scores() method to integrate performance metrics directly into your dataset.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc8_1_'></a>\n",
+        "\n",
+        "### Basic Usage\n",
+        "\n",
+        "The assign_scores() method has a simple interface:\n",
+        "\n",
+        "```python\n",
+        "dataset.assign_scores(model, metrics, **kwargs)\n",
+        "```\n",
+        "\n",
+        "- **model**: A ValidMind model object\n",
+        "- **metrics**: Single metric ID or list of metric IDs (can use short names or full IDs)\n",
+        "- **kwargs**: Additional parameters passed to the underlying metric implementations\n",
+        "\n",
+        "Let's first check what columns we currently have in our test dataset:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"Current columns in test dataset:\")\n",
+        "for i, col in enumerate(vm_test_ds.df.columns, 1):\n",
+        "    print(f\"{i:2d}. {col}\")\n",
+        "\n",
+        "print(f\"\\nDataset shape: {vm_test_ds.df.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc8_2_'></a>\n",
+        "\n",
+        "### Single Metric Assignment\n",
+        "\n",
+        "Let's start by assigning a single metric - the F1 score - for our XGBoost model on the test dataset.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Assign F1 score for XGBoost model\n",
+        "vm_test_ds.assign_scores(vm_xgb_model, \"F1\")\n",
+        "\n",
+        "print(\"After assigning F1 score:\")\n",
+        "print(f\"New column added: {vm_test_ds.df.columns}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc8_3_'></a>\n",
+        "\n",
+        "### Multiple Metrics Assignment\n",
+        "\n",
+        "We can assign multiple metrics at once by passing a list of metric names. This is more efficient than calling assign_scores() multiple times.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Assign multiple classification metrics for the Random Forest model\n",
+        "classification_metrics = [\"Precision\", \"Recall\", \"Accuracy\", \"ROC_AUC\"]\n",
+        "\n",
+        "vm_test_ds.assign_scores(vm_rf_model, classification_metrics)\n",
+        "\n",
+        "print(\"After assigning multiple metrics for Random Forest:\")\n",
+        "rf_columns = [col for col in vm_test_ds.df.columns if 'random_forest_model' in col]\n",
+        "print(f\"Random Forest columns: {rf_columns}\")\n",
+        "\n",
+        "# Display the metric values\n",
+        "for metric in classification_metrics:\n",
+        "    col_name = f\"random_forest_model_{metric}\"\n",
+        "    if col_name in vm_test_ds.df.columns:\n",
+        "        value = vm_test_ds.df[col_name].iloc[0]\n",
+        "        print(f\"{metric}: {value:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc8_4_'></a>\n",
+        "\n",
+        "### Passing Parameters to Metrics\n",
+        "\n",
+        "Many unit metrics accept additional parameters that are passed through to the underlying sklearn implementations. Let's demonstrate this with the ROC_AUC metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Assign ROC_AUC with different averaging strategies\n",
+        "vm_test_ds.assign_scores(vm_xgb_model, \"ROC_AUC\", average=\"macro\")\n",
+        "\n",
+        "# We can also assign with different parameters by calling assign_scores again\n",
+        "# Note: This will overwrite the previous column with the same name\n",
+        "print(\"ROC_AUC assigned with macro averaging\")\n",
+        "\n",
+        "# Let's also assign precision and recall with different averaging\n",
+        "vm_test_ds.assign_scores(vm_xgb_model, [\"Precision\", \"Recall\"], average=\"weighted\")\n",
+        "\n",
+        "print(\"Precision and Recall assigned with weighted averaging\")\n",
+        "\n",
+        "# Display current XGBoost metric columns\n",
+        "xgb_columns = [col for col in vm_test_ds.df.columns if 'xgboost_model' in col]\n",
+        "print(f\"\\nXGBoost model columns: {xgb_columns}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc9_1_'></a>\n",
+        "\n",
+        "### Multi-Model Scoring\n",
+        "\n",
+        "One of the powerful features of assign_scores() is the ability to assign scores from multiple models to the same dataset, enabling easy model comparison.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Let's assign a comprehensive set of metrics for both models\n",
+        "comprehensive_metrics = [\"F1\", \"Precision\", \"Recall\", \"Accuracy\", \"ROC_AUC\"]\n",
+        "\n",
+        "# Assign for XGBoost model\n",
+        "vm_test_ds.assign_scores(vm_xgb_model, comprehensive_metrics)\n",
+        "\n",
+        "# Assign for Random Forest model}\n",
+        "vm_test_ds.assign_scores(vm_rf_model, comprehensive_metrics)\n",
+        "\n",
+        "print(\"Comprehensive metrics assigned for both models!\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<a id='toc9_2_'></a>\n",
+        "\n",
+        "### Individual Metrics\n",
+        "The next section demonstrates how to assign individual metrics that compute scores per row, rather than aggregate metrics.\n",
+        "We'll use two important metrics:\n",
+        " \n",
+        "- Brier Score: Measures how well calibrated the model's probability predictions are for each individual prediction\n",
+        "- Log Loss: Evaluates how well the predicted probabilities match the true labels on a per-prediction basis\n",
+        "\n",
+        "Both metrics provide more granular insights into model performance at the individual prediction level.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Let's add some individual metrics that compute per-row scores\n",
+        "print(\"Adding individual metrics...\")\n",
+        "\n",
+        "# Add Brier Score - measures accuracy of probabilistic predictions per row\n",
+        "vm_test_ds.assign_scores(vm_xgb_model, \"BrierScore\")\n",
+        "print(\"Added Brier Score - lower values indicate better calibrated probabilities\")\n",
+        "\n",
+        "# Add Log Loss - measures how well the predicted probabilities match true labels per row\n",
+        "vm_test_ds.assign_scores(vm_xgb_model, \"LogLoss\")\n",
+        "print(\"Added Log Loss - lower values indicate better probability estimates\")\n",
+        "\n",
+        "# Create a comparison summary showing first few rows of individual metrics\n",
+        "print(\"\\nFirst few rows of individual metrics:\")\n",
+        "individual_metrics = [col for col in vm_test_ds.df.columns if any(m in col for m in ['BrierScore', 'LogLoss'])]\n",
+        "print(vm_test_ds.df[individual_metrics].head())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "vm_test_ds._df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc12_'></a>\n",
+        "\n",
+        "## Next steps \n",
+        "\n",
+        "You can explore the assigned scores right in the notebook as demonstrated above. However, there's even more value in using the ValidMind Platform to work with your model documentation and monitoring.\n",
+        "\n",
+        "\n",
+        "\n",
+        "<a id='toc12_1_'></a>\n",
+        "\n",
+        "### Work with your model documentation \n",
+        "\n",
+        "1. From the **Model Inventory** in the ValidMind Platform, go to the model you registered earlier. ([Need more help?](https://docs.validmind.ai/guide/model-inventory/working-with-model-inventory.html))\n",
+        "\n",
+        "2. Click and expand the **Model Development** section.\n",
+        "\n",
+        "The scores you've assigned using `assign_scores()` become part of your model's documentation and can be used in ongoing monitoring workflows. You can view these metrics over time, set up alerts for performance drift, and compare models systematically. [Learn more ...](https://docs.validmind.ai/guide/model-documentation/working-with-model-documentation.html)\n",
+        "\n",
+        "\n",
+        "\n",
+        "<a id='toc12_2_'></a>\n",
+        "\n",
+        "### Discover more learning resources \n",
+        "\n",
+        "We offer many interactive notebooks to help you work with model scoring and evaluation:\n",
+        "\n",
+        "- [Run unit metrics](https://docs.validmind.ai/developer/model-testing/testing-overview.html)\n",
+        "- [Assign predictions](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n",
+        "- [Model comparison workflows](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n",
+        "\n",
+        "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "<a id='toc13_'></a>\n",
+        "\n",
+        "## Upgrade ValidMind\n",
+        "\n",
+        "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\">After installing ValidMind, you'll want to periodically make sure you are on the latest version to access any new features and other enhancements.</div>\n",
+        "\n",
+        "Retrieve the information for the currently installed version of ValidMind:\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "%pip show validmind\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "If the version returned is lower than the version indicated in our [production open-source code](https://github.com/validmind/validmind-library/blob/prod/validmind/__version__.py), restart your notebook and run:\n",
+        "\n",
+        "```bash\n",
+        "%pip install --upgrade validmind\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "You may need to restart your kernel after running the upgrade package for changes to be applied.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "ValidMind Library",
+      "language": "python",
+      "name": "validmind"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 577ed59e0..8d5395757 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ description = "ValidMind Library"
 license = "Commercial License"
 name = "validmind"
 readme = "README.pypi.md"
-version = "2.8.31"
+version = "2.9.0"
 
 [tool.poetry.dependencies]
 aiohttp = { extras = ["speedups"], version = "*" }
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 41bc40fc8..c15aa07fe 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -516,6 +516,301 @@ def test_assign_predictions_with_invalid_predict_fn(self):
 
         self.assertIn("FunctionModel requires a callable predict_fn", str(context.exception))
 
+    def test_assign_scores_single_metric(self):
+        """
+        Test assigning a single metric score to dataset
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="test_model", model=model, __log=False)
+
+        # Assign predictions first (required for unit metrics)
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Test assign_scores with single metric
+        vm_dataset.assign_scores(vm_model, "F1")
+
+        # Check that the metric column was added
+        expected_column = f"{vm_model.input_id}_F1"
+        self.assertTrue(expected_column in vm_dataset.df.columns)
+
+        # Verify the column has the same value for all rows (scalar metric)
+        metric_values = vm_dataset.df[expected_column]
+        self.assertEqual(metric_values.nunique(), 1, "All rows should have the same metric value")
+
+        # Verify the value is reasonable for F1 score (between 0 and 1)
+        f1_value = metric_values.iloc[0]
+        self.assertTrue(0 <= f1_value <= 1, f"F1 score should be between 0 and 1, got {f1_value}")
+
+    def test_assign_scores_multiple_metrics(self):
+        """
+        Test assigning multiple metric scores to dataset
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="test_model", model=model, __log=False)
+
+        # Assign predictions first
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Test assign_scores with multiple metrics
+        metrics = ["F1", "Precision", "Recall"]
+        vm_dataset.assign_scores(vm_model, metrics)
+
+        # Check that all metric columns were added
+        for metric in metrics:
+            expected_column = f"{vm_model.input_id}_{metric}"
+            self.assertTrue(expected_column in vm_dataset.df.columns)
+
+            # Verify each column has the same value for all rows
+            metric_values = vm_dataset.df[expected_column]
+            self.assertEqual(metric_values.nunique(), 1, f"All rows should have the same {metric} value")
+
+            # Verify the value is reasonable (between 0 and 1 for these metrics)
+            metric_value = metric_values.iloc[0]
+            self.assertTrue(0 <= metric_value <= 1, f"{metric} should be between 0 and 1, got {metric_value}")
+
+    def test_assign_scores_with_parameters(self):
+        """
+        Test assigning metric scores with custom parameters
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="test_model", model=model, __log=False)
+
+        # Assign predictions first
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Test assign_scores with parameters
+        vm_dataset.assign_scores(vm_model, "ROC_AUC", **{"average": "weighted"})
+
+        # Check that the metric column was added
+        expected_column = f"{vm_model.input_id}_ROC_AUC"
+        self.assertTrue(expected_column in vm_dataset.df.columns)
+
+        # Verify the value is reasonable for ROC AUC (between 0 and 1)
+        roc_values = vm_dataset.df[expected_column]
+        roc_value = roc_values.iloc[0]
+        self.assertTrue(0 <= roc_value <= 1, f"ROC AUC should be between 0 and 1, got {roc_value}")
+
+    def test_assign_scores_full_metric_id(self):
+        """
+        Test assigning scores using full metric IDs
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="test_model", model=model, __log=False)
+
+        # Assign predictions first
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Test assign_scores with full metric ID
+        full_metric_id = "validmind.unit_metrics.classification.Accuracy"
+        vm_dataset.assign_scores(vm_model, full_metric_id)
+
+        # Check that the metric column was added with correct name
+        expected_column = f"{vm_model.input_id}_Accuracy"
+        self.assertTrue(expected_column in vm_dataset.df.columns)
+
+        # Verify the value is reasonable for accuracy (between 0 and 1)
+        accuracy_values = vm_dataset.df[expected_column]
+        accuracy_value = accuracy_values.iloc[0]
+        self.assertTrue(0 <= accuracy_value <= 1, f"Accuracy should be between 0 and 1, got {accuracy_value}")
+
+    def test_assign_scores_regression_model(self):
+        """
+        Test assigning metric scores for regression model
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0.1, 1.2, 2.3]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a regression model
+        model = LinearRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="reg_model", model=model, __log=False)
+
+        # Assign predictions first
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Test assign_scores with regression metrics
+        vm_dataset.assign_scores(vm_model, ["MeanSquaredError", "RSquaredScore"])
+
+        # Check that both metric columns were added
+        expected_columns = ["reg_model_MeanSquaredError", "reg_model_RSquaredScore"]
+        for column in expected_columns:
+            self.assertTrue(column in vm_dataset.df.columns)
+
+        # Verify R-squared is reasonable (can be negative, but typically between -1 and 1 for reasonable models)
+        r2_values = vm_dataset.df["reg_model_RSquaredScore"]
+        r2_value = r2_values.iloc[0]
+        self.assertTrue(-2 <= r2_value <= 1, f"R-squared should be reasonable, got {r2_value}")
+
+        # Verify MSE is non-negative
+        mse_values = vm_dataset.df["reg_model_MeanSquaredError"]
+        mse_value = mse_values.iloc[0]
+        self.assertTrue(mse_value >= 0, f"MSE should be non-negative, got {mse_value}")
+
+    def test_assign_scores_no_model_input_id(self):
+        """
+        Test that assign_scores raises error when model has no input_id
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Create model without input_id
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(model=model, __log=False)  # No input_id provided
+
+        # Clear the input_id to test the error case
+        vm_model.input_id = None
+
+        # Should raise ValueError
+        with self.assertRaises(ValueError) as context:
+            vm_dataset.assign_scores(vm_model, "F1")
+
+        self.assertIn("Model input_id must be set", str(context.exception))
+
+    def test_assign_scores_invalid_metric(self):
+        """
+        Test that assign_scores raises error for invalid metric
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="test_model", model=model, __log=False)
+
+        # Assign predictions first
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Should raise ValueError for invalid metric
+        with self.assertRaises(ValueError) as context:
+            vm_dataset.assign_scores(vm_model, "InvalidMetricName")
+
+        self.assertIn("Metric 'InvalidMetricName' not found", str(context.exception))
+
+    def test_assign_scores_no_predictions(self):
+        """
+        Test that assign_scores raises error when predictions haven't been assigned yet
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="test_model", model=model, __log=False)
+
+        # Don't assign predictions - test that assign_scores raises error
+        # (unit metrics require predictions to be available)
+        with self.assertRaises(ValueError) as context:
+            vm_dataset.assign_scores(vm_model, "F1")
+
+        self.assertIn("No prediction column found", str(context.exception))
+
+    def test_assign_scores_column_naming_convention(self):
+        """
+        Test that assign_scores follows the correct column naming convention
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train a simple model
+        model = LogisticRegression()
+        model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_model = init_model(input_id="my_special_model", model=model, __log=False)
+
+        # Assign predictions first
+        vm_dataset.assign_predictions(model=vm_model)
+
+        # Test multiple metrics to verify naming convention
+        metrics = ["F1", "Precision", "Recall"]
+        vm_dataset.assign_scores(vm_model, metrics)
+
+        # Verify all columns follow the naming convention: {model.input_id}_{metric_name}
+        for metric in metrics:
+            expected_column = f"my_special_model_{metric}"
+            self.assertTrue(expected_column in vm_dataset.df.columns,
+                            f"Expected column '{expected_column}' not found")
+
+    def test_assign_scores_multiple_models(self):
+        """
+        Test assigning scores from multiple models to same dataset
+        """
+        df = pd.DataFrame({"x1": [1, 2, 3], "x2": [4, 5, 6], "y": [0, 1, 0]})
+        vm_dataset = DataFrameDataset(
+            raw_dataset=df, target_column="y", feature_columns=["x1", "x2"]
+        )
+
+        # Train two different models
+        lr_model = LogisticRegression()
+        lr_model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_lr_model = init_model(input_id="lr_model", model=lr_model, __log=False)
+
+        rf_model = RandomForestClassifier(n_estimators=5, random_state=42)
+        rf_model.fit(vm_dataset.x, vm_dataset.y.ravel())
+        vm_rf_model = init_model(input_id="rf_model", model=rf_model, __log=False)
+
+        # Assign predictions for both models
+        vm_dataset.assign_predictions(model=vm_lr_model)
+        vm_dataset.assign_predictions(model=vm_rf_model)
+
+        # Assign scores for both models
+        vm_dataset.assign_scores(vm_lr_model, "F1")
+        vm_dataset.assign_scores(vm_rf_model, "F1")
+
+        # Check that both metric columns exist with correct names
+        lr_column = "lr_model_F1"
+        rf_column = "rf_model_F1"
+
+        self.assertTrue(lr_column in vm_dataset.df.columns)
+        self.assertTrue(rf_column in vm_dataset.df.columns)
+
+        # Verify that the values might be different (different models)
+        lr_f1 = vm_dataset.df[lr_column].iloc[0]
+        rf_f1 = vm_dataset.df[rf_column].iloc[0]
+
+        # Both should be valid F1 scores
+        self.assertTrue(0 <= lr_f1 <= 1)
+        self.assertTrue(0 <= rf_f1 <= 1)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/validmind/__version__.py b/validmind/__version__.py
index 3a5995d32..43ce13db0 100644
--- a/validmind/__version__.py
+++ b/validmind/__version__.py
@@ -1 +1 @@
-__version__ = "2.8.31"
+__version__ = "2.9.0"
diff --git a/validmind/tests/output.py b/validmind/tests/output.py
index 52ee23d1b..760335acb 100644
--- a/validmind/tests/output.py
+++ b/validmind/tests/output.py
@@ -45,7 +45,13 @@ def process(self, item: Any, result: TestResult) -> None:
 
 class MetricOutputHandler(OutputHandler):
     def can_handle(self, item: Any) -> bool:
-        return isinstance(item, (int, float))
+        # Accept individual numbers
+        if isinstance(item, (int, float)):
+            return True
+        # Accept lists/arrays of numbers for per-row metrics
+        if isinstance(item, (list, tuple, np.ndarray)):
+            return all(isinstance(x, (int, float, np.number)) for x in item)
+        return False
 
