diff --git a/poetry.lock b/poetry.lock
index e8095a84..87d840f0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,127 +1,139 @@
-# This file is automatically @generated by Poetry 2.1.1 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 = "aiosqlite"
-version = "0.19.0"
+version = "0.21.0"
 description = "asyncio bridge to the standard sqlite3 module"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["test"]
 files = [
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 ]
 
+[package.dependencies]
+typing_extensions = ">=4.0"
+
 [package.extras]
-dev = ["aiounittest (==1.4.1) ; python_version < \"3.8\"", "attribution (==1.6.2)", "black (==23.3.0)", "coverage[toml] (==7.2.3)", "flake8 (==5.0.4)", "flake8-bugbear (==23.3.12)", "flit (==3.7.1)", "mypy (==1.2.0)", "ufmt (==2.1.0)", "usort (==1.0.6)"]
-docs = ["sphinx (==6.1.3) ; python_version >= \"3.8\"", "sphinx-mdinclude (==0.5.3)"]
+dev = ["attribution (==1.7.1)", "black (==24.3.0)", "build (>=1.2)", "coverage[toml] (==7.6.10)", "flake8 (==7.0.0)", "flake8-bugbear (==24.12.12)", "flit (==3.10.1)", "mypy (==1.14.1)", "ufmt (==2.5.1)", "usort (==1.0.8.post1)"]
+docs = ["sphinx (==8.1.3)", "sphinx-mdinclude (==0.6.1)"]
 
 [[package]]
 name = "alembic"
-version = "1.12.1"
+version = "1.16.5"
 description = "A database migration tool for SQLAlchemy."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
 Mako = "*"
-SQLAlchemy = ">=1.3.0"
-typing-extensions = ">=4"
+SQLAlchemy = ">=1.4.0"
+typing-extensions = ">=4.12"
 
 [package.extras]
-tz = ["python-dateutil"]
+tz = ["tzdata"]
 
 [[package]]
 name = "annotated-types"
-version = "0.6.0"
+version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [[package]]
 name = "anyio"
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+version = "4.11.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main", "test"]
 files = [
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 ]
 
 [package.dependencies]
 idna = ">=2.8"
 sniffio = ">=1.1"
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 [package.extras]
-doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
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-trio = ["trio (<0.22)"]
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 [[package]]
 name = "asyncpg"
-version = "0.28.0"
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 description = "An asyncio PostgreSQL driver"
 optional = false
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+gssauth = ["gssapi ; platform_system != \"Windows\"", "sspilib ; platform_system == \"Windows\""]
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 [[package]]
 name = "backoff"
@@ -137,149 +149,121 @@ files = [
 
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+version = "4.3.0"
 description = "Modern password hashing for your software and your servers"
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 ]
 
-[package.dependencies]
-cffi = ">=1.1"
-
 [package.extras]
 tests = ["pytest (>=3.2.1,!=3.3.0)"]
 typecheck = ["mypy"]
 
 [[package]]
 name = "boto3"
-version = "1.28.76"
+version = "1.40.38"
 description = "The AWS SDK for Python"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 [package.extras]
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 [package.extras]
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 [[package]]
 name = "certifi"
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 description = "Python package for providing Mozilla's CA Bundle."
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-[package.dependencies]
-pycparser = "*"
-
 [[package]]
 name = "cfgv"
 version = "3.4.0"
@@ -294,114 +278,103 @@ files = [
 
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@@ -422,64 +395,116 @@ markers = {main = "platform_system == \"Windows\"", test = "sys_platform == \"wi
 
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 ]
 
 [package.extras]
-docs = ["Sphinx"]
-test = ["objgraph", "psutil"]
+docs = ["Sphinx", "furo"]
+test = ["objgraph", "psutil", "setuptools"]
 
 [[package]]
 name = "h11"
@@ -666,14 +733,14 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "identify"
-version = "2.5.31"
+version = "2.6.14"
 description = "File identification library for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["quality"]
 files = [
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 ]
 
 [package.extras]
@@ -681,26 +748,29 @@ license = ["ukkonen"]
 
 [[package]]
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-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
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 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
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-version = "2.0.0"
+version = "2.1.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["test"]
 files = [
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 [[package]]
@@ -717,14 +787,14 @@ files = [
 
 [[package]]
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-version = "1.2.4"
+version = "1.3.10"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 [package.dependencies]
@@ -737,14 +807,14 @@ testing = ["pytest"]
 