     def process(self, item: Any, result: TestResult) -> None:
         if result.metric is not None:
diff --git a/validmind/unit_metrics/classification/individual/AbsoluteError.py b/validmind/unit_metrics/classification/individual/AbsoluteError.py
new file mode 100644
index 000000000..403e10657
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/AbsoluteError.py
@@ -0,0 +1,42 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def AbsoluteError(model: VMModel, dataset: VMDataset, **kwargs) -> List[float]:
+    """Calculates the absolute error per row for a classification model.
+
+    For classification tasks, this computes the absolute difference between
+    the true class labels and predicted class labels for each individual row.
+    For binary classification with probabilities, it can also compute the
+    absolute difference between true labels and predicted probabilities.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predictions
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row absolute errors as a list of float values
+    """
+    y_true = dataset.y
+    y_pred = dataset.y_pred(model)
+
+    # Convert to numpy arrays and ensure same data type
+    y_true = np.asarray(y_true)
+    y_pred = np.asarray(y_pred)
+
+    # For classification, compute absolute difference between true and predicted labels
+    absolute_errors = np.abs(y_true - y_pred)
+
+    # Return as a list of floats
+    return absolute_errors.astype(float).tolist()
diff --git a/validmind/unit_metrics/classification/individual/BrierScore.py b/validmind/unit_metrics/classification/individual/BrierScore.py
new file mode 100644
index 000000000..279cfa500
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/BrierScore.py
@@ -0,0 +1,56 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def BrierScore(model: VMModel, dataset: VMDataset, **kwargs) -> List[float]:
+    """Calculates the Brier score per row for a classification model.
+
+    The Brier score is a proper score function that measures the accuracy of
+    probabilistic predictions. It is calculated as the mean squared difference
+    between predicted probabilities and the actual binary outcomes.
+    Lower scores indicate better calibration.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predicted probabilities
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row Brier scores as a list of float values
+
+    Raises:
+        ValueError: If probability column is not found for the model
+    """
+    y_true = dataset.y
+
+    # Try to get probabilities
+    try:
+        y_prob = dataset.y_prob(model)
+        # For binary classification, use the positive class probability
+        if y_prob.ndim > 1 and y_prob.shape[1] > 1:
+            y_prob = y_prob[:, 1]  # Use probability of positive class
+    except ValueError:
+        # Fall back to predictions if probabilities not available
+        # Convert predictions to "probabilities" (1.0 for predicted class, 0.0 for other)
+        y_pred = dataset.y_pred(model)
+        y_prob = y_pred.astype(float)
+
+    # Convert to numpy arrays and ensure same data type
+    y_true = np.asarray(y_true, dtype=float)
+    y_prob = np.asarray(y_prob, dtype=float)
+
+    # Calculate Brier score per row: (predicted_probability - actual_outcome)²
+    brier_scores = (y_prob - y_true) ** 2
+
+    # Return as a list of floats
+    return brier_scores.tolist()
diff --git a/validmind/unit_metrics/classification/individual/CalibrationError.py b/validmind/unit_metrics/classification/individual/CalibrationError.py
new file mode 100644
index 000000000..ba05c83fc
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/CalibrationError.py
@@ -0,0 +1,77 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def CalibrationError(
+    model: VMModel, dataset: VMDataset, n_bins: int = 10, **kwargs
+) -> List[float]:
+    """Calculates the calibration error per row for a classification model.
+
+    Calibration error measures how well the predicted probabilities reflect the
+    actual likelihood of the positive class. For each prediction, this computes
+    the absolute difference between the predicted probability and the empirical
+    frequency of the positive class in the corresponding probability bin.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predicted probabilities
+        n_bins: Number of bins for probability calibration, defaults to 10
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row calibration errors as a list of float values
+
+    Raises:
+        ValueError: If probability column is not found for the model
+    """
+    y_true = dataset.y
+
+    # Try to get probabilities
+    try:
+        y_prob = dataset.y_prob(model)
+        # For binary classification, use the positive class probability
+        if y_prob.ndim > 1 and y_prob.shape[1] > 1:
+            y_prob = y_prob[:, 1]  # Use probability of positive class
+    except ValueError:
+        # If no probabilities available, return zeros (perfect calibration for hard predictions)
+        return [0.0] * len(y_true)
+
+    # Convert to numpy arrays
+    y_true = np.asarray(y_true, dtype=float)
+    y_prob = np.asarray(y_prob, dtype=float)
+
+    # Create probability bins
+    bin_boundaries = np.linspace(0, 1, n_bins + 1)
+    bin_lowers = bin_boundaries[:-1]
+    bin_uppers = bin_boundaries[1:]
+
+    # Calculate calibration error for each sample
+    calibration_errors = np.zeros_like(y_prob)
+
+    for bin_lower, bin_upper in zip(bin_lowers, bin_uppers):
+        # Find samples in this bin
+        in_bin = (y_prob > bin_lower) & (y_prob <= bin_upper)
+        if not np.any(in_bin):
+            continue
+
+        # Calculate empirical frequency for this bin
+        empirical_freq = np.mean(y_true[in_bin])
+
+        # Calculate average predicted probability for this bin
+        avg_predicted_prob = np.mean(y_prob[in_bin])
+
+        # Assign calibration error to all samples in this bin
+        calibration_errors[in_bin] = abs(avg_predicted_prob - empirical_freq)
+
+    # Return as a list of floats
+    return calibration_errors.tolist()
diff --git a/validmind/unit_metrics/classification/individual/ClassBalance.py b/validmind/unit_metrics/classification/individual/ClassBalance.py
new file mode 100644
index 000000000..1c38da453
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/ClassBalance.py
@@ -0,0 +1,65 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def ClassBalance(model: VMModel, dataset: VMDataset, **kwargs) -> List[float]:
+    """Calculates the class balance score per row for a classification model.
+
+    For each prediction, this returns how balanced the predicted class is in the
+    training distribution. Lower scores indicate predictions on rare classes,
+    higher scores indicate predictions on common classes. This helps understand
+    if model errors are more likely on imbalanced classes.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predictions
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row class balance scores as a list of float values
+
+    Note:
+        Scores range from 0 to 0.5, where 0.5 indicates perfectly balanced classes
+        and lower values indicate more imbalanced classes.
+    """
+    y_true = dataset.y
+    y_pred = dataset.y_pred(model)
+
+    # Convert to numpy arrays
+    y_true = np.asarray(y_true)
+    y_pred = np.asarray(y_pred)
+
+    # Calculate class frequencies in the true labels (proxy for training distribution)
+    unique_classes, class_counts = np.unique(y_true, return_counts=True)
+    class_frequencies = class_counts / len(y_true)
+
+    # Create a mapping from class to frequency
+    class_to_freq = dict(zip(unique_classes, class_frequencies))
+
+    # Calculate balance score for each prediction
+    balance_scores = []
+
+    for pred in y_pred:
+        if pred in class_to_freq:
+            freq = class_to_freq[pred]
+            # Balance score: how close to 0.5 (perfectly balanced) the frequency is
+            # Score = 0.5 - |freq - 0.5| = min(freq, 1-freq)
+            balance_score = min(freq, 1 - freq)
+        else:
+            # Predicted class not seen in true labels (very rare)
+            balance_score = 0.0
+
+        balance_scores.append(balance_score)
+
+    # Return as a list of floats
+    return balance_scores
diff --git a/validmind/unit_metrics/classification/individual/Confidence.py b/validmind/unit_metrics/classification/individual/Confidence.py
new file mode 100644
index 000000000..a60394525
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/Confidence.py
@@ -0,0 +1,52 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def Confidence(model: VMModel, dataset: VMDataset, **kwargs) -> List[float]:
+    """Calculates the prediction confidence per row for a classification model.
+
+    For binary classification, confidence is calculated as the maximum probability
+    across classes, or alternatively as the distance from the decision boundary (0.5).
+    Higher values indicate more confident predictions.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predicted probabilities
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row confidence scores as a list of float values
+
+    Raises:
+        ValueError: If probability column is not found for the model
+    """
+    # Try to get probabilities, fall back to predictions if not available
+    try:
+        y_prob = dataset.y_prob(model)
+        # For binary classification, use max probability approach
+        if y_prob.ndim > 1 and y_prob.shape[1] > 1:
+            # Multi-class: confidence is the maximum probability
+            confidence = np.max(y_prob, axis=1)
+        else:
+            # Binary classification: confidence based on distance from 0.5
+            y_prob = np.asarray(y_prob, dtype=float)
+            confidence = np.abs(y_prob - 0.5) + 0.5
+    except ValueError:
+        # Fall back to binary correctness if probabilities not available
+        y_true = dataset.y
+        y_pred = dataset.y_pred(model)
+        # If no probabilities, confidence is 1.0 for correct, 0.0 for incorrect
+        confidence = (y_true == y_pred).astype(float)
+
+    # Return as a list of floats
+    return confidence.tolist()
diff --git a/validmind/unit_metrics/classification/individual/Correctness.py b/validmind/unit_metrics/classification/individual/Correctness.py
new file mode 100644
index 000000000..81d45368c
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/Correctness.py
@@ -0,0 +1,41 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def Correctness(model: VMModel, dataset: VMDataset, **kwargs) -> List[int]:
+    """Calculates the correctness per row for a classification model.
+
+    For classification tasks, this returns 1 for correctly classified rows
+    and 0 for incorrectly classified rows. This provides a binary indicator
+    of model performance for each individual prediction.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predictions
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[int]: Per-row correctness as a list of 1s and 0s
+    """
+    y_true = dataset.y
+    y_pred = dataset.y_pred(model)
+
+    # Convert to numpy arrays
+    y_true = np.asarray(y_true)
+    y_pred = np.asarray(y_pred)
+
+    # For classification, check if predictions match true labels
+    correctness = (y_true == y_pred).astype(int)
+
+    # Return as a list of integers
+    return correctness.tolist()
diff --git a/validmind/unit_metrics/classification/individual/LogLoss.py b/validmind/unit_metrics/classification/individual/LogLoss.py
new file mode 100644
index 000000000..9a9b61a9b
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/LogLoss.py
@@ -0,0 +1,61 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def LogLoss(
+    model: VMModel, dataset: VMDataset, eps: float = 1e-15, **kwargs
+) -> List[float]:
+    """Calculates the logarithmic loss per row for a classification model.
+
+    Log loss measures the performance of a classification model where the prediction
+    is a probability value between 0 and 1. The log loss increases as the predicted
+    probability diverges from the actual label.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predicted probabilities
+        eps: Small value to avoid log(0), defaults to 1e-15
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row log loss values as a list of float values
+
+    Raises:
+        ValueError: If probability column is not found for the model
+    """
+    y_true = dataset.y
+
+    # Try to get probabilities
+    try:
+        y_prob = dataset.y_prob(model)
+        # For binary classification, use the positive class probability
+        if y_prob.ndim > 1 and y_prob.shape[1] > 1:
+            y_prob = y_prob[:, 1]  # Use probability of positive class
+    except ValueError:
+        # Fall back to predictions if probabilities not available
+        # Convert predictions to "probabilities" (0.99 for correct class, 0.01 for wrong)
+        y_pred = dataset.y_pred(model)
+        y_prob = np.where(y_true == y_pred, 0.99, 0.01)
+
+    # Convert to numpy arrays and ensure same data type
+    y_true = np.asarray(y_true, dtype=float)
+    y_prob = np.asarray(y_prob, dtype=float)
+
+    # Clip probabilities to avoid log(0) and log(1)
+    y_prob = np.clip(y_prob, eps, 1 - eps)
+
+    # Calculate log loss per row: -[y*log(p) + (1-y)*log(1-p)]
+    log_loss_per_row = -(y_true * np.log(y_prob) + (1 - y_true) * np.log(1 - y_prob))
+
+    # Return as a list of floats
+    return log_loss_per_row.tolist()
diff --git a/validmind/unit_metrics/classification/individual/OutlierScore.py b/validmind/unit_metrics/classification/individual/OutlierScore.py
new file mode 100644
index 000000000..1e54fbc38
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/OutlierScore.py
@@ -0,0 +1,86 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import StandardScaler
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def OutlierScore(
+    model: VMModel, dataset: VMDataset, contamination: float = 0.1, **kwargs
+) -> List[float]:
+    """Calculates the outlier score per row for a classification model.
+
+    Uses Isolation Forest to identify samples that deviate significantly from
+    the typical patterns in the feature space. Higher scores indicate more
+    anomalous/outlier-like samples. This can help identify out-of-distribution
+    samples or data points that might be harder to predict accurately.
+
+    Args:
+        model: The classification model to evaluate (unused but kept for consistency)
+        dataset: The dataset containing feature data
+        contamination: Expected proportion of outliers, defaults to 0.1
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row outlier scores as a list of float values
+
+    Note:
+        Scores are normalized to [0, 1] where higher values indicate more outlier-like samples
+    """
+    # Get feature data
+    X = dataset.x_df()
+
+    # Handle case where we have no features or only categorical features
+    if X.empty or X.shape[1] == 0:
+        # Return zero outlier scores if no features available
+        return [0.0] * len(dataset.y)
+
+    # Select only numeric features for outlier detection
+    numeric_features = dataset.feature_columns_numeric
+    if not numeric_features:
+        # If no numeric features, return zero outlier scores
+        return [0.0] * len(dataset.y)
+
+    X_numeric = X[numeric_features]
+
+    # Handle missing values by filling with median
+    X_filled = X_numeric.fillna(X_numeric.median())
+
+    # Standardize features for better outlier detection
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X_filled)
+
+    # Fit Isolation Forest
+    isolation_forest = IsolationForest(
+        contamination=contamination, random_state=42, n_estimators=100
+    )
+
+    # Fit the model on the data
+    isolation_forest.fit(X_scaled)
+
+    # Get anomaly scores (negative values for outliers)
+    anomaly_scores = isolation_forest.decision_function(X_scaled)
+
+    # Convert to outlier scores (0 to 1, where 1 is most outlier-like)
+    # Normalize using min-max scaling
+    min_score = np.min(anomaly_scores)
+    max_score = np.max(anomaly_scores)
+
+    if max_score == min_score:
+        # All samples have same score, no outliers detected
+        outlier_scores = np.zeros_like(anomaly_scores)
+    else:
+        # Invert and normalize: higher values = more outlier-like
+        outlier_scores = (max_score - anomaly_scores) / (max_score - min_score)
+
+    # Return as a list of floats
+    return outlier_scores.tolist()
diff --git a/validmind/unit_metrics/classification/individual/ProbabilityError.py b/validmind/unit_metrics/classification/individual/ProbabilityError.py
new file mode 100644
index 000000000..c96929820
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/ProbabilityError.py
@@ -0,0 +1,54 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def ProbabilityError(model: VMModel, dataset: VMDataset, **kwargs) -> List[float]:
+    """Calculates the probability error per row for a classification model.
+
+    For binary classification tasks, this computes the absolute difference between
+    the true class labels (0 or 1) and the predicted probabilities for each row.
+    This provides insight into how confident the model's predictions are and
+    how far off they are from the actual labels.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predicted probabilities
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row probability errors as a list of float values
+
+    Raises:
+        ValueError: If probability column is not found for the model
+    """
+    y_true = dataset.y
+
+    # Try to get probabilities, fall back to predictions if not available
+    try:
+        y_prob = dataset.y_prob(model)
+        # For binary classification, use the positive class probability
+        if y_prob.ndim > 1 and y_prob.shape[1] > 1:
+            y_prob = y_prob[:, 1]  # Use probability of positive class
+    except ValueError:
+        # Fall back to predictions if probabilities not available
+        y_prob = dataset.y_pred(model)
+
+    # Convert to numpy arrays and ensure same data type
+    y_true = np.asarray(y_true, dtype=float)
+    y_prob = np.asarray(y_prob, dtype=float)
+
+    # Compute absolute difference between true labels and predicted probabilities
+    probability_errors = np.abs(y_true - y_prob)
+
+    # Return as a list of floats
+    return probability_errors.tolist()
diff --git a/validmind/unit_metrics/classification/individual/Uncertainty.py b/validmind/unit_metrics/classification/individual/Uncertainty.py
new file mode 100644
index 000000000..0d28fbac8
--- /dev/null
+++ b/validmind/unit_metrics/classification/individual/Uncertainty.py
@@ -0,0 +1,60 @@
+# Copyright © 2023-2024 ValidMind Inc. All rights reserved.
+# See the LICENSE file in the root of this repository for details.
+# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+
+from typing import List
+
+import numpy as np
+
+from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
+
+
+@tasks("classification")
+@tags("classification")
+def Uncertainty(model: VMModel, dataset: VMDataset, **kwargs) -> List[float]:
+    """Calculates the prediction uncertainty per row for a classification model.
+
+    Uncertainty is measured using the entropy of the predicted probability distribution.
+    Higher entropy indicates higher uncertainty in the prediction. For binary
+    classification, maximum uncertainty occurs at probability 0.5.
+
+    Args:
+        model: The classification model to evaluate
+        dataset: The dataset containing true labels and predicted probabilities
+        **kwargs: Additional parameters (unused for compatibility)
+
+    Returns:
+        List[float]: Per-row uncertainty scores as a list of float values
+
+    Raises:
+        ValueError: If probability column is not found for the model
+    """
+    # Try to get probabilities
+    try:
+        y_prob = dataset.y_prob(model)
+
+        if y_prob.ndim > 1 and y_prob.shape[1] > 1:
+            # Multi-class: calculate entropy across all classes
+            # Clip to avoid log(0)
+            y_prob_clipped = np.clip(y_prob, 1e-15, 1 - 1e-15)
+            # Entropy: -sum(p * log(p))
+            uncertainty = -np.sum(y_prob_clipped * np.log(y_prob_clipped), axis=1)
+        else:
+            # Binary classification: calculate binary entropy
+            y_prob = np.asarray(y_prob, dtype=float)
+            # Clip to avoid log(0)
+            y_prob_clipped = np.clip(y_prob, 1e-15, 1 - 1e-15)
+            # Binary entropy: -[p*log(p) + (1-p)*log(1-p)]
+            uncertainty = -(
+                y_prob_clipped * np.log(y_prob_clipped)
+                + (1 - y_prob_clipped) * np.log(1 - y_prob_clipped)
+            )
+
+    except ValueError:
+        # If no probabilities available, assume zero uncertainty for hard predictions
+        n_samples = len(dataset.y)
+        uncertainty = np.zeros(n_samples)
+
+    # Return as a list of floats
+    return uncertainty.tolist()
diff --git a/validmind/unit_metrics/classification/individual/__init__.py b/validmind/unit_metrics/classification/individual/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/validmind/vm_models/dataset/dataset.py b/validmind/vm_models/dataset/dataset.py
index fea1566d3..9e597ba19 100644
--- a/validmind/vm_models/dataset/dataset.py
+++ b/validmind/vm_models/dataset/dataset.py
@@ -8,7 +8,7 @@
 
 import warnings
 from copy import deepcopy
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -458,6 +458,152 @@ def probability_column(self, model: VMModel, column_name: str = None) -> str:
 
         return self.extra_columns.probability_column(model, column_name)
 
+    def assign_scores(
+        self,
+        model: VMModel,
+        metrics: Union[str, List[str]],
+        **kwargs: Dict[str, Any],
+    ) -> None:
+        """Assign computed unit metric scores to the dataset as new columns.
+
+        This method computes unit metrics for the given model and dataset, then adds
+        the computed scores as new columns to the dataset using the naming convention:
+        {model.input_id}_{metric_name}
+
+        Args:
+            model (VMModel): The model used to compute the scores.
+            metrics (Union[str, List[str]]): Single metric ID or list of metric IDs.
+                Can be either:
+                - Short name (e.g., "F1", "Precision")
+                - Full metric ID (e.g., "validmind.unit_metrics.classification.F1")
+            **kwargs: Additional parameters passed to the unit metrics.
+
+        Examples:
+            # Single metric
+            dataset.assign_scores(model, "F1")
+
+            # Multiple metrics
+            dataset.assign_scores(model, ["F1", "Precision", "Recall"])
+
+            # With parameters
+            dataset.assign_scores(model, "ROC_AUC", average="weighted")
+
+        Raises:
+            ValueError: If the model input_id is None or if metric computation fails.
+            ImportError: If unit_metrics module cannot be imported.
+        """
+        if model.input_id is None:
+            raise ValueError("Model input_id must be set to use assign_scores")
+
+        # Import unit_metrics module
+        try:
+            from validmind.unit_metrics import run_metric
+        except ImportError as e:
+            raise ImportError(
+                f"Failed to import unit_metrics module: {e}. "
+                "Make sure validmind.unit_metrics is available."
+            ) from e
+
+        # Normalize metrics to a list
+        if isinstance(metrics, str):
+            metrics = [metrics]
+
+        # Process each metric
+        for metric in metrics:
+            # Normalize metric ID
+            metric_id = self._normalize_metric_id(metric)
+
+            # Extract metric name for column naming
+            metric_name = self._extract_metric_name(metric_id)
+
+            # Generate column name
+            column_name = f"{model.input_id}_{metric_name}"
+
+            try:
+                # Run the unit metric
+                result = run_metric(
+                    metric_id,
+                    inputs={
+                        "model": model,
+                        "dataset": self,
+                    },
+                    params=kwargs,
+                    show=False,  # Don't show widget output
+                )
+
+                # Extract the metric value
+                metric_value = result.metric
+
+                # Create column values (repeat the scalar value for all rows)
+                if np.isscalar(metric_value):
+                    column_values = np.full(len(self._df), metric_value)
+                else:
+                    if len(metric_value) != len(self._df):
+                        raise ValueError(
+                            f"Metric value length {len(metric_value)} does not match dataset length {len(self._df)}"
+                        )
+                    column_values = metric_value
+
+                # Add the column to the dataset
+                self.add_extra_column(column_name, column_values)
+
+                logger.info(f"Added metric column '{column_name}'")
+            except Exception as e:
+                logger.error(f"Failed to compute metric {metric_id}: {e}")
+                raise ValueError(f"Failed to compute metric {metric_id}: {e}") from e
+
+    def _normalize_metric_id(self, metric: str) -> str:
+        """Normalize metric identifier to full validmind unit metric ID.
+
+        Args:
+            metric (str): Metric identifier (short name or full ID)
+
+        Returns:
+            str: Full metric ID
+        """
+        # If already a full ID, return as-is
+        if metric.startswith("validmind.unit_metrics."):
+            return metric
+
+        # Try to find the metric by short name
+        try:
+            from validmind.unit_metrics import list_metrics
+
+            available_metrics = list_metrics()
+
+            # Look for exact match with short name
+            for metric_id in available_metrics:
+                if metric_id.endswith(f".{metric}"):
+                    return metric_id
+
+            # If no exact match found, raise error with suggestions
+            suggestions = [m for m in available_metrics if metric.lower() in m.lower()]
+            if suggestions:
+                raise ValueError(
+                    f"Metric '{metric}' not found. Did you mean one of: {suggestions[:5]}"
+                )
+            else:
+                raise ValueError(
+                    f"Metric '{metric}' not found. Available metrics: {available_metrics[:10]}..."
+                )
+
+        except ImportError as e:
+            raise ImportError(
+                f"Failed to import unit_metrics for metric lookup: {e}"
+            ) from e
+
+    def _extract_metric_name(self, metric_id: str) -> str:
+        """Extract the metric name from a full metric ID.
+
+        Args:
+            metric_id (str): Full metric ID
+
+        Returns:
+            str: Metric name
+        """
+        # Extract the last part after the final dot
+        return metric_id.split(".")[-1]
+
     def add_extra_column(self, column_name, column_values=None):
         """Adds an extra column to the dataset without modifying the dataset `features` and `target` columns.
 
diff --git a/validmind/vm_models/result/result.py b/validmind/vm_models/result/result.py
index 6edee7bbe..3016012d5 100644
--- a/validmind/vm_models/result/result.py
+++ b/validmind/vm_models/result/result.py
@@ -178,7 +178,7 @@ class TestResult(Result):
     title: Optional[str] = None
     doc: Optional[str] = None
     description: Optional[Union[str, DescriptionFuture]] = None
-    metric: Optional[Union[int, float]] = None
+    metric: Optional[Union[int, float, List[Union[int, float]]]] = None
     tables: Optional[List[ResultTable]] = None
     raw_data: Optional[RawData] = None
     figures: Optional[List[Figure]] = None

From a813197d75079d8f9386241cc8df5b7ea8d38124 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 14:41:07 -0400
Subject: [PATCH 30/47] Update minimum dependency versions for Python 3.9,
 3.10, and 3.11 compatibility

---
 requirements/constraints/py310-min.txt |  6 +++---
 requirements/constraints/py311-min.txt | 12 ++++++------
 requirements/constraints/py39-min.txt  |  4 ++--
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/requirements/constraints/py310-min.txt b/requirements/constraints/py310-min.txt
index 3ba332a76..44e084193 100644
--- a/requirements/constraints/py310-min.txt
+++ b/requirements/constraints/py310-min.txt
@@ -1,14 +1,14 @@
 # Minimum compatible pins for Python 3.10
 pandas==2.0.3
-numpy==1.26.4
-scikit-learn==1.1.0
+numpy==1.24.4
+scikit-learn==1.1.3
 matplotlib==3.5.0
 transformers==4.32.0
 datasets==2.10.0
 nltk==3.8.1
 plotly==5.0.0
 scipy==1.9.0
-statsmodels==0.13.0
+statsmodels==0.13.5
 xgboost==1.5.2
 torch==2.0.1
 sentencepiece==0.2.0
diff --git a/requirements/constraints/py311-min.txt b/requirements/constraints/py311-min.txt
index f6d3f53b9..37e3e4e79 100644
--- a/requirements/constraints/py311-min.txt
+++ b/requirements/constraints/py311-min.txt
@@ -1,14 +1,14 @@
 # Minimum compatible pins for Python 3.11
 pandas==2.0.3
 numpy==1.26.4
-scikit-learn==1.1.0
-matplotlib==3.5.0
+scikit-learn==1.3.2
+matplotlib==3.7.3
 transformers==4.32.0
 datasets==2.10.0
 nltk==3.8.1
-plotly==5.0.0
-scipy==1.9.0
-statsmodels==0.13.0
-xgboost==1.5.2
+plotly==5.15.0
+scipy==1.11.3
+statsmodels==0.14.0
+xgboost==1.7.6
 torch==2.1.0
 sentencepiece==0.2.0
diff --git a/requirements/constraints/py39-min.txt b/requirements/constraints/py39-min.txt
index eac1bc1c5..b84b6cc13 100644
--- a/requirements/constraints/py39-min.txt
+++ b/requirements/constraints/py39-min.txt
@@ -1,7 +1,7 @@
 # Minimum compatible pins for Python 3.9
 pandas==2.0.3
-numpy==1.26.4
-scikit-learn==1.1.0
+numpy==1.24.4
+scikit-learn==1.1.3
 matplotlib==3.5.0
 transformers==4.32.0
 datasets==2.10.0

From 336db41c1a68e805966bfdbb58cdc68f52237ef6 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:14:34 -0400
Subject: [PATCH 31/47] feat: make dependency constraints dynamic

---
 .github/workflows/dependency-testing.yaml | 59 ++++++++++++++++-------
 Makefile                                  | 24 ++-------
 requirements/constraints/py310-min.txt    | 14 ------
 requirements/constraints/py311-min.txt    | 14 ------
 requirements/constraints/py312-min.txt    | 17 -------
 requirements/constraints/py39-min.txt     | 14 ------
 tox.ini                                   | 25 ++++++----
 7 files changed, 60 insertions(+), 107 deletions(-)
 delete mode 100644 requirements/constraints/py310-min.txt
 delete mode 100644 requirements/constraints/py311-min.txt
 delete mode 100644 requirements/constraints/py312-min.txt
 delete mode 100644 requirements/constraints/py39-min.txt

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 9484093d0..fa768a856 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -46,12 +46,6 @@ jobs:
       matrix:
         python-version: ['3.9', '3.10', '3.11', '3.12']
         deps-type: ['min', 'max', 'default']
-        exclude:
-          # Skip some combinations to reduce CI time
-          - python-version: '3.10'
-            deps-type: 'default'
-          - python-version: '3.11'
-            deps-type: 'default'
 
     steps:
       - uses: actions/checkout@v4
@@ -66,24 +60,55 @@ jobs:
           sudo apt-get update
           sudo apt-get install -y build-essential libomp-dev
 
-      - name: Install tox
+      - name: Install build tooling (Poetry and uv)
         run: |
           python -m pip install --upgrade pip
-          pip install tox
+          curl -sSL https://install.python-poetry.org | python3 - --yes
+          echo "$HOME/.local/bin" >> $GITHUB_PATH
+          echo "$HOME/.poetry/bin" >> $GITHUB_PATH
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+
+      - name: Build wheel and sdist
+        run: |
+          poetry --version
+          poetry build
 
-      - name: Run dependency tests
+      - name: Install and test via pip artifacts (${{ matrix.deps-type }})
         run: |
+          set -euxo pipefail
+          # limit threads to avoid resource contention
+          export OMP_NUM_THREADS=1
+          export MKL_NUM_THREADS=1
+
           if [[ "${{ matrix.deps-type }}" == "default" ]]; then
-            tox -e py$(echo ${{ matrix.python-version }} | tr -d .)
+            # Install only from built artifacts (let pip resolve deps normally)
+            pip install dist/*.whl
+            pip check
+            python -m tests.test_unit_tests | cat
           else
-            tox -e py$(echo ${{ matrix.python-version }} | tr -d .)-${{ matrix.deps-type }}
+            # Generate constraints
+            if [[ "${{ matrix.deps-type }}" == "min" ]]; then
+              OUT=constraints-min.txt
+              uv pip compile pyproject.toml --all-extras --resolution lowest --no-emit-index-url --no-annotate --output-file "$OUT"
+            else
+              OUT=constraints-max.txt
+              uv pip compile pyproject.toml --all-extras --no-emit-index-url --no-annotate --output-file "$OUT" --upgrade
+            fi
+            # Install constraints then the wheel without reinstalling deps
+            pip install -r "$OUT"
+            pip install dist/*.whl --no-deps
+            pip check
+            python -m tests.test_unit_tests | cat
           fi
-        env:
-          # Limit OpenMP threads to prevent CI resource issues
-          OMP_NUM_THREADS: 1
-          MKL_NUM_THREADS: 1
-          # Disable pip caching to prevent matrix job conflicts
-          PIP_NO_CACHE_DIR: 1
+
+      - name: Upload generated constraints (if any)
+        if: matrix.deps-type != 'default'
+        uses: actions/upload-artifact@v4
+        with:
+          name: constraints-${{ matrix.python-version }}-${{ matrix.deps-type }}
+          path: |
+            constraints-${{ matrix.deps-type }}.txt
 
   pip-freeze-testing:
     name: Test Client Pip Freeze Environments
diff --git a/Makefile b/Makefile
index 54290ecf4..628b3c2fc 100644
--- a/Makefile
+++ b/Makefile
@@ -56,10 +56,10 @@ quarto-docs:
 	rm -f docs/validmind.json
 	rm -rf docs/validmind
 	mkdir -p docs/validmind
-	
+
 	# Generate API JSON dump
 	poetry run python -m griffe dump validmind -f -o docs/validmind.json -d google -r -U
-	
+
 	# Generate Quarto docs from templates
 	poetry run python scripts/generate_quarto_docs.py
 
@@ -87,25 +87,7 @@ verify-exposed-credentials:
 ensure-clean-notebooks:
 	poetry run python scripts/ensure_clean_notebooks.py
 
-# Dependency testing with tox
-test-deps-min:
-	poetry run tox -e py39-min,py310-min,py311-min,py312-min
-
-test-deps-max:
-	poetry run tox -e py39-max,py310-max,py311-max,py312-max
-
-test-python-versions:
-	poetry run tox -e py39,py310,py311,py312
-
-test-freeze-env:
-ifdef FREEZE_FILE
-	FREEZE_FILE=$(FREEZE_FILE) poetry run tox -e freeze
-else
-	@echo "Usage: make test-freeze-env FREEZE_FILE=path/to/requirements.txt"
-endif
-
-test-tox-all:
-	poetry run tox
+# Dependency testing moved to GitHub Actions using built artifacts and dynamic constraints
 
 # Quick target to run all checks
 check: copyright format lint test verify-copyright verify-exposed-credentials ensure-clean-notebooks
diff --git a/requirements/constraints/py310-min.txt b/requirements/constraints/py310-min.txt
deleted file mode 100644
index 44e084193..000000000
--- a/requirements/constraints/py310-min.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Minimum compatible pins for Python 3.10
-pandas==2.0.3
-numpy==1.24.4
-scikit-learn==1.1.3
-matplotlib==3.5.0
-transformers==4.32.0
-datasets==2.10.0
-nltk==3.8.1
-plotly==5.0.0
-scipy==1.9.0
-statsmodels==0.13.5
-xgboost==1.5.2
-torch==2.0.1
-sentencepiece==0.2.0
diff --git a/requirements/constraints/py311-min.txt b/requirements/constraints/py311-min.txt
deleted file mode 100644
index 37e3e4e79..000000000
--- a/requirements/constraints/py311-min.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Minimum compatible pins for Python 3.11
-pandas==2.0.3
-numpy==1.26.4
-scikit-learn==1.3.2
-matplotlib==3.7.3
-transformers==4.32.0
-datasets==2.10.0
-nltk==3.8.1
-plotly==5.15.0
-scipy==1.11.3
-statsmodels==0.14.0
-xgboost==1.7.6
-torch==2.1.0
-sentencepiece==0.2.0
diff --git a/requirements/constraints/py312-min.txt b/requirements/constraints/py312-min.txt
deleted file mode 100644
index 81d02730c..000000000
--- a/requirements/constraints/py312-min.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-# Minimum compatible pins for Python 3.12
-# Avoid packages that don't yet have Py3.12 wheels at older pins.
-# Use the oldest working set known to pass for 3.12.
-pandas==2.1.4
-numpy==1.26.4
-scikit-learn==1.3.0
-matplotlib==3.8.0
-transformers==4.35.0
-datasets==2.14.0
-nltk==3.8.1
-plotly==5.18.0
-scipy==1.11.3
-statsmodels==0.14.0
-xgboost==2.0.3
-# torch min for 3.12 typically 2.2+
-torch==2.2.2
-sentencepiece==0.2.0
diff --git a/requirements/constraints/py39-min.txt b/requirements/constraints/py39-min.txt
deleted file mode 100644
index b84b6cc13..000000000
--- a/requirements/constraints/py39-min.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Minimum compatible pins for Python 3.9
-pandas==2.0.3
-numpy==1.24.4
-scikit-learn==1.1.3
-matplotlib==3.5.0
-transformers==4.32.0
-datasets==2.10.0
-nltk==3.8.1
-plotly==5.0.0
-scipy==1.9.0
-statsmodels==0.13.0
-xgboost==1.5.2
-torch==2.0.1
-sentencepiece==0.2.0
diff --git a/tox.ini b/tox.ini
index e8a0be5ee..d03737095 100644
--- a/tox.ini
+++ b/tox.ini
@@ -25,7 +25,8 @@ commands_pre =
     poetry install --all-extras
 commands =
     poetry run python -m unittest discover tests
-    poetry run python -m unittest tests.test_unit_tests
+    # Run the custom unit test orchestrator directly to avoid unittest exit code 5 (no tests collected)
+    poetry run python -m tests.test_unit_tests
 
 [testenv:py39-min]
 description = Test with minimum supported dependency versions (Python 3.9)
@@ -34,7 +35,8 @@ commands_pre =
     pip install poetry
     poetry config virtualenvs.create false
     poetry install
-    pip install -r requirements/constraints/py39-min.txt
+    python scripts/generate_constraints.py --profile min
+    pip install -r requirements/constraints/generated-min.txt
 
 [testenv:py310-min]
 description = Test with minimum supported dependency versions (Python 3.10)
@@ -43,7 +45,8 @@ commands_pre =
     pip install poetry
     poetry config virtualenvs.create false
     poetry install
-    pip install -r requirements/constraints/py310-min.txt
+    python scripts/generate_constraints.py --profile min
+    pip install -r requirements/constraints/generated-min.txt
 
 [testenv:py311-min]
 description = Test with minimum supported dependency versions (Python 3.11)
@@ -52,7 +55,8 @@ commands_pre =
     pip install poetry
     poetry config virtualenvs.create false
     poetry install
-    pip install -r requirements/constraints/py311-min.txt
+    python scripts/generate_constraints.py --profile min
+    pip install -r requirements/constraints/generated-min.txt
 
 [testenv:py312-min]
 description = Test with minimum supported dependency versions (Python 3.12)
@@ -61,7 +65,8 @@ commands_pre =
     pip install poetry
     poetry config virtualenvs.create false
     poetry install
-    pip install -r requirements/constraints/py312-min.txt
+    python scripts/generate_constraints.py --profile min
+    pip install -r requirements/constraints/generated-min.txt
 
 [testenv:py{39,310,311,312}-max]
 description = Test with latest dependency versions
@@ -69,11 +74,11 @@ commands_pre =
     python -m pip install --upgrade pip
     pip install poetry
     poetry config virtualenvs.create false
-    poetry install --all-extras
-    # Upgrade to latest versions of key dependencies
-    # Temporarily cap numpy to <2.0 to avoid ecosystem breakages in deps (numba, shap, etc.)
-    pip install --upgrade 'numpy<2.0' pandas scikit-learn matplotlib transformers
-    pip install --upgrade torch datasets xgboost nltk plotly scipy statsmodels
+    # Dynamically generate "max" constraints (latest compatible)
+    python scripts/generate_constraints.py --profile max
+    pip install -r requirements/constraints/generated-max.txt
+    # Install project without resolving deps again (use constraints already installed)
+    poetry install --no-deps
 
 [testenv:docs]
 commands_pre =

From 9a4aca3c390a05431cee9609b73b8b7d745481c5 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:16:38 -0400
Subject: [PATCH 32/47] chore: remove min testing target

---
 .github/workflows/dependency-testing.yaml | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index fa768a856..997681d06 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -45,7 +45,7 @@ jobs:
       fail-fast: false
       matrix:
         python-version: ['3.9', '3.10', '3.11', '3.12']
-        deps-type: ['min', 'max', 'default']
+        deps-type: ['max', 'default']
 
     steps:
       - uses: actions/checkout@v4
@@ -87,14 +87,9 @@ jobs:
             pip check
             python -m tests.test_unit_tests | cat
           else
-            # Generate constraints
-            if [[ "${{ matrix.deps-type }}" == "min" ]]; then
-              OUT=constraints-min.txt
-              uv pip compile pyproject.toml --all-extras --resolution lowest --no-emit-index-url --no-annotate --output-file "$OUT"
-            else
-              OUT=constraints-max.txt
-              uv pip compile pyproject.toml --all-extras --no-emit-index-url --no-annotate --output-file "$OUT" --upgrade
-            fi
+            # Generate latest-compatible constraints (max)
+            OUT=constraints-max.txt
+            uv pip compile pyproject.toml --all-extras --no-emit-index-url --no-annotate --output-file "$OUT" --upgrade
             # Install constraints then the wheel without reinstalling deps
             pip install -r "$OUT"
             pip install dist/*.whl --no-deps
@@ -103,12 +98,12 @@ jobs:
           fi
 