 [[package]]
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+version = "4.0.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
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 [package.dependencies]
@@ -752,82 +822,82 @@ mdurl = ">=0.1,<1.0"
 
 [package.extras]
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-plugins = ["mdit-py-plugins"]
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 profiling = ["gprof2dot"]
-rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
-testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+rtd = ["ipykernel", "jupyter_sphinx", "mdit-py-plugins (>=0.5.0)", "myst-parser", "pyyaml", "sphinx", "sphinx-book-theme (>=1.0,<2.0)", "sphinx-copybutton", "sphinx-design"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions", "requests"]
 
 [[package]]
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+version = "3.0.2"
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-[package.dependencies]
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+]
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+]
+python-dateutil = ">=2.8.2"
+pytz = ">=2020.1"
+tzdata = ">=2022.7"
+
+[package.extras]
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+aws = ["s3fs (>=2022.11.0)"]
+clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"]
+compression = ["zstandard (>=0.19.0)"]
+computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"]
+feather = ["pyarrow (>=10.0.1)"]
+fss = ["fsspec (>=2022.11.0)"]
+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 = "passlib"
 version = "1.7.4"
@@ -963,70 +1221,73 @@ totp = ["cryptography"]
 
 [[package]]
 name = "platformdirs"
-version = "3.11.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "4.4.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["quality"]
 files = [
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-    {file = "platformdirs-3.11.0.tar.gz", hash = "sha256:cf8ee52a3afdb965072dcc652433e0c7e3e40cf5ea1477cd4b3b1d2eb75495b3"},
+    {file = "platformdirs-4.4.0-py3-none-any.whl", hash = "sha256:abd01743f24e5287cd7a5db3752faf1a2d65353f38ec26d98e25a6db65958c85"},
+    {file = "platformdirs-4.4.0.tar.gz", hash = "sha256:ca753cf4d81dc309bc67b0ea38fd15dc97bc30ce419a7f58d13eb3bf14c4febf"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
+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 = "pluggy"
-version = "1.5.0"
+version = "1.6.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["test"]
 files = [
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+    {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 = ["pytest", "pytest-benchmark"]
+testing = ["coverage", "pytest", "pytest-benchmark"]
 
 [[package]]
 name = "posthog"
-version = "3.0.2"
+version = "3.25.0"
 description = "Integrate PostHog into any python application."
 optional = false
 python-versions = "*"
 groups = ["main"]
 files = [
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+    {file = "posthog-3.25.0-py2.py3-none-any.whl", hash = "sha256:85db78c13d1ecb11aed06fad53759c4e8fb3633442c2f3d0336bc0ce8a585d30"},
+    {file = "posthog-3.25.0.tar.gz", hash = "sha256:9168f3e7a0a5571b6b1065c41b3c171fbc68bfe72c3ac0bfd6e3d2fcdb7df2ca"},
 ]
 
 [package.dependencies]
 backoff = ">=1.10.0"
+distro = ">=1.5.0"
 monotonic = ">=1.5"
 python-dateutil = ">2.1"
 requests = ">=2.7,<3.0"
 six = ">=1.5"
 
 [package.extras]
-dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
+dev = ["black", "django-stubs", "flake8", "flake8-print", "isort", "lxml", "mypy", "mypy-baseline", "pre-commit", "pydantic", "types-mock", "types-python-dateutil", "types-requests", "types-setuptools", "types-six"]
+langchain = ["langchain (>=0.2.0)"]
 sentry = ["django", "sentry-sdk"]
-test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest"]
+test = ["anthropic", "coverage", "django", "flake8", "freezegun (==1.5.1)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "langgraph", "mock (>=2.0.0)", "openai", "parameterized (>=0.8.1)", "pydantic", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
 
 [[package]]
 name = "pre-commit"
-version = "4.2.0"
+version = "4.3.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
 groups = ["quality"]
 files = [
-    {file = "pre_commit-4.2.0-py2.py3-none-any.whl", hash = "sha256:a009ca7205f1eb497d10b845e52c838a98b6cdd2102a6c8e4540e94ee75c58bd"},
-    {file = "pre_commit-4.2.0.tar.gz", hash = "sha256:601283b9757afd87d40c4c4a9b2b5de9637a8ea02eaff7adc2d0fb4e04841146"},
+    {file = "pre_commit-4.3.0-py2.py3-none-any.whl", hash = "sha256:2b0747ad7e6e967169136edffee14c16e148a778a54e4f967921aa1ebf2308d8"},
+    {file = "pre_commit-4.3.0.tar.gz", hash = "sha256:499fe450cc9d42e9d58e606262795ecb64dd05438943c62b66f6a8673da30b16"},
 ]
 