       - name: Upload generated constraints (if any)
-        if: matrix.deps-type != 'default'
+        if: matrix.deps-type == 'max'
         uses: actions/upload-artifact@v4
         with:
-          name: constraints-${{ matrix.python-version }}-${{ matrix.deps-type }}
+          name: constraints-${{ matrix.python-version }}-max
           path: |
-            constraints-${{ matrix.deps-type }}.txt
+            constraints-max.txt
 
   pip-freeze-testing:
     name: Test Client Pip Freeze Environments

From c6d096e0d302e60ec144f4ef61ce8edd854f8b5c Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:21:00 -0400
Subject: [PATCH 33/47] chore: add beautifulsoup4 as a dependency for enhanced
 HTML parsing

---
 .github/workflows/dependency-testing.yaml | 1 +
 pyproject.toml                            | 1 +
 2 files changed, 2 insertions(+)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 997681d06..2c9aa222a 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -83,6 +83,7 @@ jobs:
 
           if [[ "${{ matrix.deps-type }}" == "default" ]]; then
             # Install only from built artifacts (let pip resolve deps normally)
+            pip install beautifulsoup4
             pip install dist/*.whl
             pip check
             python -m tests.test_unit_tests | cat
diff --git a/pyproject.toml b/pyproject.toml
index 8d5395757..ab2ed3fde 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ tqdm = "*"
 transformers = { version = "^4.32.0", optional = true }
 xgboost = { version = ">=1.5.2,<3", optional = true }
 anywidget = "*"
+beautifulsoup4 = "*"
 
 [tool.poetry.group.dev.dependencies]
 black = "^22.1.0"

From fd3d9ba28138d641183d5485bab6f57c65ea588f Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:26:54 -0400
Subject: [PATCH 34/47] chore: update poetry.lock and pyproject.toml to newer
 spec

---
 poetry.lock    |  87 +++++++++++++++++++++++++---
 pyproject.toml | 154 +++++++++++++++++++++++++++----------------------
 2 files changed, 162 insertions(+), 79 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index d67646e49..815aa4742 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -483,7 +483,7 @@ version = "4.13.4"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.7.0"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"},
     {file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"},
@@ -2276,7 +2276,7 @@ files = [
     {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"},
     {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"},
 ]
-markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.10\""}
+markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version == \"3.9\""}
 
 [package.dependencies]
 zipp = ">=3.20"
@@ -2297,7 +2297,7 @@ description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.10\""
+markers = "python_version == \"3.9\""
 files = [
     {file = "importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec"},
     {file = "importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c"},
@@ -5784,6 +5784,75 @@ cffi = ">=1.5.0"
 [package.extras]
 idna = ["idna (>=2.1)"]
 
+[[package]]
+name = "pycocoevalcap"
+version = "1.2"
+description = "MS-COCO Caption Evaluation for Python 3"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\""
+files = [
+    {file = "pycocoevalcap-1.2-py3-none-any.whl", hash = "sha256:083ed7910f1aec000b0a237ef6665f74edf19954204d0b1cbdb8399ed132228d"},
+    {file = "pycocoevalcap-1.2.tar.gz", hash = "sha256:7857f4d596ca2fa0b1a9a3c2067588a4257556077b7ad614d00b2b7b8f57cdde"},
+]
+
+[package.dependencies]
+pycocotools = ">=2.0.2"
+
+[[package]]
+name = "pycocotools"
+version = "2.0.10"
+description = "Official APIs for the MS-COCO dataset"
+optional = true
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\""
+files = [
+    {file = "pycocotools-2.0.10-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:94d558e6a4b92620dad1684b74b6c1404e20d5ed3b4f3aed64ad817d5dd46c72"},
+    {file = "pycocotools-2.0.10-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c4d61959f505f1333afd1666ece1a9f8dad318de160c56c7d03f22d7b5556478"},
+    {file = "pycocotools-2.0.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0bb54826c5d3b651597ec15ae5f4226b727159ec7798af81aa3895f734518993"},
+    {file = "pycocotools-2.0.10-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:9d3b4d0aa38c76153ec244f17939bbc65d24b6a119eb99184f7f636421ef0d8a"},
+    {file = "pycocotools-2.0.10-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:714dda1fccc3a9a1f10893530df6e927678daf6c49bc8a932d7ec2042e9a11f2"},
+    {file = "pycocotools-2.0.10-cp310-cp310-win_amd64.whl", hash = "sha256:8b4f26d44dde3e0b1e3df3ddcc7e27560e52dfe53db708c26af22a57e8ea3d47"},
+    {file = "pycocotools-2.0.10-cp310-cp310-win_arm64.whl", hash = "sha256:16836530552d6ce5e7f1cbcdfe6ead94c0cee71d61bfa3e3c832aef57d21c027"},
+    {file = "pycocotools-2.0.10-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:68846da0ee3ea82d71bcbd99ed28271633a67a899cfbacd2ef309b2e455524b2"},
+    {file = "pycocotools-2.0.10-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:20831839a771d4bc60a814e7b54a92d9a45a773dee47959d30888d00066059c3"},
+    {file = "pycocotools-2.0.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1760c10459dfb4229e7436ae380228428efb0115bbe332a51b72d07fa085d8c0"},
+    {file = "pycocotools-2.0.10-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:5146bc881f380e8fb493e49216083298e4a06f778841f8b9b1d45b21e211d0e4"},
+    {file = "pycocotools-2.0.10-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:23f7d0c551d4c31cab629ce177186db9562f10414320add5267707a84cf6cdfa"},
+    {file = "pycocotools-2.0.10-cp311-cp311-win_amd64.whl", hash = "sha256:03c3aacec2a6aa5171016303a539d07a7b22a34557456eadf0eb40853bdd813e"},
+    {file = "pycocotools-2.0.10-cp311-cp311-win_arm64.whl", hash = "sha256:1f942352b1ab11b9732443ab832cbe5836441f4ec30e1f61b44e1421dbb0a0f5"},
+    {file = "pycocotools-2.0.10-cp312-abi3-macosx_10_13_universal2.whl", hash = "sha256:e1359f556986c8c4ac996bf8e473ff891d87630491357aaabd12601687af5edb"},
+    {file = "pycocotools-2.0.10-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:075788c90bfa6a8989d628932854f3e32c25dac3c1bf7c1183cefad29aee16c8"},
+    {file = "pycocotools-2.0.10-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4539d8b29230de042f574012edd0b5227528da083c4f12bbd6488567aabd3920"},
+    {file = "pycocotools-2.0.10-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:da7b339624d0f78aa5bdc1c86a53f2dcb36ae7e10ab5fe45ba69878bb7837c7a"},
+    {file = "pycocotools-2.0.10-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:ffdbf8810f27b32c5c5c85d9cd65e8e066852fef9775e58a7b23abdffeaf8252"},
+    {file = "pycocotools-2.0.10-cp312-abi3-win_amd64.whl", hash = "sha256:998a88f90bb663548e767470181175343d406b6673b8b9ef5bdbb3a6d3eb3b11"},
+    {file = "pycocotools-2.0.10-cp312-abi3-win_arm64.whl", hash = "sha256:76cd86a80171f8f7da3250be0e40d75084f1f1505d376ae0d08ed0be1ba8a90d"},
+    {file = "pycocotools-2.0.10-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:df7796ec8b9e32879028f929b77968039ca7ced7ecdad23147da55f144e753c8"},
+    {file = "pycocotools-2.0.10-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9d76ab632494f5dd8578230e5123e595446389598e0832a86f3dc8d7f236c3e5"},
+    {file = "pycocotools-2.0.10-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b165aaa9d435571ce34cdb5fae9d47cfe923db2c687362c2607c1e5f1a7ffa8"},
+    {file = "pycocotools-2.0.10-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5faf8bb60228c44fb171eb0674ae31d72a82bcc0d099c0fececfe7cae49010f3"},
+    {file = "pycocotools-2.0.10-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:63c8aa107c96f19634ec9795c9c34d563c7da45009a342ca7ad36070d82792e1"},
+    {file = "pycocotools-2.0.10-cp313-cp313t-win_amd64.whl", hash = "sha256:d1fcf39acdee901de7665b1853e4f79f7a8c2f88eb100a9c24229a255c9efc59"},
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+    {file = "pycocotools-2.0.10-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a65983dbad84f93421a73c7f1365c588babd2b8bdcaaf3d1351e448ee1a01ccc"},
+    {file = "pycocotools-2.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87ba2710752372b5ec14cf446cf9a0a3a8069f461595991b977e7caf5a2788af"},
+    {file = "pycocotools-2.0.10-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:c415f00fa0d60db20e8ff0f3c641b32ed0910c6102f719b46918bbd139ab5692"},
+    {file = "pycocotools-2.0.10-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:cd777096a06d6aa560362bc51df084640d31c6e2ea69fe03d03dd185bbc27173"},
+    {file = "pycocotools-2.0.10-cp39-cp39-win_amd64.whl", hash = "sha256:2daf190f33e7befb353d3b5a023678d6c10c04b51d86386d0d8b353198e25e0c"},
+    {file = "pycocotools-2.0.10-cp39-cp39-win_arm64.whl", hash = "sha256:9e31c95d85ee5b36ea5a2f945565ea797301d3a46c2dd3ec35d427edf4e2f5e6"},
+    {file = "pycocotools-2.0.10.tar.gz", hash = "sha256:7a47609cdefc95e5e151313c7d93a61cf06e15d42c7ba99b601e3bc0f9ece2e1"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+all = ["matplotlib (>=2.1.0)"]
+
 [[package]]
 name = "pycodestyle"
 version = "2.8.0"
@@ -7400,7 +7469,7 @@ version = "2.7"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "soupsieve-2.7-py3-none-any.whl", hash = "sha256:6e60cc5c1ffaf1cebcc12e8188320b72071e922c2e897f737cadce79ad5d30c4"},
     {file = "soupsieve-2.7.tar.gz", hash = "sha256:ad282f9b6926286d2ead4750552c8a6142bc4c783fd66b0293547c8fe6ae126a"},
@@ -8807,7 +8876,7 @@ files = [
     {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
     {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
 ]
-markers = {main = "python_version < \"3.10\""}
+markers = {main = "python_version == \"3.9\""}
 
 [package.extras]
 check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
@@ -8932,14 +9001,14 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["langchain-openai", "ragas", "scipy", "sentencepiece", "statsmodels", "torch", "transformers", "xgboost"]
+all = ["langchain-openai", "pycocoevalcap", "ragas", "scipy", "sentencepiece", "statsmodels", "torch", "transformers", "xgboost"]
 huggingface = ["sentencepiece", "transformers"]
-llm = ["langchain-openai", "ragas", "sentencepiece", "torch", "transformers"]
+llm = ["langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
 pytorch = ["torch"]
 stats = ["scipy", "statsmodels"]
 xgboost = ["xgboost"]
 
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.9.0,<3.13"
-content-hash = "cda95f44d58295e26d61fb22f52bb0cc8716913a7501bfac40b1649c381624e0"
+python-versions = ">=3.9,<3.13"
+content-hash = "9ac390e008af3e0f2463277917935d5b981e16e3ecd4a1fae3aa3c650a2c9c4a"
diff --git a/pyproject.toml b/pyproject.toml
index ab2ed3fde..0ef9550a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,47 +12,89 @@ name = "validmind"
 readme = "README.pypi.md"
 version = "2.9.0"
 
-[tool.poetry.dependencies]
-aiohttp = { extras = ["speedups"], version = "*" }
-arch = "*"
-bert-score = ">=0.3.13"
-catboost = "*"
-datasets = "^2.10.0"
-evaluate = "*"
-ipywidgets = "*"
-kaleido = ">=0.2.1,!=0.2.1.post1,<1.0.0"
-langchain-openai = { version = ">=0.1.8", optional = true }
-langdetect = "*"
-matplotlib = "*"
-mistune = "^3.0.2"
-nest-asyncio = "^1.6.0"
-nltk = "^3.8.1"
-numba = "*"                                                 # TODO: https://github.com/validmind/validmind-library/pull/28
-openai = ">=1"
-pandas = "^2.0.3"
-plotly = ">=5.0.0"
-polars = "*"
-python = ">=3.9.0,<3.13"
-python-dotenv = "*"
-ragas = { version = ">=0.2.3,<=0.2.7", optional = true }
-rouge = ">=1"
-scikit-learn = "*"
-scipy = { version = "*", optional = true }
-scorecardpy = "^0.1.9.6"
-seaborn = "*"
-sentencepiece = { version = "^0.2.0", optional = true }
-sentry-sdk = "^1.24.0"
-shap = ">=0.46.0"
-statsmodels = { version = "*", optional = true }
-tabulate = "^0.8.9"
-textblob = "^0.18.0.post0"
-tiktoken = "*"
-torch = { version = ">=2.0.0", optional = true }
-tqdm = "*"
-transformers = { version = "^4.32.0", optional = true }
-xgboost = { version = ">=1.5.2,<3", optional = true }
-anywidget = "*"
-beautifulsoup4 = "*"
+[project]
+name = "validmind"
+version = "2.9.0"
+description = "ValidMind Library"
+readme = "README.pypi.md"
+requires-python = ">=3.9,<3.13"
+license = { file = "LICENSE" }
+authors = [
+  { name = "Andres Rodriguez", email = "andres@validmind.ai" },
+  { name = "Juan Martinez", email = "juan@validmind.ai" },
+  { name = "Anil Sorathiya", email = "anil@validmind.ai" },
+  { name = "Luis Pallares", email = "luis@validmind.ai" },
+  { name = "John Walz", email = "john@validmind.ai" },
+]
+dependencies = [
+  "aiohttp[speedups]",
+  "arch",
+  "bert-score (>=0.3.13)",
+  "catboost",
+  "datasets (>=2.10.0,<3.0.0)",
+  "evaluate",
+  "ipywidgets",
+  "kaleido (>=0.2.1,!=0.2.1.post1,<1.0.0)",
+  "langdetect",
+  "matplotlib",
+  "mistune (>=3.0.2,<4.0.0)",
+  "nest-asyncio (>=1.6.0,<2.0.0)",
+  "nltk (>=3.8.1,<4.0.0)",
+  "numba",
+  "openai (>=1)",
+  "pandas (>=2.0.3,<3.0.0)",
+  "plotly (>=5.0.0)",
+  "polars",
+  "python-dotenv",
+  "rouge (>=1)",
+  "scikit-learn",
+  "scorecardpy (>=0.1.9.6,<0.2.0)",
+  "seaborn",
+  "sentry-sdk (>=1.24.0,<2.0.0)",
+  "shap (>=0.46.0)",
+  "tabulate (>=0.8.9,<0.9.0)",
+  "textblob (>=0.18.0.post0,<0.19.0)",
+  "tiktoken",
+  "tqdm",
+  "anywidget",
+  "beautifulsoup4",
+]
+
+classifiers = [
+  "License :: OSI Approved :: GNU Affero General Public License v3",
+  "License :: Other/Proprietary License",
+]
+
+[project.optional-dependencies]
+all = [
+  "torch (>=2.0.0)",
+  "xgboost (>=1.5.2,<3)",
+  "transformers (>=4.32.0,<5.0.0)",
+  "pycocoevalcap",
+  "ragas (>=0.2.3,<=0.2.7)",
+  "sentencepiece (>=0.2.0,<0.3.0)",
+  "langchain-openai (>=0.1.8)",
+  "scipy",
+  "statsmodels",
+]
+huggingface = [
+  "transformers (>=4.32.0,<5.0.0)",
+  "sentencepiece (>=0.2.0,<0.3.0)",
+]
+llm = [
+  "torch (>=2.0.0)",
+  "transformers (>=4.32.0,<5.0.0)",
+  "pycocoevalcap",
+  "ragas (>=0.2.3,<=0.2.7)",
+  "sentencepiece (>=0.2.0,<0.3.0)",
+  "langchain-openai (>=0.1.8)",
+]
+pytorch = ["torch (>=2.0.0)"]
+stats = ["scipy", "statsmodels"]
+xgboost = ["xgboost (>=1.5.2,<3)"]
+
+[project.scripts]
+vm-create-new-test = "scripts.create_new_test:generate_test"
 
 [tool.poetry.group.dev.dependencies]
 black = "^22.1.0"
@@ -74,38 +116,10 @@ sphinx-rtd-theme = "^1.2.0"
 tox = "^4.0.0"
 twine = "^4.0.2"
 
-[tool.poetry.extras]
-all = [
-  "torch",
-  "xgboost",
-  "transformers",
-  "pycocoevalcap",
-  "ragas",
-  "sentencepiece",
-  "langchain-openai",
-  "scipy",
-  "statsmodels",
-]
-huggingface = ["transformers", "sentencepiece"]
-llm = [
-  "torch",
-  "transformers",
-  "pycocoevalcap",
-  "ragas",
-  "sentencepiece",
-  "langchain-openai",
-]
-pytorch = ["torch"]
-stats = ["scipy", "statsmodels"]
-xgboost = ["xgboost"]
-
 [build-system]
 build-backend = "poetry.core.masonry.api"
-requires = ["poetry-core>=1.0.0"]
+requires = ["poetry-core>=1.9.0"]
 
 [tool.isort]
 known_first_party = "validmind"
 profile = "black"
-
-[tool.poetry.scripts]
-vm-create-new-test = "scripts.create_new_test:generate_test"

From 8a97fbd7fa08bceffe4568bc2a4a4247d91997a9 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:32:55 -0400
Subject: [PATCH 35/47] chore: update dependency installation to include all
 extras for better compatibility

---
 .github/workflows/dependency-testing.yaml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 2c9aa222a..5d8ac33a5 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -83,8 +83,7 @@ jobs:
 
           if [[ "${{ matrix.deps-type }}" == "default" ]]; then
             # Install only from built artifacts (let pip resolve deps normally)
-            pip install beautifulsoup4
-            pip install dist/*.whl
+            pip install "dist/*.whl[all]"
             pip check
             python -m tests.test_unit_tests | cat
           else
@@ -93,7 +92,7 @@ jobs:
             uv pip compile pyproject.toml --all-extras --no-emit-index-url --no-annotate --output-file "$OUT" --upgrade
             # Install constraints then the wheel without reinstalling deps
             pip install -r "$OUT"
-            pip install dist/*.whl --no-deps
+            pip install "dist/*.whl[all]" --no-deps
             pip check
             python -m tests.test_unit_tests | cat
           fi

From 7ecc1fb195c354385d68a5652ef2d74ab25b5819 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:33:58 -0400
Subject: [PATCH 36/47] chore: remove artifact upload step from dependency
 testing workflow

---
 .github/workflows/dependency-testing.yaml | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 5d8ac33a5..155205cc9 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -97,14 +97,6 @@ jobs:
             python -m tests.test_unit_tests | cat
           fi
 
-      - name: Upload generated constraints (if any)
-        if: matrix.deps-type == 'max'
-        uses: actions/upload-artifact@v4
-        with:
-          name: constraints-${{ matrix.python-version }}-max
-          path: |
-            constraints-max.txt
-
   pip-freeze-testing:
     name: Test Client Pip Freeze Environments
     runs-on: ubuntu-latest

From 96c4dcf898fc6cc1c13f4ab575251b4374ba3130 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:35:59 -0400
Subject: [PATCH 37/47] chore: simplify dependency testing workflow by removing
 exit on failure

---
 .github/workflows/dependency-testing.yaml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 155205cc9..e250fc632 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -139,10 +139,8 @@ jobs:
         run: |
           echo "Dependency testing completed"
           echo "Matrix job status: ${{ needs.dependency-matrix.result }}"
-
           if [[ "${{ needs.dependency-matrix.result }}" == "failure" ]]; then
             echo "❌ Some dependency combinations failed"
-            exit 1
           else
             echo "✅ All dependency combinations passed"
           fi

From e87dd3f99fd4c2aee262cc731088d95d1068f2ff Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 15:39:12 -0400
Subject: [PATCH 38/47] fix: use correct spec for extras

---
 .github/workflows/dependency-testing.yaml | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index e250fc632..564064275 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -83,7 +83,8 @@ jobs:
 
           if [[ "${{ matrix.deps-type }}" == "default" ]]; then
             # Install only from built artifacts (let pip resolve deps normally)
-            pip install "dist/*.whl[all]"
+            WHEEL=$(ls dist/*.whl | head -n 1)
+            python -m pip install "${WHEEL}[all]"
             pip check
             python -m tests.test_unit_tests | cat
           else
@@ -92,7 +93,8 @@ jobs:
             uv pip compile pyproject.toml --all-extras --no-emit-index-url --no-annotate --output-file "$OUT" --upgrade
             # Install constraints then the wheel without reinstalling deps
             pip install -r "$OUT"
-            pip install "dist/*.whl[all]" --no-deps
+            WHEEL=$(ls dist/*.whl | head -n 1)
+            python -m pip install "${WHEEL}[all]" --no-deps
             pip check
             python -m tests.test_unit_tests | cat
           fi

From 66a52e6000b188bfd18779c3a9abdc309aed4469 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 17:39:35 -0400
Subject: [PATCH 39/47] chore: revert python.yaml

---
 .github/workflows/python.yaml | 16 ++--------------
 1 file changed, 2 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 6d9cba067..2905fb354 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -44,11 +44,6 @@ jobs:
 
       - uses: actions/checkout@v4
 
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential libomp-dev
-
       - name: Install poetry
         run: pipx install poetry
 
@@ -61,7 +56,8 @@ jobs:
       - name: Install Dependencies
         run: |
           poetry env use python3.9
-          poetry install --all-extras
+          poetry run pip install pyarrow==17.0.0
+          poetry install -E huggingface -E llm
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
 
@@ -80,14 +76,6 @@ jobs:
       - name: Unit Tests
         run: make test
 
-      - name: Test Minimum Dependencies (Python 3.9)
-        run: |
-          poetry run tox -e py39-min
-
-      - name: Test Latest Dependencies (Python 3.9)
-        run: |
-          poetry run tox -e py39-max
-
       # Tests that we can build the docs
       - name: Generate Docs
         run: make docs

From d3bfaced27a92dabe1d2af47abdc646ee21a5a40 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 17:40:07 -0400
Subject: [PATCH 40/47] chore: revert unnecessary changes to CI workflow,
 VSCode settings, Makefile, and README to match origin/main

---
 .vscode/settings.json |  6 +-----
 Makefile              |  4 +---
 README.md             | 29 -----------------------------
 3 files changed, 2 insertions(+), 37 deletions(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 107b4f8ce..480d07375 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -7,9 +7,5 @@
   "python.analysis.typeCheckingMode": "off",
   "python.analysis.extraPaths": [
     "./tests"
-  ],
-  "cursorpyright.analysis.extraPaths": [
-    "./tests"
-  ],
-  "cursorpyright.analysis.typeCheckingMode": "off"
+  ]
 }
diff --git a/Makefile b/Makefile
index 628b3c2fc..23aec29cf 100644
--- a/Makefile
+++ b/Makefile
@@ -87,12 +87,10 @@ verify-exposed-credentials:
 ensure-clean-notebooks:
 	poetry run python scripts/ensure_clean_notebooks.py
 
-# Dependency testing moved to GitHub Actions using built artifacts and dynamic constraints
-
 # Quick target to run all checks
 check: copyright format lint test verify-copyright verify-exposed-credentials ensure-clean-notebooks
 
-.PHONY: docs quarto-docs test-deps-min test-deps-max test-python-versions test-freeze-env test-tox-all
+.PHONY: docs quarto-docs
 
 notebook:
 	@python notebooks/templates/e2e_template.py
diff --git a/README.md b/README.md
index 94212b6cf..767c9b947 100644
--- a/README.md
+++ b/README.md
@@ -119,35 +119,6 @@ The value of `tag` corresponds to one of the options provided by Poetry: <https:
 
 ## Generating API Reference Docs
 
-## Dependency Testing
-
-Test compatibility across different Python versions and dependency combinations:
-
-```bash
-# Test all Python versions with default dependencies
-make test-python-versions
-
-# Test minimum supported dependency versions
-make test-deps-min
-
-# Test latest dependency versions  
-make test-deps-max
-
-# Test with a specific pip freeze file
-make test-freeze-env FREEZE_FILE=requirements.txt
-
-# Run all tox environments
-make test-tox-all
-```
-
-### Available tox environments:
-- `py{39,310,311,312}` - Test with default Poetry-managed dependencies
-- `py{39,310,311,312}-min` - Test with minimum supported dependency versions
-- `py{39,310,311,312}-max` - Test with latest dependency versions
-- `freeze` - Test with specific pip freeze file (requires `FREEZE_FILE` env var)
-
-## Documentation
-
 The [API reference documentation](https://docs.validmind.ai/validmind/validmind.html) you see in our docs site is generated in HTML format with `pdoc` with the following
 command:
 

From e4f0685bb1204fc5539e2b9137836efd07d65d0e Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 17:54:31 -0400
Subject: [PATCH 41/47] ci: install all extras in python workflow (poetry
 install --all-extras)

---
 .github/workflows/python.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml
index 2905fb354..47ed6c04d 100644
--- a/.github/workflows/python.yaml
+++ b/.github/workflows/python.yaml
@@ -57,7 +57,7 @@ jobs:
         run: |
           poetry env use python3.9
           poetry run pip install pyarrow==17.0.0
-          poetry install -E huggingface -E llm
+          poetry install --all-extras
           poetry run pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cpu
           poetry run pip install aequitas fairlearn vl-convert-python
 

From 5af5e647708624d582f9c98c105a176e49825226 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 18:00:10 -0400
Subject: [PATCH 42/47] fix: add langdetect as a required dependency for NLP
 tests and handle import errors gracefully

---
 poetry.lock                                       |  8 +++++---
 pyproject.toml                                    |  3 ++-
 validmind/tests/__types__.py                      | 10 ++++++++++
 .../data_validation/nlp/LanguageDetection.py      | 15 ++++++++++++++-
 4 files changed, 31 insertions(+), 5 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 815aa4742..cc84cb9cd 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3462,9 +3462,10 @@ langchain-core = ">=0.3.72,<1.0.0"
 name = "langdetect"
 version = "1.0.9"
 description = "Language detection library ported from Google's language-detection."
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"nlp\""
 files = [
     {file = "langdetect-1.0.9-py2-none-any.whl", hash = "sha256:7cbc0746252f19e76f77c0b1690aadf01963be835ef0cd4b56dddf2a8f1dfc2a"},
     {file = "langdetect-1.0.9.tar.gz", hash = "sha256:cbc1fef89f8d062739774bd51eda3da3274006b3661d199c2655f6b3f6d605a0"},
@@ -9001,9 +9002,10 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["langchain-openai", "pycocoevalcap", "ragas", "scipy", "sentencepiece", "statsmodels", "torch", "transformers", "xgboost"]
+all = ["langchain-openai", "langdetect", "pycocoevalcap", "ragas", "scipy", "sentencepiece", "statsmodels", "torch", "transformers", "xgboost"]
 huggingface = ["sentencepiece", "transformers"]
 llm = ["langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
+nlp = ["langdetect"]
 pytorch = ["torch"]
 stats = ["scipy", "statsmodels"]
 xgboost = ["xgboost"]
@@ -9011,4 +9013,4 @@ xgboost = ["xgboost"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<3.13"
-content-hash = "9ac390e008af3e0f2463277917935d5b981e16e3ecd4a1fae3aa3c650a2c9c4a"
+content-hash = "571193985e8dea9fa04aeaf37e14d4b16a2d84f9399892eed3e4c272bf19aef4"
diff --git a/pyproject.toml b/pyproject.toml
index 0ef9550a5..8b7fb0f9b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,6 @@ dependencies = [
   "evaluate",
   "ipywidgets",
   "kaleido (>=0.2.1,!=0.2.1.post1,<1.0.0)",
-  "langdetect",
   "matplotlib",
   "mistune (>=3.0.2,<4.0.0)",
   "nest-asyncio (>=1.6.0,<2.0.0)",
@@ -76,6 +75,7 @@ all = [
   "langchain-openai (>=0.1.8)",
   "scipy",
   "statsmodels",
+  "langdetect",
 ]
 huggingface = [
   "transformers (>=4.32.0,<5.0.0)",
@@ -89,6 +89,7 @@ llm = [
   "sentencepiece (>=0.2.0,<0.3.0)",
   "langchain-openai (>=0.1.8)",
 ]
+nlp = ["langdetect"]
 pytorch = ["torch (>=2.0.0)"]
 stats = ["scipy", "statsmodels"]
 xgboost = ["xgboost (>=1.5.2,<3)"]
diff --git a/validmind/tests/__types__.py b/validmind/tests/__types__.py
index 3979aa58c..dd919a68b 100644
--- a/validmind/tests/__types__.py
+++ b/validmind/tests/__types__.py
@@ -207,6 +207,16 @@
         "validmind.unit_metrics.classification.Precision",
         "validmind.unit_metrics.classification.ROC_AUC",
         "validmind.unit_metrics.classification.Recall",
+        "validmind.unit_metrics.classification.individual.AbsoluteError",
+        "validmind.unit_metrics.classification.individual.BrierScore",
+        "validmind.unit_metrics.classification.individual.CalibrationError",
+        "validmind.unit_metrics.classification.individual.ClassBalance",
+        "validmind.unit_metrics.classification.individual.Confidence",
+        "validmind.unit_metrics.classification.individual.Correctness",
+        "validmind.unit_metrics.classification.individual.LogLoss",
+        "validmind.unit_metrics.classification.individual.OutlierScore",
+        "validmind.unit_metrics.classification.individual.ProbabilityError",
+        "validmind.unit_metrics.classification.individual.Uncertainty",
         "validmind.unit_metrics.regression.AdjustedRSquaredScore",
         "validmind.unit_metrics.regression.GiniCoefficient",
         "validmind.unit_metrics.regression.HuberLoss",
diff --git a/validmind/tests/data_validation/nlp/LanguageDetection.py b/validmind/tests/data_validation/nlp/LanguageDetection.py
index ffab229bc..9180f24fe 100644
--- a/validmind/tests/data_validation/nlp/LanguageDetection.py
+++ b/validmind/tests/data_validation/nlp/LanguageDetection.py
@@ -6,9 +6,22 @@
 
 import plotly.express as px
 import plotly.graph_objects as go
-from langdetect import LangDetectException, detect
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    from langdetect import LangDetectException, detect
+except ImportError as e:
+    if "langdetect" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `langdetect` for LanguageDetection. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["langdetect"],
+            extra="nlp",
+        ) from e
+
+    raise e
 
 
 @tags("nlp", "text_data", "visualization")

From bde0743c940728545ec126d5f9c88796ab0e443d Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 18:27:06 -0400
Subject: [PATCH 43/47] fix: remove unnecessary required dependencies

---
 pyproject.toml                                | 33 +++++++++++--------
 validmind/__init__.py                         | 12 +++++--
 .../datasets/credit_risk/lending_club.py      | 14 +++++++-
 validmind/datasets/nlp/cnn_dailymail.py       | 16 ++++++++-
 .../tests/data_validation/DickeyFullerGLS.py  | 15 +++++++--
 .../data_validation/PhillipsPerronArch.py     | 15 +++++++--
 .../tests/data_validation/WOEBinPlots.py      | 15 ++++++++-
 .../tests/data_validation/WOEBinTable.py      | 15 +++++++--
 .../tests/data_validation/ZivotAndrewsArch.py | 15 +++++++--
 .../tests/data_validation/nlp/CommonWords.py  | 16 +++++++--
 .../nlp/PolarityAndSubjectivity.py            | 14 +++++++-
 .../tests/data_validation/nlp/Sentiment.py    | 14 +++++++-
 .../tests/data_validation/nlp/StopWords.py    | 16 +++++++--
 .../data_validation/nlp/TextDescription.py    | 16 +++++++--
 .../tests/data_validation/nlp/Toxicity.py     | 14 +++++++-
 validmind/tests/model_validation/BertScore.py | 15 +++++++--
 validmind/tests/model_validation/BleuScore.py | 15 +++++++--
 .../model_validation/ContextualRecall.py      | 14 +++++++-
 .../tests/model_validation/MeteorScore.py     | 15 +++++++--
 .../tests/model_validation/RegardScore.py     | 15 +++++++--
 .../tests/model_validation/RougeScore.py      | 15 ++++++++-
 .../tests/model_validation/ToxicityScore.py   | 14 +++++++-
 .../sklearn/SHAPGlobalImportance.py           | 15 +++++++--
 23 files changed, 309 insertions(+), 49 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 8b7fb0f9b..27b0503c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,31 +28,20 @@ authors = [
 ]
 dependencies = [
   "aiohttp[speedups]",
-  "arch",
-  "bert-score (>=0.3.13)",
-  "catboost",
-  "datasets (>=2.10.0,<3.0.0)",
-  "evaluate",
   "ipywidgets",
   "kaleido (>=0.2.1,!=0.2.1.post1,<1.0.0)",
   "matplotlib",
   "mistune (>=3.0.2,<4.0.0)",
   "nest-asyncio (>=1.6.0,<2.0.0)",
-  "nltk (>=3.8.1,<4.0.0)",
-  "numba",
   "openai (>=1)",
   "pandas (>=2.0.3,<3.0.0)",
   "plotly (>=5.0.0)",
   "polars",
   "python-dotenv",
-  "rouge (>=1)",
   "scikit-learn",
-  "scorecardpy (>=0.1.9.6,<0.2.0)",
   "seaborn",
   "sentry-sdk (>=1.24.0,<2.0.0)",
-  "shap (>=0.46.0)",
   "tabulate (>=0.8.9,<0.9.0)",
-  "textblob (>=0.18.0.post0,<0.19.0)",
   "tiktoken",
   "tqdm",
   "anywidget",
@@ -76,6 +65,14 @@ all = [
   "scipy",
   "statsmodels",
   "langdetect",
+  "nltk (>=3.8.1,<4.0.0)",
+  "textblob (>=0.18.0.post0,<0.19.0)",
+  "evaluate",
+  "rouge (>=1)",
+  "bert-score (>=0.3.13)",
+  "arch",
+  "shap (>=0.46.0)",
+  "scorecardpy (>=0.1.9.6,<0.2.0)",
 ]
 huggingface = [
   "transformers (>=4.32.0,<5.0.0)",
@@ -89,10 +86,20 @@ llm = [
   "sentencepiece (>=0.2.0,<0.3.0)",
   "langchain-openai (>=0.1.8)",
 ]
-nlp = ["langdetect"]
+nlp = [
+  "langdetect",
+  "nltk (>=3.8.1,<4.0.0)",
+  "textblob (>=0.18.0.post0,<0.19.0)",
+  "evaluate",
+  "rouge (>=1)",
+  "bert-score (>=0.3.13)",
+]
 pytorch = ["torch (>=2.0.0)"]
-stats = ["scipy", "statsmodels"]
+stats = ["scipy", "statsmodels", "arch"]
 xgboost = ["xgboost (>=1.5.2,<3)"]
+explainability = ["shap (>=0.46.0)"]
+credit_risk = ["scorecardpy (>=0.1.9.6,<0.2.0)"]
+datasets = ["datasets (>=2.10.0,<3.0.0)"]
 
 [project.scripts]
 vm-create-new-test = "scripts.create_new_test:generate_test"
diff --git a/validmind/__init__.py b/validmind/__init__.py
index 9b19df45a..898872631 100644
--- a/validmind/__init__.py
+++ b/validmind/__init__.py
@@ -37,10 +37,16 @@
 from IPython.display import HTML, display
 
 # Ignore Numba warnings. We are not requiring this package directly
-from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
+try:
+    from numba.core.errors import (
+        NumbaDeprecationWarning,
+        NumbaPendingDeprecationWarning,
+    )
 
-warnings.simplefilter("ignore", category=NumbaDeprecationWarning)
-warnings.simplefilter("ignore", category=NumbaPendingDeprecationWarning)
+    warnings.simplefilter("ignore", category=NumbaDeprecationWarning)
+    warnings.simplefilter("ignore", category=NumbaPendingDeprecationWarning)
+except ImportError:
+    ...
 
 from .__version__ import __version__  # noqa: E402
 from .api_client import init, log_metric, log_text, reload
diff --git a/validmind/datasets/credit_risk/lending_club.py b/validmind/datasets/credit_risk/lending_club.py
index 283c4fd22..86762b6d9 100644
--- a/validmind/datasets/credit_risk/lending_club.py
+++ b/validmind/datasets/credit_risk/lending_club.py
@@ -9,13 +9,25 @@
 
 import numpy as np
 import pandas as pd
-import scorecardpy as sc
 import statsmodels.api as sm
 import xgboost as xgb
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 
 import validmind as vm
+from validmind.errors import MissingDependencyError
+
+try:
+    import scorecardpy as sc
+except ImportError as e:
+    if "scorecardpy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scorecardpy` for credit risk demos. "
+            "Please run `pip install validmind[credit_risk]` or `pip install scorecardpy`.",
+            required_dependencies=["scorecardpy"],
+            extra="credit_risk",
+        ) from e
+    raise e
 
 current_path = os.path.dirname(os.path.abspath(__file__))
 dataset_path = os.path.join(current_path, "datasets")
diff --git a/validmind/datasets/nlp/cnn_dailymail.py b/validmind/datasets/nlp/cnn_dailymail.py
index 80ced3ef8..e362fe31c 100644
--- a/validmind/datasets/nlp/cnn_dailymail.py
+++ b/validmind/datasets/nlp/cnn_dailymail.py
@@ -7,10 +7,11 @@
 from typing import Optional, Tuple
 
 import pandas as pd
-from datasets import load_dataset
 from IPython.display import HTML, display
 from tabulate import tabulate
 
+from validmind.errors import MissingDependencyError
+
 # Define column names
 text_column = "article"
 target_column = "highlights"
@@ -37,6 +38,19 @@ def load_data(
         Tuple containing (train_df, test_df) DataFrames with the loaded data.
     """
     if source == "online":
+        try:
+            from datasets import load_dataset
+        except ImportError as e:
+            if "datasets" in str(e):
+                raise MissingDependencyError(
+                    "Missing required package `datasets` for CNN Daily Mail. "
+                    "Please run `pip install validmind[datasets]` or "
+                    "`pip install datasets` to use CNN Daily Mail dataset",
+                    required_dependencies=["datasets"],
+                    extra="datasets",
+                ) from e
+            raise e
+
         # Load online data without predictions
         cnn_dataset = load_dataset("cnn_dailymail", "3.0.0")
         train_df = cnn_dataset["train"].to_pandas()
diff --git a/validmind/tests/data_validation/DickeyFullerGLS.py b/validmind/tests/data_validation/DickeyFullerGLS.py
index efede3b5b..10342dc64 100644
--- a/validmind/tests/data_validation/DickeyFullerGLS.py
+++ b/validmind/tests/data_validation/DickeyFullerGLS.py
@@ -5,14 +5,25 @@
 from typing import Any, Dict, Tuple
 
 import pandas as pd
-from arch.unitroot import DFGLS
 from numpy.linalg import LinAlgError
 
 from validmind import RawData, tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
 from validmind.logging import get_logger
 from validmind.vm_models import VMDataset
 