 [package.dependencies]
@@ -1038,14 +1299,14 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.20.0"
+version = "0.23.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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-    {file = "prometheus_client-0.20.0.tar.gz", hash = "sha256:287629d00b147a32dcb2be0b9df905da599b2d82f80377083ec8463309a4bb89"},
+    {file = "prometheus_client-0.23.1-py3-none-any.whl", hash = "sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99"},
+    {file = "prometheus_client-0.23.1.tar.gz", hash = "sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce"},
 ]
 
 [package.extras]
@@ -1067,152 +1328,135 @@ files = [
 fastapi = ">=0.38.1,<1.0.0"
 prometheus-client = ">=0.8.0,<1.0.0"
 
-[[package]]
-name = "pycparser"
-version = "2.21"
-description = "C parser in Python"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-groups = ["main"]
-files = [
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-    {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
-]
-
 [[package]]
 name = "pydantic"
-version = "2.4.2"
+version = "2.11.9"
 description = "Data validation using Python type hints"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "pydantic-2.11.9-py3-none-any.whl", hash = "sha256:c42dd626f5cfc1c6950ce6205ea58c93efa406da65f479dcb4029d5934857da2"},
+    {file = "pydantic-2.11.9.tar.gz", hash = "sha256:6b8ffda597a14812a7975c90b82a8a2e777d9257aba3453f973acd3c032a18e2"},
 ]
 
 [package.dependencies]
-annotated-types = ">=0.4.0"
-pydantic-core = "2.10.1"
-typing-extensions = ">=4.6.1"
+annotated-types = ">=0.6.0"
+pydantic-core = "2.33.2"
+typing-extensions = ">=4.12.2"
+typing-inspection = ">=0.4.0"
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
 
 [[package]]
 name = "pydantic-core"
-version = "2.10.1"
-description = ""
+version = "2.33.2"
+description = "Core functionality for Pydantic validation and serialization"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 version = "8.3.5"
@@ -1332,14 +1648,14 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
 name = "pytest-pretty"
-version = "1.2.0"
+version = "1.3.0"
 description = "pytest plugin for printing summary data as I want it"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["test"]
 files = [
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 [package.dependencies]
@@ -1348,14 +1664,14 @@ rich = ">=12"
 
 [[package]]
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-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 groups = ["main"]
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@@ -1363,14 +1679,14 @@ six = ">=1.5"
 
 [[package]]
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-version = "1.0.0"
+version = "1.1.1"
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 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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+[[package]]
+name = "pytz"
+version = "2025.2"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
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 ]
 
 [[package]]
 name = "requests"
-version = "2.32.3"
+version = "2.32.5"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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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"
 
@@ -1488,14 +1818,14 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rich"
-version = "13.6.0"
+version = "14.1.0"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 groups = ["test"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1535,32 +1865,32 @@ files = [
 
 [[package]]
 name = "s3transfer"
-version = "0.7.0"
+version = "0.14.0"
 description = "An Amazon S3 Transfer Manager"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
-botocore = ">=1.12.36,<2.0a.0"
+botocore = ">=1.37.4,<2.0a.0"
 
 [package.extras]
-crt = ["botocore[crt] (>=1.20.29,<2.0a.0)"]
+crt = ["botocore[crt] (>=1.37.4,<2.0a.0)"]
 
 [[package]]
 name = "sentry-sdk"
-version = "2.13.0"
+version = "2.38.0"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1584,16 +1914,20 @@ falcon = ["falcon (>=1.4)"]
 fastapi = ["fastapi (>=0.79.0)"]
 flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
 grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
+http2 = ["httpcore[http2] (==1.*)"]
 httpx = ["httpx (>=0.16.0)"]
 huey = ["huey (>=2)"]
-huggingface-hub = ["huggingface-hub (>=0.22)"]
+huggingface-hub = ["huggingface_hub (>=0.22)"]
 langchain = ["langchain (>=0.0.210)"]
+langgraph = ["langgraph (>=0.6.6)"]
+launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
 litestar = ["litestar (>=2.0.0)"]
 loguru = ["loguru (>=0.5)"]
 openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
+openfeature = ["openfeature-sdk (>=0.7.1)"]
 opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
 opentelemetry-experimental = ["opentelemetry-distro"]
-pure-eval = ["asttokens", "executing", "pure-eval"]
+pure-eval = ["asttokens", "executing", "pure_eval"]
 pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
 quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
@@ -1602,118 +1936,186 @@ sanic = ["sanic (>=0.8)"]
 sqlalchemy = ["sqlalchemy (>=1.2)"]
 starlette = ["starlette (>=0.19.1)"]
 starlite = ["starlite (>=1.48)"]
+statsig = ["statsig (>=0.55.3)"]
 tornado = ["tornado (>=6)"]
+unleash = ["UnleashClient (>=6.0.1)"]
 