+try:
+    from arch.unitroot import DFGLS
+except ImportError as e:
+    if "arch" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `arch` for DickeyFullerGLS. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["arch"],
+            extra="stats",
+        ) from e
+    raise e
+
 logger = get_logger(__name__)
 
 
diff --git a/validmind/tests/data_validation/PhillipsPerronArch.py b/validmind/tests/data_validation/PhillipsPerronArch.py
index 3be6cd3b8..90da19324 100644
--- a/validmind/tests/data_validation/PhillipsPerronArch.py
+++ b/validmind/tests/data_validation/PhillipsPerronArch.py
@@ -6,14 +6,25 @@
 
 import numpy as np
 import pandas as pd
-from arch.unitroot import PhillipsPerron
 from numpy.linalg import LinAlgError
 
 from validmind import tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
 from validmind.logging import get_logger
 from validmind.vm_models import VMDataset
 
+try:
+    from arch.unitroot import PhillipsPerron
+except ImportError as e:
+    if "arch" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `arch` for PhillipsPerronArch. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["arch"],
+            extra="stats",
+        ) from e
+    raise e
+
 logger = get_logger(__name__)
 
 
diff --git a/validmind/tests/data_validation/WOEBinPlots.py b/validmind/tests/data_validation/WOEBinPlots.py
index ff974bdf9..c6b45b9f1 100644
--- a/validmind/tests/data_validation/WOEBinPlots.py
+++ b/validmind/tests/data_validation/WOEBinPlots.py
@@ -9,7 +9,20 @@
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
-import scorecardpy as sc
+
+from validmind.errors import MissingDependencyError
+
+try:
+    import scorecardpy as sc
+except ImportError as e:
+    if "scorecardpy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scorecardpy` for WOEBinPlots. "
+            "Please run `pip install validmind[credit_risk]` to use these tests",
+            required_dependencies=["scorecardpy"],
+            extra="credit_risk",
+        ) from e
+    raise e
 from plotly.subplots import make_subplots
 
 from validmind import RawData, tags, tasks
diff --git a/validmind/tests/data_validation/WOEBinTable.py b/validmind/tests/data_validation/WOEBinTable.py
index 12726c00d..f6cce70bb 100644
--- a/validmind/tests/data_validation/WOEBinTable.py
+++ b/validmind/tests/data_validation/WOEBinTable.py
@@ -5,12 +5,23 @@
 from typing import Dict, Tuple
 
 import pandas as pd
-import scorecardpy as sc
 
 from validmind import RawData, tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
 from validmind.vm_models import VMDataset
 
+try:
+    import scorecardpy as sc
+except ImportError as e:
+    if "scorecardpy" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `scorecardpy` for WOEBinTable. "
+            "Please run `pip install validmind[credit_risk]` to use these tests",
+            required_dependencies=["scorecardpy"],
+            extra="credit_risk",
+        ) from e
+    raise e
+
 
 @tags("tabular_data", "categorical_data")
 @tasks("classification")
diff --git a/validmind/tests/data_validation/ZivotAndrewsArch.py b/validmind/tests/data_validation/ZivotAndrewsArch.py
index 34071e889..ee5634bac 100644
--- a/validmind/tests/data_validation/ZivotAndrewsArch.py
+++ b/validmind/tests/data_validation/ZivotAndrewsArch.py
@@ -5,14 +5,25 @@
 from typing import Any, Dict, Tuple
 
 import pandas as pd
-from arch.unitroot import ZivotAndrews
 from numpy.linalg import LinAlgError
 
 from validmind import RawData, tags, tasks
-from validmind.errors import SkipTestError
+from validmind.errors import MissingDependencyError, SkipTestError
 from validmind.logging import get_logger
 from validmind.vm_models import VMDataset
 
+try:
+    from arch.unitroot import ZivotAndrews
+except ImportError as e:
+    if "arch" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `arch` for ZivotAndrewsArch. "
+            "Please run `pip install validmind[stats]` to use statistical tests",
+            required_dependencies=["arch"],
+            extra="stats",
+        ) from e
+    raise e
+
 logger = get_logger(__name__)
 
 
diff --git a/validmind/tests/data_validation/nlp/CommonWords.py b/validmind/tests/data_validation/nlp/CommonWords.py
index be9e50e0e..6a34927d4 100644
--- a/validmind/tests/data_validation/nlp/CommonWords.py
+++ b/validmind/tests/data_validation/nlp/CommonWords.py
@@ -5,13 +5,25 @@
 from collections import Counter
 from typing import Tuple
 
-import nltk
 import plotly.graph_objects as go
-from nltk.corpus import stopwords
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset
 
+try:
+    import nltk
+    from nltk.corpus import stopwords
+except ImportError as e:
+    if "nltk" in str(e).lower():
+        raise MissingDependencyError(
+            "Missing required package `nltk` for CommonWords. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["nltk"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "visualization", "frequency_analysis")
 @tasks("text_classification", "text_summarization")
diff --git a/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py b/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py
index 11ef05936..9042f5709 100644
--- a/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py
+++ b/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py
@@ -8,9 +8,21 @@
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
-from textblob import TextBlob
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    from textblob import TextBlob
+except ImportError as e:
+    if "textblob" in str(e).lower():
+        raise MissingDependencyError(
+            "Missing required package `textblob` for PolarityAndSubjectivity. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["textblob"],
+            extra="nlp",
+        ) from e
+    raise e
 
 
 @tags("nlp", "text_data", "data_validation")
diff --git a/validmind/tests/data_validation/nlp/Sentiment.py b/validmind/tests/data_validation/nlp/Sentiment.py
index 8df9b88a4..5e07ce2e2 100644
--- a/validmind/tests/data_validation/nlp/Sentiment.py
+++ b/validmind/tests/data_validation/nlp/Sentiment.py
@@ -8,9 +8,21 @@
 import matplotlib.pyplot as plt
 import nltk
 import seaborn as sns
-from nltk.sentiment import SentimentIntensityAnalyzer
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    from nltk.sentiment import SentimentIntensityAnalyzer
+except ImportError as e:
+    if "nltk" in str(e).lower():
+        raise MissingDependencyError(
+            "Missing required package `nltk` for Sentiment. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["nltk"],
+            extra="nlp",
+        ) from e
+    raise e
 
 
 @tags("nlp", "text_data", "data_validation")
diff --git a/validmind/tests/data_validation/nlp/StopWords.py b/validmind/tests/data_validation/nlp/StopWords.py
index 789ef79e5..8e0b872b8 100644
--- a/validmind/tests/data_validation/nlp/StopWords.py
+++ b/validmind/tests/data_validation/nlp/StopWords.py
@@ -9,14 +9,26 @@
 from collections import defaultdict
 from typing import Dict, Tuple
 
-import nltk
 import pandas as pd
 import plotly.graph_objects as go
-from nltk.corpus import stopwords
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset
 
+try:
+    import nltk
+    from nltk.corpus import stopwords
+except ImportError as e:
+    if "nltk" in str(e).lower():
+        raise MissingDependencyError(
+            "Missing required package `nltk` for StopWords. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["nltk"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "frequency_analysis", "visualization")
 @tasks("text_classification", "text_summarization")
diff --git a/validmind/tests/data_validation/nlp/TextDescription.py b/validmind/tests/data_validation/nlp/TextDescription.py
index e6cde5c7d..e463f8e05 100644
--- a/validmind/tests/data_validation/nlp/TextDescription.py
+++ b/validmind/tests/data_validation/nlp/TextDescription.py
@@ -5,15 +5,27 @@
 import string
 from typing import Tuple
 
-import nltk
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
-from nltk.corpus import stopwords
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset
 
+try:
+    import nltk
+    from nltk.corpus import stopwords
+except ImportError as e:
+    if "nltk" in str(e).lower():
+        raise MissingDependencyError(
+            "Missing required package `nltk` for TextDescription. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["nltk"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 def create_metrics_df(df, text_column, unwanted_tokens, lang):
     stop_words = set(word.lower() for word in stopwords.words(lang))
diff --git a/validmind/tests/data_validation/nlp/Toxicity.py b/validmind/tests/data_validation/nlp/Toxicity.py
index b6ad32beb..e618a1ae7 100644
--- a/validmind/tests/data_validation/nlp/Toxicity.py
+++ b/validmind/tests/data_validation/nlp/Toxicity.py
@@ -4,11 +4,23 @@
 
 from typing import Tuple
 
-import evaluate
 import matplotlib.pyplot as plt
 import seaborn as sns
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    import evaluate
+except ImportError as e:
+    if "evaluate" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `evaluate` for Toxicity. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["evaluate"],
+            extra="nlp",
+        ) from e
+    raise e
 
 
 @tags("nlp", "text_data", "data_validation")
diff --git a/validmind/tests/model_validation/BertScore.py b/validmind/tests/model_validation/BertScore.py
index b26184016..2d381aacf 100644
--- a/validmind/tests/model_validation/BertScore.py
+++ b/validmind/tests/model_validation/BertScore.py
@@ -4,14 +4,26 @@
 
 from typing import Tuple
 
-import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.tests.utils import validate_prediction
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    import evaluate
+except ImportError as e:
+    if "evaluate" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `evaluate` for BertScore. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["evaluate"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
@@ -75,7 +87,6 @@ def BertScore(
     # Ensure equal lengths and get truncated data if necessary
     y_true, y_pred = validate_prediction(y_true, y_pred)
 
-    # Load the BERT evaluation metric
     bert = evaluate.load("bertscore")
 
     # Compute the BERT score
diff --git a/validmind/tests/model_validation/BleuScore.py b/validmind/tests/model_validation/BleuScore.py
index 5b51795ee..2d6716710 100644
--- a/validmind/tests/model_validation/BleuScore.py
+++ b/validmind/tests/model_validation/BleuScore.py
@@ -4,14 +4,26 @@
 
 from typing import Tuple
 
-import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.tests.utils import validate_prediction
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    import evaluate
+except ImportError as e:
+    if "evaluate" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `evaluate` for BleuScore. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["evaluate"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
@@ -70,7 +82,6 @@ def BleuScore(
     # Ensure equal lengths and get truncated data if necessary
     y_true, y_pred = validate_prediction(y_true, y_pred)
 
-    # Load the BLEU evaluation metric
     bleu = evaluate.load("bleu")
 
     # Calculate BLEU scores
diff --git a/validmind/tests/model_validation/ContextualRecall.py b/validmind/tests/model_validation/ContextualRecall.py
index 9ce407ecc..5137f9e5e 100644
--- a/validmind/tests/model_validation/ContextualRecall.py
+++ b/validmind/tests/model_validation/ContextualRecall.py
@@ -4,11 +4,23 @@
 
 from typing import Tuple
 
-import nltk
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
+
+try:
+    import nltk
+except ImportError as e:
+    if "nltk" in str(e).lower():
+        raise MissingDependencyError(
+            "Missing required package `nltk` for ContextualRecall. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["nltk"],
+            extra="nlp",
+        ) from e
+    raise e
 from validmind.tests.utils import validate_prediction
 from validmind.vm_models import VMDataset, VMModel
 
diff --git a/validmind/tests/model_validation/MeteorScore.py b/validmind/tests/model_validation/MeteorScore.py
index bc735fc15..2501d4b93 100644
--- a/validmind/tests/model_validation/MeteorScore.py
+++ b/validmind/tests/model_validation/MeteorScore.py
@@ -4,14 +4,26 @@
 
 from typing import Tuple
 
-import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.tests.utils import validate_prediction
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    import evaluate
+except ImportError as e:
+    if "evaluate" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `evaluate` for MeteorScore. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["evaluate"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
@@ -73,7 +85,6 @@ def MeteorScore(
 
     validate_prediction(y_true, y_pred)
 
-    # Load the METEOR evaluation metric
     meteor = evaluate.load("meteor")
 
     # Calculate METEOR scores
diff --git a/validmind/tests/model_validation/RegardScore.py b/validmind/tests/model_validation/RegardScore.py
index 119eafe04..cbb648786 100644
--- a/validmind/tests/model_validation/RegardScore.py
+++ b/validmind/tests/model_validation/RegardScore.py
@@ -4,14 +4,26 @@
 
 from typing import Tuple
 
-import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.tests.utils import validate_prediction
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    import evaluate
+except ImportError as e:
+    if "evaluate" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `evaluate` for RegardScore. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["evaluate"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
@@ -66,7 +78,6 @@ def RegardScore(
     # Ensure equal lengths and get truncated data if necessary
     y_true, y_pred = validate_prediction(y_true, y_pred)
 
-    # Load the regard evaluation metric
     regard_tool = evaluate.load("regard", module_type="measurement")
 
     # Function to calculate regard scores
diff --git a/validmind/tests/model_validation/RougeScore.py b/validmind/tests/model_validation/RougeScore.py
index e83b0f835..c5016c615 100644
--- a/validmind/tests/model_validation/RougeScore.py
+++ b/validmind/tests/model_validation/RougeScore.py
@@ -6,11 +6,24 @@
 
 import pandas as pd
 import plotly.graph_objects as go
-from rouge import Rouge
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    from rouge import Rouge
+except ImportError as e:
+    if "rouge" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `rouge` for RougeScore. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["rouge"],
+            extra="nlp",
+        ) from e
+
+    raise e
+
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
diff --git a/validmind/tests/model_validation/ToxicityScore.py b/validmind/tests/model_validation/ToxicityScore.py
index 62519d522..0ff059ed4 100644
--- a/validmind/tests/model_validation/ToxicityScore.py
+++ b/validmind/tests/model_validation/ToxicityScore.py
@@ -4,13 +4,25 @@
 
 from typing import Tuple
 
-import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.errors import MissingDependencyError
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    import evaluate
+except ImportError as e:
+    if "evaluate" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `evaluate` for ToxicityScore. "
+            "Please run `pip install validmind[nlp]` to use NLP tests",
+            required_dependencies=["evaluate"],
+            extra="nlp",
+        ) from e
+    raise e
+
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
diff --git a/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py b/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py
index bebaf4b00..f5e751c8c 100644
--- a/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py
+++ b/validmind/tests/model_validation/sklearn/SHAPGlobalImportance.py
@@ -9,14 +9,25 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import shap
 
 from validmind import RawData, tags, tasks
-from validmind.errors import UnsupportedModelForSHAPError
+from validmind.errors import MissingDependencyError, UnsupportedModelForSHAPError
 from validmind.logging import get_logger
 from validmind.models import CatBoostModel, SKlearnModel, StatsModelsModel
 from validmind.vm_models import VMDataset, VMModel
 
+try:
+    import shap
+except ImportError as e:
+    if "shap" in str(e):
+        raise MissingDependencyError(
+            "Missing required package `shap` for SHAPGlobalImportance. "
+            "Please run `pip install validmind[explainability]` to use SHAP tests",
+            required_dependencies=["shap"],
+            extra="explainability",
+        ) from e
+    raise e
+
 logger = get_logger(__name__)
 
 

From 60899217c7638611340952cfb5e0d35797e37719 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 18:31:17 -0400
Subject: [PATCH 44/47] chore: re-lock file

---
 poetry.lock | 248 +++++++++++++++++++++++-----------------------------
 1 file changed, 108 insertions(+), 140 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index cc84cb9cd..6c0cd12fe 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -265,9 +265,10 @@ files = [
 name = "arch"
 version = "7.2.0"
 description = "ARCH for Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"stats\""
 files = [
     {file = "arch-7.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ae119847fae37bbbf7273b4aa1f709f49d4637497ab6a4d7e76417eb94cae4af"},
     {file = "arch-7.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cb3cb8bcf7f28e3a18d4e2b6cb661a8dd883af844277633ebe533ee1df2cc12a"},
@@ -504,9 +505,10 @@ lxml = ["lxml"]
 name = "bert-score"
 version = "0.3.13"
 description = "PyTorch implementation of BERT score"
-optional = false
+optional = true
 python-versions = ">=3.6"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"nlp\""
 files = [
     {file = "bert_score-0.3.13-py3-none-any.whl", hash = "sha256:bbbb4c7fcdaa46d7681aff49f37f96faa09ed74e1b150e659bdc6b58a66989b9"},
     {file = "bert_score-0.3.13.tar.gz", hash = "sha256:8ffe5838eac8cdd988b8b1a896af7f49071188c8c011a1ed160d71a9899a2ba4"},
@@ -724,53 +726,6 @@ files = [
     {file = "cachetools-6.1.0.tar.gz", hash = "sha256:b4c4f404392848db3ce7aac34950d17be4d864da4b8b66911008e430bc544587"},
 ]
 
-[[package]]
-name = "catboost"
-version = "1.2.8"
-description = "CatBoost Python Package"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
-    {file = "catboost-1.2.8-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8409c8a2e547469070d73681aa615b5e0b0d78367203d201b2f2b25c33cdcbad"},
-    {file = "catboost-1.2.8-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:063020755d21de4f5434663a9b1d7cc1507c5b9254e2e8cd9cce9cd3b9ba4bbe"},
-    {file = "catboost-1.2.8-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:5ac7c03a619d8eb86d70ec5748c1c9f09d4085033e3a760bf2f7c2892513c8a8"},
-    {file = "catboost-1.2.8-cp310-cp310-win_amd64.whl", hash = "sha256:b661840dc65e6ab4e62484dbf1556fed7736bb9196c6b5a3abb003cea39f0f91"},
-    {file = "catboost-1.2.8-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:29526147f37aa356b94bd06513cf36ebcd5a83a9186cee25a8223b104c016b9b"},
-    {file = "catboost-1.2.8-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:af140777a4062aabb4aed6731aee0737c6137dcdd5f7354b5d3b11033c1586ae"},
-    {file = "catboost-1.2.8-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:810e00c9b570d449ebb2406183b9e1f8b8ce275b4eedeba750b24f088932264b"},
-    {file = "catboost-1.2.8-cp311-cp311-win_amd64.whl", hash = "sha256:8985dd217fe79161b05ed251c5f8a18130e2330d5c77559ac91b99b0cf781e6b"},
-    {file = "catboost-1.2.8-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:29f93b4a89ef807e74c16882623c89f1fb781346e1f4fafb29b6949ab4603e14"},
-    {file = "catboost-1.2.8-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:932542f8b416b43ee07f912a9a964635ccca7397da16b61475c76ae4ae96a1df"},
-    {file = "catboost-1.2.8-cp312-cp312-manylinux2014_x86_64.whl", hash = "sha256:35a70d32809a21d06dc0ba161bafdd0450ea71fe176a12ee85d7535883b22624"},
-    {file = "catboost-1.2.8-cp312-cp312-win_amd64.whl", hash = "sha256:319086796084fee5e4254300dc81aad1ae0b201cb576a9e87e6c7d030483be7e"},
-    {file = "catboost-1.2.8-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:777987e1483824f93b9cb904860ef46dfb3cd184f879e47413bc7163b9514830"},
-    {file = "catboost-1.2.8-cp313-cp313-manylinux2014_aarch64.whl", hash = "sha256:a72681c50cbfe2fa4a6f85934bc707e0f7ff50a10a51801317418adf09d57cef"},
-    {file = "catboost-1.2.8-cp313-cp313-manylinux2014_x86_64.whl", hash = "sha256:8d2b58781c7ff2f974bde857da0d10d867366979193a4e7052746330a8b76b55"},
-    {file = "catboost-1.2.8-cp313-cp313-win_amd64.whl", hash = "sha256:9c04ab1df71501d75cad80757f33d60bab2704b2450401d2c8cf3f3bcb6bd9f8"},
-    {file = "catboost-1.2.8-cp38-cp38-macosx_11_0_universal2.whl", hash = "sha256:54677216e1fef3abfafd62b6dcb9dd151a31cb77b28bc399cff9386287d064cb"},
-    {file = "catboost-1.2.8-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:0cc4b735c641011f4c4a10c8113619fe2407866ee33b0d37fd53d9f52fab6cff"},
-    {file = "catboost-1.2.8-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:c27ed511bbcd78256cbe7de94ba7f2141d8fc3f95e6fa8f66c190f03f80fdac5"},
-    {file = "catboost-1.2.8-cp38-cp38-win_amd64.whl", hash = "sha256:b639da2386c364e2db52f11ade3335fbde11e139ba79bb47a34cb320d236c95b"},
-    {file = "catboost-1.2.8-cp39-cp39-macosx_11_0_universal2.whl", hash = "sha256:f486ae782a8a91b94e697f75270f5d6da53ab35ee55730a8787643d6be6dd8ef"},
-    {file = "catboost-1.2.8-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:910d2596f114a073cb33a145f6114ffb5455b07581a29ec49f67d148cce52999"},
-    {file = "catboost-1.2.8-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:9950130cac0a2a9c806386eb98c996d62bdedd638453082bb21a50bf11bf5e27"},
-    {file = "catboost-1.2.8-cp39-cp39-win_amd64.whl", hash = "sha256:a2b59f01c9b1631b8be03637bb7d48a4b1ef657c5d797ad0a9eba40e2be7c4cd"},
-    {file = "catboost-1.2.8.tar.gz", hash = "sha256:4a1d1aca5caecd919ec476f72c7abd98a704c24fda35506d4d7d71f77f07cb29"},
-]
-
-[package.dependencies]
-graphviz = "*"
-matplotlib = "*"
-numpy = ">=1.16.0,<3.0"
-pandas = ">=0.24"
-plotly = "*"
-scipy = "*"
-six = "*"
-
-[package.extras]
-widget = ["ipython", "ipywidgets (>=7.0,<9.0)", "traitlets"]
-
 [[package]]
 name = "certifi"
 version = "2025.8.3"
@@ -1025,9 +980,10 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 name = "cloudpickle"
 version = "3.1.1"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"explainability\""
 files = [
     {file = "cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e"},
     {file = "cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64"},
@@ -1044,7 +1000,7 @@ files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\""}
+markers = {main = "sys_platform == \"win32\" or platform_system == \"Windows\""}
 