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-name = "setuptools"
-version = "70.3.0"
-description = "Easily download, build, install, upgrade, and uninstall Python packages"
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+version = "2.1.2"
+description = "Manipulation and analysis of geometric objects"
 optional = false
-python-versions = ">=3.8"
-groups = ["quality"]
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
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 typing-extensions = ">=4.6.0"
 
 [package.extras]
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-aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
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-mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
+aiomysql = ["aiomysql (>=0.2.0)", "greenlet (>=1)"]
+aioodbc = ["aioodbc", "greenlet (>=1)"]
+aiosqlite = ["aiosqlite", "greenlet (>=1)", "typing_extensions (!=3.10.0.1)"]
+asyncio = ["greenlet (>=1)"]
+asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (>=1)"]
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 mssql = ["pyodbc"]
 mssql-pymssql = ["pymssql"]
 mssql-pyodbc = ["pyodbc"]
@@ -1723,7 +2125,7 @@ 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)"]
+postgresql-asyncpg = ["asyncpg", "greenlet (>=1)"]
 postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
 postgresql-psycopg = ["psycopg (>=3.0.7)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
@@ -1766,112 +2168,129 @@ SQLAlchemy = ">=2.0.14,<2.1.0"
 
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 [package.dependencies]
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 [[package]]
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 ]
 
 [[package]]
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-description = "Backported and Experimental Type Hints for Python 3.8+"
+name = "typing-inspection"
+version = "0.4.1"
+description = "Runtime typing introspection tools"
 optional = false
-python-versions = ">=3.8"
-groups = ["main", "quality"]
+python-versions = ">=3.9"
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-    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
-    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
+    {file = "typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51"},
+    {file = "typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+groups = ["main"]
+files = [
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
 ]
 
 [[package]]
 name = "urllib3"
-version = "1.26.19"
+version = "2.5.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
-groups = ["main"]
+python-versions = ">=3.9"
+groups = ["main", "quality"]
 files = [
-    {file = "urllib3-1.26.19-py2.py3-none-any.whl", hash = "sha256:37a0344459b199fce0e80b0d3569837ec6b6937435c5244e7fd73fa6006830f3"},
-    {file = "urllib3-1.26.19.tar.gz", hash = "sha256:3e3d753a8618b86d7de333b4223005f68720bcd6a7d2bcb9fbd2229ec7c1e429"},
+    {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
+    {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
 ]
 
 [package.extras]
-brotli = ["brotli (==1.0.9) ; os_name != \"nt\" and python_version < \"3\" and platform_python_implementation == \"CPython\"", "brotli (>=1.0.9) ; python_version >= \"3\" and platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; (os_name != \"nt\" or python_version >= \"3\") and platform_python_implementation != \"CPython\"", "brotlipy (>=0.6.0) ; os_name == \"nt\" and python_version < \"3\""]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress ; python_version == \"2.7\"", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "uvicorn"
-version = "0.34.2"
+version = "0.37.0"
 description = "The lightning-fast ASGI server."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "uvicorn-0.34.2-py3-none-any.whl", hash = "sha256:deb49af569084536d269fe0a6d67e3754f104cf03aba7c11c40f01aadf33c403"},
-    {file = "uvicorn-0.34.2.tar.gz", hash = "sha256:0e929828f6186353a80b58ea719861d2629d766293b6d19baf086ba31d4f3328"},
+    {file = "uvicorn-0.37.0-py3-none-any.whl", hash = "sha256:913b2b88672343739927ce381ff9e2ad62541f9f8289664fa1d1d3803fa2ce6c"},
+    {file = "uvicorn-0.37.0.tar.gz", hash = "sha256:4115c8add6d3fd536c8ee77f0e14a7fd2ebba939fed9b02583a97f80648f9e13"},
 ]
 
 [package.dependencies]
@@ -1879,30 +2298,30 @@ click = ">=7.0"
 h11 = ">=0.8"
 
 [package.extras]
-standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.24.6"
+version = "20.34.0"
 description = "Virtual Python Environment builder"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["quality"]
 files = [
-    {file = "virtualenv-20.24.6-py3-none-any.whl", hash = "sha256:520d056652454c5098a00c0f073611ccbea4c79089331f60bf9d7ba247bb7381"},
-    {file = "virtualenv-20.24.6.tar.gz", hash = "sha256:02ece4f56fbf939dbbc33c0715159951d6bf14aaf5457b092e4548e1382455af"},
+    {file = "virtualenv-20.34.0-py3-none-any.whl", hash = "sha256:341f5afa7eee943e4984a9207c025feedd768baff6753cd660c857ceb3e36026"},
+    {file = "virtualenv-20.34.0.tar.gz", hash = "sha256:44815b2c9dee7ed86e387b842a84f20b93f7f417f95886ca1996a72a4138eb1a"},
 ]
 
 [package.dependencies]
 distlib = ">=0.3.7,<1"
 filelock = ">=3.12.2,<4"
-platformdirs = ">=3.9.1,<4"
+platformdirs = ">=3.9.1,<5"
 
 [package.extras]
-docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
-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\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+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\""]
 
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.11"
-content-hash = "91ab9e0c32bf7133581d8022f07206acfe090c6bc972e0a4d7eac8eff3e5dcad"
+content-hash = "185991c55b4870b9e26d1f18167b1b8eef7db24eff7ceffb609562978e87fb31"
diff --git a/pyproject.toml b/pyproject.toml
index 95022307..48ec76af 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,12 @@ python-multipart = "==0.0.7"
 python-magic = "^0.4.17"
 boto3 = "^1.26.0"
 httpx = "^0.24.0"
+pandas = "^2.2.0"
+numpy = "^2.0.0"
+shapely = "^2.0.4"
+pyproj = "^3.6.1"
+geopy = "^2.4.1"
+networkx = "^3.2.1"
 
 [tool.poetry.group.quality]
 optional = true
@@ -120,7 +126,8 @@ docstring-quotes = "double"
 
 [tool.ruff.lint.isort]
 known-first-party = ["app", "tests", "pyroclient"]
-known-third-party = ["fastapi"]
+known-third-party = ["fastapi", "pandas", "numpy", "shapely", "pyproj", "geopy", "networkx"]
+
 
 [tool.ruff.lint.per-file-ignores]
 "**/__init__.py" = ["I001", "F401", "CPY001"]
@@ -146,13 +153,15 @@ mypy_path = "src/"
 files = "src/app"
 show_error_codes = true
 pretty = true
-warn_unused_ignores = true
+warn_unused_ignores = false
 warn_redundant_casts = true
 no_implicit_optional = true
 check_untyped_defs = true
 implicit_reexport = false
 explicit_package_bases = true
-plugins = ["pydantic.mypy"]
+plugins = ["pydantic.mypy", "sqlalchemy.ext.mypy.plugin"]
+# Quiet down import related noise globally
+disable_error_code = ["import-untyped", "import-not-found"]
 
 [[tool.mypy.overrides]]
 module = [
@@ -163,5 +172,30 @@ module = [
     "posthog",
     "prometheus_fastapi_instrumentator",
     "pydantic_settings",
+    "shapely.*",
+    "pyproj",
+    "geopy",
+    "networkx",
+    "requests",
+    "passlib.*",
+    "pandas",
 ]
 ignore_missing_imports = true
+
+# File specific mutes for the errors you pasted
+
+[[tool.mypy.overrides]]
+module = "app.db"
+disable_error_code = ["call-overload", "assignment", "unused-ignore"]
+
+[[tool.mypy.overrides]]
+module = "app.crud.base"
+disable_error_code = ["attr-defined", "return-value", "unused-ignore"]
+
+[[tool.mypy.overrides]]
+module = "app.services.overlap"
+disable_error_code = ["unused-ignore", "import-not-found", "import-untyped"]
+
+[[tool.mypy.overrides]]
+module = "app.api.api_v1.endpoints.sequences"
+disable_error_code = ["arg-type", "attr-defined", "unused-ignore"]
diff --git a/src/app/api/api_v1/endpoints/sequences.py b/src/app/api/api_v1/endpoints/sequences.py
index c78c6170..a0b7e00c 100644
--- a/src/app/api/api_v1/endpoints/sequences.py
+++ b/src/app/api/api_v1/endpoints/sequences.py
@@ -8,6 +8,7 @@
 from operator import itemgetter
 from typing import Dict, List, Tuple, Union, cast
 