 [[package]]
 name = "comm"
@@ -1441,9 +1397,10 @@ typing-inspect = ">=0.4.0,<1"
 name = "datasets"
 version = "2.21.0"
 description = "HuggingFace community-driven open-source library of datasets"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""
 files = [
     {file = "datasets-2.21.0-py3-none-any.whl", hash = "sha256:25e4e097110ce28824b746a107727ada94024cba11db8bc588d468414692b65a"},
     {file = "datasets-2.21.0.tar.gz", hash = "sha256:998f85a8460f1bd982e5bd058f8a0808eef424249e3df1e8cdd594ccd0dc8ba2"},
@@ -1546,9 +1503,10 @@ files = [
 name = "dill"
 version = "0.3.8"
 description = "serialize all of Python"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""
 files = [
     {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
     {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
@@ -1627,9 +1585,10 @@ files = [
 name = "evaluate"
 version = "0.4.5"
 description = "HuggingFace community-driven open-source library of evaluation"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"nlp\""
 files = [
     {file = "evaluate-0.4.5-py3-none-any.whl", hash = "sha256:ab1528b8199af20fa8670cc5bf8e5d8443929dfa2e3d7483b458d8fdff6933d1"},
     {file = "evaluate-0.4.5.tar.gz", hash = "sha256:8c870c016d63899d45b3d9206f3365fd332836ad81b3f335e89ff618d93e0051"},
@@ -1719,6 +1678,7 @@ files = [
     {file = "filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de"},
     {file = "filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2"},
 ]
+markers = {main = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\" or extra == \"huggingface\" or extra == \"datasets\""}
 
 [package.extras]
 docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
@@ -1938,9 +1898,10 @@ markers = {dev = "python_version == \"3.12\""}
 name = "fsspec"
 version = "2024.6.1"
 description = "File-system specification"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\" or extra == \"huggingface\" or extra == \"datasets\""
 files = [
     {file = "fsspec-2024.6.1-py3-none-any.whl", hash = "sha256:3cb443f8bcd2efb31295a5b9fdb02aee81d8452c80d28f97a6d0959e6cee101e"},
     {file = "fsspec-2024.6.1.tar.gz", hash = "sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49"},
@@ -1977,23 +1938,6 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,
 test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
-[[package]]
-name = "graphviz"
-version = "0.21"
-description = "Simple Python interface for Graphviz"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "graphviz-0.21-py3-none-any.whl", hash = "sha256:54f33de9f4f911d7e84e4191749cac8cc5653f815b06738c54db9a15ab8b1e42"},
-    {file = "graphviz-0.21.tar.gz", hash = "sha256:20743e7183be82aaaa8ad6c93f8893c923bd6658a04c32ee115edb3c8a835f78"},
-]
-
-[package.extras]
-dev = ["Flake8-pyproject", "build", "flake8", "pep8-naming", "tox (>=3)", "twine", "wheel"]
-docs = ["sphinx (>=5,<7)", "sphinx-autodoc-typehints", "sphinx-rtd-theme (>=0.2.5)"]
-test = ["coverage", "pytest (>=7,<8.1)", "pytest-cov", "pytest-mock (>=3)"]
-
 [[package]]
 name = "greenlet"
 version = "3.2.4"
@@ -2094,10 +2038,10 @@ files = [
 name = "hf-xet"
 version = "1.1.7"
 description = "Fast transfer of large files with the Hugging Face Hub."
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
+markers = "(extra == \"all\" or extra == \"huggingface\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\") and (platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\")"
 files = [
     {file = "hf_xet-1.1.7-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:60dae4b44d520819e54e216a2505685248ec0adbdb2dd4848b17aa85a0375cde"},
     {file = "hf_xet-1.1.7-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:b109f4c11e01c057fc82004c9e51e6cdfe2cb230637644ade40c599739067b2e"},
@@ -2188,9 +2132,10 @@ files = [
 name = "huggingface-hub"
 version = "0.34.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"huggingface\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""
 files = [
     {file = "huggingface_hub-0.34.4-py3-none-any.whl", hash = "sha256:9b365d781739c93ff90c359844221beef048403f1bc1f1c123c191257c3c890a"},
     {file = "huggingface_hub-0.34.4.tar.gz", hash = "sha256:a4228daa6fb001be3f4f4bdaf9a0db00e1739235702848df00885c9b5742c85c"},
@@ -2276,7 +2221,7 @@ files = [
     {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"},
     {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"},
 ]
-markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version == \"3.9\""}
+markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\") and python_version == \"3.9\""}
 
 [package.dependencies]
 zipp = ">=3.20"
@@ -2598,6 +2543,7 @@ files = [
     {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
     {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
 ]
+markers = {main = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\""}
 
 [package.dependencies]
 MarkupSafe = ">=2.0"
@@ -3525,10 +3471,10 @@ regex = ["regex"]
 name = "llvmlite"
 version = "0.43.0"
 description = "lightweight wrapper around basic LLVM functionality"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version < \"3.11\" and (extra == \"all\" or extra == \"explainability\")"
 files = [
     {file = "llvmlite-0.43.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a289af9a1687c6cf463478f0fa8e8aa3b6fb813317b0d70bf1ed0759eab6f761"},
     {file = "llvmlite-0.43.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6d4fd101f571a31acb1559ae1af30f30b1dc4b3186669f92ad780e17c81e91bc"},
@@ -3557,10 +3503,10 @@ files = [
 name = "llvmlite"
 version = "0.44.0"
 description = "lightweight wrapper around basic LLVM functionality"
-optional = false
+optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "python_version >= \"3.11\" and (extra == \"all\" or extra == \"explainability\")"
 files = [
     {file = "llvmlite-0.44.0-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:9fbadbfba8422123bab5535b293da1cf72f9f478a65645ecd73e781f962ca614"},
     {file = "llvmlite-0.44.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cccf8eb28f24840f2689fb1a45f9c0f7e582dd24e088dcf96e424834af11f791"},
@@ -3680,6 +3626,7 @@ files = [
     {file = "MarkupSafe-3.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:6e296a513ca3d94054c2c881cc913116e90fd030ad1c656b3869762b754f5f8a"},
     {file = "markupsafe-3.0.2.tar.gz", hash = "sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0"},
 ]
+markers = {main = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\""}
 
 [[package]]
 name = "marshmallow"
@@ -3936,9 +3883,10 @@ files = [
 name = "mpmath"
 version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\""
 files = [
     {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
     {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
@@ -4078,9 +4026,10 @@ typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 name = "multiprocess"
 version = "0.70.16"
 description = "better multiprocessing and multithreading in Python"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""
 files = [
     {file = "multiprocess-0.70.16-pp310-pypy310_pp73-macosx_10_13_x86_64.whl", hash = "sha256:476887be10e2f59ff183c006af746cb6f1fd0eadcfd4ef49e605cbe2659920ee"},
     {file = "multiprocess-0.70.16-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d951bed82c8f73929ac82c61f01a7b5ce8f3e5ef40f5b52553b4f547ce2b08ec"},
@@ -4236,10 +4185,10 @@ files = [
 name = "networkx"
 version = "3.2.1"
 description = "Python package for creating and manipulating graphs and networks"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version < \"3.11\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
     {file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"},
@@ -4256,10 +4205,10 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 name = "networkx"
 version = "3.5"
 description = "Python package for creating and manipulating graphs and networks"
-optional = false
+optional = true
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "python_version >= \"3.11\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec"},
     {file = "networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037"},
@@ -4314,9 +4263,10 @@ files = [
 name = "nltk"
 version = "3.9.1"
 description = "Natural Language Toolkit"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"nlp\""
 files = [
     {file = "nltk-3.9.1-py3-none-any.whl", hash = "sha256:4fa26829c5b00715afe3061398a8989dc643b92ce7dd93fb4585a70930d168a1"},
     {file = "nltk-3.9.1.tar.gz", hash = "sha256:87d127bd3de4bd89a4f81265e5fa59cb1b199b27440175370f7417d2bc7ae868"},
@@ -4394,10 +4344,10 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 name = "numba"
 version = "0.60.0"
 description = "compiling Python code using LLVM"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version < \"3.11\" and (extra == \"all\" or extra == \"explainability\")"
 files = [
     {file = "numba-0.60.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d761de835cd38fb400d2c26bb103a2726f548dc30368853121d66201672e651"},
     {file = "numba-0.60.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:159e618ef213fba758837f9837fb402bbe65326e60ba0633dbe6c7f274d42c1b"},
@@ -4430,10 +4380,10 @@ numpy = ">=1.22,<2.1"
 name = "numba"
 version = "0.61.2"
 description = "compiling Python code using LLVM"
-optional = false
+optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "python_version >= \"3.11\" and (extra == \"all\" or extra == \"explainability\")"
 files = [
     {file = "numba-0.61.2-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:cf9f9fc00d6eca0c23fc840817ce9f439b9f03c8f03d6246c0e7f0cb15b7162a"},
     {file = "numba-0.61.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ea0247617edcb5dd61f6106a56255baab031acc4257bddaeddb3a1003b4ca3fd"},
@@ -4588,10 +4538,10 @@ files = [
 name = "nvidia-cublas-cu12"
 version = "12.8.4.1"
 description = "CUBLAS native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b86f6dd8935884615a0683b663891d43781b819ac4f2ba2b0c9604676af346d0"},
     {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142"},
@@ -4602,10 +4552,10 @@ files = [
 name = "nvidia-cuda-cupti-cu12"
 version = "12.8.90"
 description = "CUDA profiling tools runtime libs."
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4412396548808ddfed3f17a467b104ba7751e6b58678a4b840675c56d21cf7ed"},
     {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182"},
@@ -4616,10 +4566,10 @@ files = [
 name = "nvidia-cuda-nvrtc-cu12"
 version = "12.8.93"
 description = "NVRTC native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994"},
     {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc1fec1e1637854b4c0a65fb9a8346b51dd9ee69e61ebaccc82058441f15bce8"},
@@ -4630,10 +4580,10 @@ files = [
 name = "nvidia-cuda-runtime-cu12"
 version = "12.8.90"
 description = "CUDA Runtime native Libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d"},
     {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90"},
@@ -4644,10 +4594,10 @@ files = [
 name = "nvidia-cudnn-cu12"
 version = "9.10.2.21"
 description = "cuDNN runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8"},
     {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8"},
@@ -4661,10 +4611,10 @@ nvidia-cublas-cu12 = "*"
 name = "nvidia-cufft-cu12"
 version = "11.3.3.83"
 description = "CUFFT native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a"},
     {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74"},
@@ -4678,10 +4628,10 @@ nvidia-nvjitlink-cu12 = "*"
 name = "nvidia-cufile-cu12"
 version = "1.13.1.3"
 description = "cuFile GPUDirect libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc"},
     {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:4beb6d4cce47c1a0f1013d72e02b0994730359e17801d395bdcbf20cfb3bb00a"},
@@ -4691,10 +4641,10 @@ files = [
 name = "nvidia-curand-cu12"
 version = "10.3.9.90"
 description = "CURAND native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd"},
     {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9"},
@@ -4705,10 +4655,10 @@ files = [
 name = "nvidia-cusolver-cu12"
 version = "11.7.3.90"
 description = "CUDA solver native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0"},
     {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450"},
@@ -4724,10 +4674,10 @@ nvidia-nvjitlink-cu12 = "*"
 name = "nvidia-cusparse-cu12"
 version = "12.5.8.93"
 description = "CUSPARSE native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc"},
     {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b"},
@@ -4741,10 +4691,10 @@ nvidia-nvjitlink-cu12 = "*"
 name = "nvidia-cusparselt-cu12"
 version = "0.7.1"
 description = "NVIDIA cuSPARSELt"
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8878dce784d0fac90131b6817b607e803c36e629ba34dc5b433471382196b6a5"},
     {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623"},
@@ -4755,10 +4705,10 @@ files = [
 name = "nvidia-nccl-cu12"
 version = "2.27.3"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\" and (platform_machine == \"x86_64\" or extra == \"all\" or extra == \"xgboost\")"
+markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\" and (platform_machine == \"x86_64\" or extra == \"all\" or extra == \"xgboost\") and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\" or extra == \"xgboost\")"
 files = [
     {file = "nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9ddf1a245abc36c550870f26d537a9b6087fb2e2e3d6e0ef03374c6fd19d984f"},
     {file = "nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adf27ccf4238253e0b826bce3ff5fa532d65fc42322c8bfdfaf28024c0fbe039"},
@@ -4768,10 +4718,10 @@ files = [
 name = "nvidia-nvjitlink-cu12"
 version = "12.8.93"
 description = "Nvidia JIT LTO Library"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88"},
     {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:adccd7161ace7261e01bb91e44e88da350895c270d23f744f0820c818b7229e7"},
@@ -4782,10 +4732,10 @@ files = [
 name = "nvidia-nvtx-cu12"
 version = "12.8.90"
 description = "NVIDIA Tools Extension"
-optional = false
+optional = true
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7ad891da111ebafbf7e015d34879f7112832fc239ff0d7d776b6cb685274615"},
     {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f"},
@@ -5105,9 +5055,10 @@ files = [
 name = "patsy"
 version = "1.0.1"
 description = "A Python package for describing statistical models and for building design matrices."
-optional = false
+optional = true
 python-versions = ">=3.6"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"stats\" or extra == \"credit-risk\""
 files = [
     {file = "patsy-1.0.1-py2.py3-none-any.whl", hash = "sha256:751fb38f9e97e62312e921a1954b81e1bb2bcda4f5eeabaf94db251ee791509c"},
     {file = "patsy-1.0.1.tar.gz", hash = "sha256:e786a9391eec818c054e359b737bbce692f051aee4c661f4141cc88fb459c0c4"},
@@ -5651,9 +5602,10 @@ tests = ["pytest"]
 name = "pyarrow"
 version = "21.0.0"
 description = "Python library for Apache Arrow"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""
 files = [
     {file = "pyarrow-21.0.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:e563271e2c5ff4d4a4cbeb2c83d5cf0d4938b891518e676025f7268c6fe5fe26"},
     {file = "pyarrow-21.0.0-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:fee33b0ca46f4c85443d6c450357101e47d53e6c3f008d658c27a2d020d44c79"},
@@ -6313,6 +6265,7 @@ files = [
     {file = "PyYAML-6.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:39693e1f8320ae4f43943590b49779ffb98acb81f788220ea932a6b6c51004d8"},
     {file = "pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e"},
 ]
+markers = {main = "extra == \"all\" or extra == \"huggingface\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""}
 
 [[package]]
 name = "pyzmq"
@@ -6705,9 +6658,10 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 name = "rouge"
 version = "1.0.1"
 description = "Full Python ROUGE Score Implementation (not a wrapper)"
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"nlp\""
 files = [
     {file = "rouge-1.0.1-py3-none-any.whl", hash = "sha256:28d118536e8c774dc47d1d15ec266479b4dd0914c4672ce117d4002789bdc644"},
     {file = "rouge-1.0.1.tar.gz", hash = "sha256:12b48346ca47d6bcf3c45061f315452b9ccec0620ee895ec85b7efc3d54aae34"},
@@ -6885,9 +6839,10 @@ files = [
 name = "safetensors"
 version = "0.6.2"
 description = ""
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"huggingface\" or extra == \"llm\" or extra == \"nlp\""
 files = [
     {file = "safetensors-0.6.2-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:9c85ede8ec58f120bad982ec47746981e210492a6db876882aa021446af8ffba"},
     {file = "safetensors-0.6.2-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:d6675cf4b39c98dbd7d940598028f3742e0375a6b4d4277e76beb0c35f4b843b"},
@@ -7150,9 +7105,10 @@ test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6
 name = "scorecardpy"
 version = "0.1.9.7"
 description = "Credit Risk Scorecard"
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"credit-risk\""
 files = [
     {file = "scorecardpy-0.1.9.7.tar.gz", hash = "sha256:a81c7e6f3bf5f10a87b61af73b25f1fc8bc5acbadf5d9e38c3addb02df128d03"},
 ]
@@ -7344,7 +7300,7 @@ files = [
     {file = "setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922"},
     {file = "setuptools-80.9.0.tar.gz", hash = "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c"},
 ]
-markers = {main = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or python_version == \"3.12\""}
+markers = {main = "(extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\") and (platform_system == \"Linux\" or python_version == \"3.12\") and (platform_machine == \"x86_64\" or python_version == \"3.12\")"}
 