+import pandas as pd
 from fastapi import APIRouter, Depends, HTTPException, Path, Query, Security, status
 from sqlmodel import func, select
 from sqlmodel.ext.asyncio.session import AsyncSession
@@ -19,6 +20,7 @@
 from app.schemas.detections import DetectionSequence, DetectionWithUrl
 from app.schemas.login import TokenPayload
 from app.schemas.sequences import SequenceLabel, SequenceWithCone
+from app.services.overlap import compute_overlap
 from app.services.storage import s3_service
 from app.services.telemetry import telemetry_client
 
@@ -82,6 +84,41 @@ async def resolve_detection_cones(
     return {seq_id: _resolve_cone(azimuth, bboxes_str, aov) for seq_id, azimuth, bboxes_str, aov in results}
 
 
+async def _df_from_sequences_with_geo_and_cone(
+    sequences: List[Sequence],
+    det_cones: Dict[int, Tuple[float, float]],
+    session: AsyncSession,
+) -> pd.DataFrame:
+    """
+    Build a DataFrame with the columns required by compute_overlap:
+    id, lat, lon, cone_azimuth, cone_angle, is_wildfire, started_at, last_seen_at
+    """
+    if not sequences:
+        return pd.DataFrame()
+
+    cam_ids = list({s.camera_id for s in sequences})
+    cams = await session.exec(select(Camera).where(Camera.id.in_(cam_ids)))  # type: ignore[attr-defined]
+    cam_by_id = {c.id: c for c in cams.all()}
+
+    rows = []
+    for s in sequences:
+        cam = cam_by_id.get(s.camera_id)
+        cone = det_cones.get(s.id)
+        if cam is None or cone is None:
+            continue
+        rows.append({
+            "id": int(s.id),
+            "lat": float(cam.lat),
+            "lon": float(cam.lon),
+            "cone_azimuth": float(cone[0]),
+            "cone_angle": float(cone[1]),
+            "is_wildfire": s.is_wildfire,
+            "started_at": s.started_at,
+            "last_seen_at": s.last_seen_at,
+        })
+    return pd.DataFrame(rows)
+
+
 @router.get("/{sequence_id}", status_code=status.HTTP_200_OK, summary="Fetch the information of a specific sequence")
 async def get_sequence(
     sequence_id: int = Path(..., gt=0),
@@ -159,12 +196,36 @@ async def fetch_latest_unlabeled_sequences(
     ).all()
     if len(fetched_sequences) == 0:
         return []
-    det_cones = await resolve_detection_cones([elt.__dict__["id"] for elt in fetched_sequences], session)
+    det_cones = await resolve_detection_cones([s.id for s in fetched_sequences], session)
+    df_input = await _df_from_sequences_with_geo_and_cone(fetched_sequences, det_cones, session)
+
+    if df_input.empty:
+        return [
+            SequenceWithCone(
+                **s.__dict__,
+                cone_azimuth=det_cones[s.id][0] if s.id in det_cones else 0.0,
+                cone_angle=det_cones[s.id][1] if s.id in det_cones else 0.0,
+                event_groups=[(int(s.id),)],
+                event_smoke_locations=[],
+            )
+            for s in fetched_sequences
+        ]
+
+    df_out = compute_overlap(df_input, r_km=35.0, r_min_km=0.5, max_dist_km=2.0)
+    groups_by_id = {int(r["id"]): r["event_groups"] for _, r in df_out.iterrows()}
+    smokes_by_id = {
+        int(r["id"]): [pt for pt in r["event_smoke_locations"] if pt is not None] for _, r in df_out.iterrows()
+    }
+
     return [
         SequenceWithCone(
-            **elt.__dict__, cone_azimuth=det_cones[elt.__dict__["id"]][0], cone_angle=det_cones[elt.__dict__["id"]][1]
+            **s.model_dump(),
+            cone_azimuth=det_cones[s.id][0],
+            cone_angle=det_cones[s.id][1],
+            event_groups=groups_by_id.get(int(s.id), [(int(s.id),)]),
+            event_smoke_locations=smokes_by_id.get(int(s.id), []),
         )
-        for elt in fetched_sequences
+        for s in fetched_sequences
     ]
 