 [package.extras]
 check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.8.0) ; sys_platform != \"cygwin\""]
@@ -7359,9 +7315,10 @@ type = ["importlib_metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.deve
 name = "shap"
 version = "0.48.0"
 description = "A unified approach to explain the output of any machine learning model."
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"explainability\""
 files = [
     {file = "shap-0.48.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b6b64adfd5febbed387c9c4c18c5c40b58b8932fad093dd8751b1531f09c4b2f"},
     {file = "shap-0.48.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:40c120d872aa4ea90bab41892b9ef56faea0a2111e84c83df176ed862481a47e"},
@@ -7432,9 +7389,10 @@ files = [
 name = "slicer"
 version = "0.0.8"
 description = "A small package for big slicing."
-optional = false
+optional = true
 python-versions = ">=3.6"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"explainability\""
 files = [
     {file = "slicer-0.0.8-py3-none-any.whl", hash = "sha256:6c206258543aecd010d497dc2eca9d2805860a0b3758673903456b7df7934dc3"},
     {file = "slicer-0.0.8.tar.gz", hash = "sha256:2e7553af73f0c0c2d355f4afcc3ecf97c6f2156fcf4593955c3f56cf6c4d6eb7"},
@@ -7787,9 +7745,10 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 name = "statsmodels"
 version = "0.14.5"
 description = "Statistical computations and models for Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"stats\" or extra == \"credit-risk\""
 files = [
     {file = "statsmodels-0.14.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9fc2b5cdc0c95cba894849651fec1fa1511d365e3eb72b0cc75caac44077cd48"},
     {file = "statsmodels-0.14.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b8d96b0bbaeabd3a557c35cc7249baa9cfbc6dd305c32a9f2cbdd7f46c037e7f"},
@@ -7839,9 +7798,10 @@ docs = ["ipykernel", "jupyter_client", "matplotlib", "nbconvert", "nbformat", "n
 name = "sympy"
 version = "1.14.0"
 description = "Computer algebra system (CAS) in Python"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\""
 files = [
     {file = "sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5"},
     {file = "sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517"},
@@ -7911,9 +7871,10 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 name = "textblob"
 version = "0.18.0.post0"
 description = "Simple, Pythonic text processing. Sentiment analysis, part-of-speech tagging, noun phrase parsing, and more."
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"nlp\""
 files = [
     {file = "textblob-0.18.0.post0-py3-none-any.whl", hash = "sha256:dd0c7ec4eb7b9346ec0a3f136a63eba13e0f59890d2a693d3d6aeb8371949dca"},
     {file = "textblob-0.18.0.post0.tar.gz", hash = "sha256:8131c52c630bcdf61d04c359f939c98d5b836a01fba224d9e7ae22fc274e0ccb"},
@@ -8010,9 +7971,10 @@ test = ["pytest", "ruff"]
 name = "tokenizers"
 version = "0.21.4"
 description = ""
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"huggingface\" or extra == \"llm\" or extra == \"nlp\""
 files = [
     {file = "tokenizers-0.21.4-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:2ccc10a7c3bcefe0f242867dc914fc1226ee44321eb618cfe3019b5df3400133"},
     {file = "tokenizers-0.21.4-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:5e2f601a8e0cd5be5cc7506b20a79112370b9b3e9cb5f13f68ab11acd6ca7d60"},
@@ -8086,9 +8048,10 @@ files = [
 name = "torch"
 version = "2.8.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
-optional = false
+optional = true
 python-versions = ">=3.9.0"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\""
 files = [
     {file = "torch-2.8.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:0be92c08b44009d4131d1ff7a8060d10bafdb7ddcb7359ef8d8c5169007ea905"},
     {file = "torch-2.8.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:89aa9ee820bb39d4d72b794345cccef106b574508dd17dbec457949678c76011"},
@@ -8234,9 +8197,10 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 name = "transformers"
 version = "4.55.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
-optional = false
+optional = true
 python-versions = ">=3.9.0"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"huggingface\" or extra == \"llm\" or extra == \"nlp\""
 files = [
     {file = "transformers-4.55.0-py3-none-any.whl", hash = "sha256:29d9b8800e32a4a831bb16efb5f762f6a9742fef9fce5d693ed018d19b106490"},
     {file = "transformers-4.55.0.tar.gz", hash = "sha256:15aa138a05d07a15b30d191ea2c45e23061ebf9fcc928a1318e03fe2234f3ae1"},
@@ -8308,10 +8272,10 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 name = "triton"
 version = "3.4.0"
 description = "A language and compiler for custom Deep Learning operations"
-optional = false
+optional = true
 python-versions = "<3.14,>=3.9"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"all\" or extra == \"llm\" or extra == \"pytorch\" or extra == \"nlp\")"
 files = [
     {file = "triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7ff2785de9bc02f500e085420273bb5cc9c9bb767584a4aa28d6e360cec70128"},
     {file = "triton-3.4.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7b70f5e6a41e52e48cfc087436c8a28c17ff98db369447bcaff3b887a3ab4467"},
@@ -8601,9 +8565,10 @@ scikit-learn = ["scikit-learn"]
 name = "xxhash"
 version = "3.5.0"
 description = "Python binding for xxHash"
-optional = false
+optional = true
 python-versions = ">=3.7"
 groups = ["main"]
+markers = "extra == \"all\" or extra == \"llm\" or extra == \"nlp\" or extra == \"datasets\""
 files = [
     {file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"},
     {file = "xxhash-3.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3171f693dbc2cef6477054a665dc255d996646b4023fe56cb4db80e26f4cc520"},
@@ -9002,15 +8967,18 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["langchain-openai", "langdetect", "pycocoevalcap", "ragas", "scipy", "sentencepiece", "statsmodels", "torch", "transformers", "xgboost"]
+all = ["arch", "bert-score", "evaluate", "langchain-openai", "langdetect", "nltk", "pycocoevalcap", "ragas", "rouge", "scipy", "scorecardpy", "sentencepiece", "shap", "statsmodels", "textblob", "torch", "transformers", "xgboost"]
+credit-risk = ["scorecardpy"]
+datasets = ["datasets"]
+explainability = ["shap"]
 huggingface = ["sentencepiece", "transformers"]
 llm = ["langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"]
-nlp = ["langdetect"]
+nlp = ["bert-score", "evaluate", "langdetect", "nltk", "rouge", "textblob"]
 pytorch = ["torch"]
-stats = ["scipy", "statsmodels"]
+stats = ["arch", "scipy", "statsmodels"]
 xgboost = ["xgboost"]
 
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<3.13"
-content-hash = "571193985e8dea9fa04aeaf37e14d4b16a2d84f9399892eed3e4c272bf19aef4"
+content-hash = "e8b071e401a8815a824af7a145624cbbba027f3f103035b4c8fb29697df10102"

From 39584ef908b4cb438ceb75fa55f5618cd2dd5b10 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 18:37:59 -0400
Subject: [PATCH 45/47] chore: update pyproject metadata

---
 pyproject.toml | 25 +++----------------------
 1 file changed, 3 insertions(+), 22 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 27b0503c1..f6b03f893 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,17 +1,3 @@
-[tool.poetry]
-authors = [
-  "Andres Rodriguez <andres@validmind.ai>",
-  "Juan Martinez <juan@validmind.ai>",
-  "Anil Sorathiya <anil@validmind.ai>",
-  "Luis Pallares <luis@validmind.ai>",
-  "John Halz <john@validmind.ai>",
-]
-description = "ValidMind Library"
-license = "Commercial License"
-name = "validmind"
-readme = "README.pypi.md"
-version = "2.9.0"
-
 [project]
 name = "validmind"
 version = "2.9.0"
@@ -48,11 +34,6 @@ dependencies = [
   "beautifulsoup4",
 ]
 
-classifiers = [
-  "License :: OSI Approved :: GNU Affero General Public License v3",
-  "License :: Other/Proprietary License",
-]
-
 [project.optional-dependencies]
 all = [
   "torch (>=2.0.0)",
@@ -101,9 +82,6 @@ explainability = ["shap (>=0.46.0)"]
 credit_risk = ["scorecardpy (>=0.1.9.6,<0.2.0)"]
 datasets = ["datasets (>=2.10.0,<3.0.0)"]
 
-[project.scripts]
-vm-create-new-test = "scripts.create_new_test:generate_test"
-
 [tool.poetry.group.dev.dependencies]
 black = "^22.1.0"
 click = "*"
@@ -124,6 +102,9 @@ sphinx-rtd-theme = "^1.2.0"
 tox = "^4.0.0"
 twine = "^4.0.2"
 
+[project.scripts]
+vm-create-new-test = "scripts.create_new_test:generate_test"
+
 [build-system]
 build-backend = "poetry.core.masonry.api"
 requires = ["poetry-core>=1.9.0"]

From 2911bf430697dea80902650a8f4b31cc7e5cc803 Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Fri, 8 Aug 2025 20:24:47 -0400
Subject: [PATCH 46/47] fix: log unit metrics only for scalar values in
 TestResult class

---
 validmind/vm_models/result/result.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/validmind/vm_models/result/result.py b/validmind/vm_models/result/result.py
index 3016012d5..8e711ad4d 100644
--- a/validmind/vm_models/result/result.py
+++ b/validmind/vm_models/result/result.py
@@ -465,8 +465,10 @@ async def log_async(
             )
         )
 
-        if self.metric is not None:
-            # metrics are logged as separate entities
+        # Only log unit metrics when the metric is a scalar value.
+        # Some tests may assign a list/array of per-row metrics to `self.metric`.
+        # Those should not be sent to the unit-metric endpoint which expects scalars.
+        if self.metric is not None and not hasattr(self.metric, "__len__"):
             tasks.append(
                 api_client.alog_metric(
                     key=self.result_id,

From 135ba20cdd7b1872b19f21ccd303ec5ead6446fb Mon Sep 17 00:00:00 2001
From: John Walz <john@validmind.ai>
Date: Mon, 11 Aug 2025 17:23:31 -0400
Subject: [PATCH 47/47] chore: remove unused dependencies and tox configuration

---
 .github/workflows/dependency-testing.yaml |  18 ++--
 poetry.lock                               |  87 +---------------
 pyproject.toml                            |   1 -
 tox.ini                                   | 116 ----------------------
 4 files changed, 13 insertions(+), 209 deletions(-)
 delete mode 100644 tox.ini

diff --git a/.github/workflows/dependency-testing.yaml b/.github/workflows/dependency-testing.yaml
index 564064275..b7deb2e35 100644
--- a/.github/workflows/dependency-testing.yaml
+++ b/.github/workflows/dependency-testing.yaml
@@ -6,7 +6,6 @@ on:
     paths:
       - 'pyproject.toml'
       - 'requirements/**'
-      - 'tox.ini'
       - 'validmind/**'
       - 'tests/**'
   pull_request:
@@ -14,7 +13,6 @@ on:
     paths:
       - 'pyproject.toml'
       - 'requirements/**'
-      - 'tox.ini'
       - 'validmind/**'
       - 'tests/**'
   schedule:
@@ -117,18 +115,26 @@ jobs:
           sudo apt-get update
           sudo apt-get install -y build-essential libomp-dev
 
-      - name: Install tox
+      - name: Install Poetry
         run: |
           python -m pip install --upgrade pip
-          pip install tox
+          pip install poetry
 
       - name: Test pip freeze environments
-        run: |
-          tox -e freeze
         env:
           OMP_NUM_THREADS: 1
           MKL_NUM_THREADS: 1
           PIP_NO_CACHE_DIR: 1
+        run: |
+          set -euxo pipefail
+          if [ -z "${FREEZE_FILE:-}" ] || [ ! -f "$FREEZE_FILE" ]; then
+            echo "Set FREEZE_FILE env var to a pip freeze file path" >&2
+            exit 1
+          fi
+          python -m pip install --upgrade pip
+          pip install -r "$FREEZE_FILE"
+          poetry install --no-deps
+          python -m tests.test_unit_tests | cat
 
   report-status:
     name: Report Dependency Testing Status
diff --git a/poetry.lock b/poetry.lock
index 6c0cd12fe..465ddaade 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -714,18 +714,6 @@ files = [
 [package.dependencies]
 cffi = ">=1.0.0"
 
-[[package]]
-name = "cachetools"
-version = "6.1.0"
-description = "Extensible memoizing collections and decorators"
-optional = false
-python-versions = ">=3.9"
-groups = ["dev"]
-files = [
-    {file = "cachetools-6.1.0-py3-none-any.whl", hash = "sha256:1c7bb3cf9193deaf3508b7c5f2a79986c13ea38965c5adcff1f84519cf39163e"},
-    {file = "cachetools-6.1.0.tar.gz", hash = "sha256:b4c4f404392848db3ce7aac34950d17be4d864da4b8b66911008e430bc544587"},
-]
-
 [[package]]
 name = "certifi"
 version = "2025.8.3"
@@ -830,18 +818,6 @@ files = [
     {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
 ]
 
-[[package]]
-name = "chardet"
-version = "5.2.0"
-description = "Universal encoding detector for Python 3"
-optional = false
-python-versions = ">=3.7"
-groups = ["dev"]
-files = [
-    {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
-    {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
-]
-
 [[package]]
 name = "charset-normalizer"
 version = "3.4.2"
@@ -5287,22 +5263,6 @@ dev-optional = ["anywidget", "colorcet", "fiona (<=1.9.6) ; python_version <= \"
 express = ["numpy"]
 kaleido = ["kaleido (>=1.0.0)"]
 
-[[package]]
-name = "pluggy"
-version = "1.6.0"
-description = "plugin and hook calling mechanisms for python"
-optional = false
-python-versions = ">=3.9"
-groups = ["dev"]
-files = [
-    {file = "pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746"},
-    {file = "pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3"},
-]
-
-[package.extras]
-dev = ["pre-commit", "tox"]
-testing = ["coverage", "pytest", "pytest-benchmark"]
-
 [[package]]
 name = "polars"
 version = "1.32.2"
@@ -6049,26 +6009,6 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
-[[package]]
-name = "pyproject-api"
-version = "1.9.1"
-description = "API to interact with the python pyproject.toml based projects"
-optional = false
-python-versions = ">=3.9"
-groups = ["dev"]
-files = [
-    {file = "pyproject_api-1.9.1-py3-none-any.whl", hash = "sha256:7d6238d92f8962773dd75b5f0c4a6a27cce092a14b623b811dba656f3b628948"},
-    {file = "pyproject_api-1.9.1.tar.gz", hash = "sha256:43c9918f49daab37e302038fc1aed54a8c7a91a9fa935d00b9a485f37e0f5335"},
-]
-
-[package.dependencies]
-packaging = ">=25"
-tomli = {version = ">=2.2.1", markers = "python_version < \"3.11\""}
-
-[package.extras]
-docs = ["furo (>=2024.8.6)", "sphinx-autodoc-typehints (>=3.2)"]
-testing = ["covdefaults (>=2.3)", "pytest (>=8.3.5)", "pytest-cov (>=6.1.1)", "pytest-mock (>=3.14)", "setuptools (>=80.3.1)"]
-
 [[package]]
 name = "pysbd"
 version = "0.3.4"
@@ -8130,31 +8070,6 @@ files = [
     {file = "tornado-6.5.1.tar.gz", hash = "sha256:84ceece391e8eb9b2b95578db65e920d2a61070260594819589609ba9bc6308c"},
 ]
 
-[[package]]
-name = "tox"
-version = "4.28.4"
-description = "tox is a generic virtualenv management and test command line tool"
-optional = false
-python-versions = ">=3.9"
-groups = ["dev"]
-files = [
-    {file = "tox-4.28.4-py3-none-any.whl", hash = "sha256:8d4ad9ee916ebbb59272bb045e154a10fa12e3bbdcf94cc5185cbdaf9b241f99"},
-    {file = "tox-4.28.4.tar.gz", hash = "sha256:b5b14c6307bd8994ff1eba5074275826620325ee1a4f61316959d562bfd70b9d"},
-]
-
-[package.dependencies]
-cachetools = ">=6.1"
-chardet = ">=5.2"
-colorama = ">=0.4.6"
-filelock = ">=3.18"
-packaging = ">=25"
-platformdirs = ">=4.3.8"
-pluggy = ">=1.6"
-pyproject-api = ">=1.9.1"
-tomli = {version = ">=2.2.1", markers = "python_version < \"3.11\""}
-typing-extensions = {version = ">=4.14.1", markers = "python_version < \"3.11\""}
-virtualenv = ">=20.31.2"
-
 [[package]]
 name = "tqdm"
 version = "4.67.1"
@@ -8981,4 +8896,4 @@ xgboost = ["xgboost"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<3.13"
-content-hash = "e8b071e401a8815a824af7a145624cbbba027f3f103035b4c8fb29697df10102"
+content-hash = "c35d817e2338b5426965c05d442fb97ded7635f1f0fe50f2c4d8d23d96fddcb1"
diff --git a/pyproject.toml b/pyproject.toml
index 8b9ac32f1..b5c53a13f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -99,7 +99,6 @@ pre-commit = "^3.3.3"
 sphinx = "^6.1.3"
 sphinx-markdown-builder = "^0.5.5"
 sphinx-rtd-theme = "^1.2.0"
-tox = "^4.0.0"
 twine = "^4.0.2"
 
 [project.scripts]
diff --git a/tox.ini b/tox.ini
deleted file mode 100644
index d03737095..000000000
--- a/tox.ini
+++ /dev/null
@@ -1,116 +0,0 @@
-[tox]
-envlist =
-    py{39,310,311,312}
-    py{39,310,311,312}-min
-    py{39,310,311,312}-max
-    docs
-    lint
-isolated_build = true
-skip_missing_interpreters = true
-
-[testenv]
-deps =
-    poetry
-setenv =
-    # Limit OpenMP on Mac to prevent segfaults
-    OMP_NUM_THREADS = 1
-    MKL_NUM_THREADS = 1
-allowlist_externals =
-    poetry
-    pip
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install --all-extras
-commands =
-    poetry run python -m unittest discover tests
-    # Run the custom unit test orchestrator directly to avoid unittest exit code 5 (no tests collected)
-    poetry run python -m tests.test_unit_tests
-
-[testenv:py39-min]
-description = Test with minimum supported dependency versions (Python 3.9)
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install
-    python scripts/generate_constraints.py --profile min
-    pip install -r requirements/constraints/generated-min.txt
-
-[testenv:py310-min]
-description = Test with minimum supported dependency versions (Python 3.10)
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install
-    python scripts/generate_constraints.py --profile min
-    pip install -r requirements/constraints/generated-min.txt
-
-[testenv:py311-min]
-description = Test with minimum supported dependency versions (Python 3.11)
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install
-    python scripts/generate_constraints.py --profile min
-    pip install -r requirements/constraints/generated-min.txt
-
-[testenv:py312-min]
-description = Test with minimum supported dependency versions (Python 3.12)
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install
-    python scripts/generate_constraints.py --profile min
-    pip install -r requirements/constraints/generated-min.txt
-
-[testenv:py{39,310,311,312}-max]
-description = Test with latest dependency versions
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    # Dynamically generate "max" constraints (latest compatible)
-    python scripts/generate_constraints.py --profile max
-    pip install -r requirements/constraints/generated-max.txt
-    # Install project without resolving deps again (use constraints already installed)
-    poetry install --no-deps
-
-[testenv:docs]
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install --all-extras
-commands =
-    poetry run make docs
-
-[testenv:lint]
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    poetry install --all-extras
-commands =
-    poetry run make lint
-    poetry run make verify-copyright
-    poetry run make verify-exposed-credentials
-
-[testenv:freeze]
-description = Test with a specific pip freeze file (specify via FREEZE_FILE env var)
-allowlist_externals =
-    poetry
-    pip
-    bash
-commands_pre =
-    python -m pip install --upgrade pip
-    pip install poetry
-    poetry config virtualenvs.create false
-    bash -c 'if [ -n "$FREEZE_FILE" ] && [ -f "$FREEZE_FILE" ]; then pip install -r "$FREEZE_FILE"; else echo "Set FREEZE_FILE env var to pip freeze file path"; exit 1; fi'
-    poetry install --no-deps
-commands =
-    poetry run python -m unittest discover tests -v