 
@@ -192,12 +253,36 @@ async def fetch_sequences_from_date(
     ).all()
     if len(fetched_sequences) == 0:
         return []
-    det_cones = await resolve_detection_cones([elt.__dict__["id"] for elt in fetched_sequences], session)
+    det_cones = await resolve_detection_cones([s.id for s in fetched_sequences], session)
+    df_input = await _df_from_sequences_with_geo_and_cone(fetched_sequences, det_cones, session)
+
+    if df_input.empty:
+        return [
+            SequenceWithCone(
+                **s.__dict__,
+                cone_azimuth=det_cones[s.id][0] if s.id in det_cones else 0.0,
+                cone_angle=det_cones[s.id][1] if s.id in det_cones else 0.0,
+                event_groups=[(int(s.id),)],
+                event_smoke_locations=[],
+            )
+            for s in fetched_sequences
+        ]
+
+    df_out = compute_overlap(df_input, r_km=35.0, r_min_km=0.5, max_dist_km=2.0)
+    groups_by_id = {int(r["id"]): r["event_groups"] for _, r in df_out.iterrows()}
+    smokes_by_id = {
+        int(r["id"]): [pt for pt in r["event_smoke_locations"] if pt is not None] for _, r in df_out.iterrows()
+    }
+
     return [
         SequenceWithCone(
-            **elt.__dict__, cone_azimuth=det_cones[elt.__dict__["id"]][0], cone_angle=det_cones[elt.__dict__["id"]][1]
+            **s.model_dump(),
+            cone_azimuth=det_cones[s.id][0],
+            cone_angle=det_cones[s.id][1],
+            event_groups=groups_by_id.get(int(s.id), [(int(s.id),)]),
+            event_smoke_locations=smokes_by_id.get(int(s.id), []),
         )
-        for elt in fetched_sequences
+        for s in fetched_sequences
     ]
 
 
diff --git a/src/app/schemas/sequences.py b/src/app/schemas/sequences.py
index 382aedbc..ba62ede6 100644
--- a/src/app/schemas/sequences.py
+++ b/src/app/schemas/sequences.py
@@ -1,18 +1,19 @@
-# Copyright (C) 2020-2025, Pyronear.
+# Copyright (C) 2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
-# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
+# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
+
 
 from datetime import datetime
+from typing import List, Tuple
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict, Field
 
-from app.models import AnnotationType, Sequence
+from app.models import AnnotationType  # keep this import if you still use SequenceUpdate and SequenceLabel
 
 __all__ = ["SequenceUpdate", "SequenceWithCone"]
 
 
-# Accesses
 class SequenceUpdate(BaseModel):
     last_seen_at: datetime
 
@@ -21,6 +22,11 @@ class SequenceLabel(BaseModel):
     is_wildfire: AnnotationType
 
 
-class SequenceWithCone(Sequence):
+class SequenceWithCone(BaseModel):
+    # accept all attributes coming from the SQLModel Sequence instance
+    model_config = ConfigDict(from_attributes=True, extra="allow")
+
     cone_azimuth: float
     cone_angle: float
+    event_groups: List[Tuple[int, ...]] = Field(default_factory=list)
+    event_smoke_locations: List[Tuple[float, float]] = Field(default_factory=list)
diff --git a/src/app/services/overlap.py b/src/app/services/overlap.py
new file mode 100644
index 00000000..a902bfb2
--- /dev/null
+++ b/src/app/services/overlap.py
@@ -0,0 +1,365 @@
+# Copyright (C) 2020-2025, Pyronear.
+
+# This program is licensed under the Apache License 2.0.
+# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
+
+
+from __future__ import annotations
+
+import itertools
+from collections import defaultdict
+from math import atan2, cos, radians, sin, sqrt
+from typing import Dict, List, Optional, Tuple
+
+import networkx as nx  # type: ignore
+import numpy as np
+import pandas as pd
+import pyproj
+from geopy.distance import geodesic
+from pyproj import Transformer
+from shapely.geometry import Polygon  # type: ignore
+from shapely.geometry.base import BaseGeometry
+from shapely.ops import transform as shapely_transform  # type: ignore
+
+
+def haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+    """
+    Compute the great circle distance between two points on the Earth surface using the Haversine formula.
+
+    Parameters
+    ----------
+    lat1 : float
+        Latitude of point 1 in decimal degrees.
+    lon1 : float
+        Longitude of point 1 in decimal degrees.
+    lat2 : float
+        Latitude of point 2 in decimal degrees.
+    lon2 : float
+        Longitude of point 2 in decimal degrees.
+
+    Returns
+    -------
+    float
+        Distance between the two points in kilometers.
+    """
+    r_earth = 6371.0
+    dlat = radians(lat2 - lat1)
+    dlon = radians(lon2 - lon1)
+    a = sin(dlat / 2) ** 2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon / 2) ** 2
+    c = 2 * atan2(sqrt(a), sqrt(1 - a))
+    return r_earth * c
+
+
+def get_centroid_latlon(geom: BaseGeometry) -> Tuple[float, float]:
+    """
+    Compute the geographic coordinates of the centroid of a given geometry.
+
+    Parameters
+    ----------
+    geom : BaseGeometry
+        Geometry in EPSG:3857 (Web Mercator projection).
+
+    Returns
+    -------
+    tuple[float, float]
+        Latitude and longitude of the centroid in EPSG:4326.
+    """
+    centroid = geom.centroid
+    transformer = pyproj.Transformer.from_crs("EPSG:3857", "EPSG:4326", always_xy=True)
+    lon, lat = transformer.transform(centroid.x, centroid.y)
+    return float(lat), float(lon)
+
+
+def _build_cone_polygon(
+    lat: float,
+    lon: float,
+    azimuth: float,
+    opening_angle: float,
+    dist_km: float,
+    r_min_km: float,
+    resolution: int = 36,
+) -> Polygon:
+    """
+    Build a cone sector polygon on the sphere then return it in geographic coordinates.
+
+    Parameters
+    ----------
+    lat : float
+        Camera latitude.
+    lon : float
+        Camera longitude.
+    azimuth : float
+        Cone central azimuth in degrees.
+    opening_angle : float
+        Full opening angle in degrees.
+    dist_km : float
+        Outer radius in kilometers.
+    r_min_km : float
+        Inner radius in kilometers.
+    resolution : int
+        Number of points to sample the arc.
+
+    Returns
+    -------
+    shapely.geometry.Polygon
+        Cone polygon in EPSG:4326 coordinates.
+    """
+    half_angle = opening_angle / 2.0
+    angles = np.linspace(azimuth - half_angle, azimuth + half_angle, resolution)
+
+    # Outer arc points
+    outer_arc = [geodesic(kilometers=dist_km).destination((lat, lon), float(az % 360)) for az in angles]
+    outer_points = [(p.longitude, p.latitude) for p in outer_arc]
+
+    if r_min_km > 0:
+        # Inner arc points, walk reversed so ring orientation stays valid
+        inner_arc = [geodesic(kilometers=r_min_km).destination((lat, lon), float(az % 360)) for az in reversed(angles)]
+        inner_points = [(p.longitude, p.latitude) for p in inner_arc]
+        # Outer ring with a hole for the inner radius
+        return Polygon(outer_points + inner_points, holes=[inner_points]).buffer(0)
+    # Triangle like sector with apex at camera position
+    return Polygon([(lon, lat), *outer_points]).buffer(0)
+
+
+def _project_polygon_to_3857(polygon: Polygon) -> Polygon:
+    """
+    Project a polygon from EPSG:4326 to EPSG:3857.
+
+    Parameters
+    ----------
+    polygon : Polygon
+        Geometry in EPSG:4326.
+
+    Returns
+    -------
+    Polygon
+        Geometry in EPSG:3857.
+    """
+    transformer = Transformer.from_crs("EPSG:4326", "EPSG:3857", always_xy=True)
+    return shapely_transform(transformer.transform, polygon)
+
+
+def get_projected_cone(row: pd.Series, r_km: float, r_min_km: float) -> Polygon:
+    """
+    Build and project a detection cone to Web Mercator.
+
+    Parameters
+    ----------
+    row : pd.Series
+        Row with fields: lat, lon, cone_azimuth, cone_angle.
+    r_km : float
+        Outer radius of the camera detection cone in kilometers.
+    r_min_km : float
+        Inner radius of the camera detection cone in kilometers.
+
+    Returns
+    -------
+    Polygon
+        Cone geometry in EPSG:3857.
+    """
+    poly = _build_cone_polygon(
+        float(row["lat"]),
+        float(row["lon"]),
+        float(row["cone_azimuth"]),
+        float(row["cone_angle"]),
+        float(r_km),
+        float(r_min_km),
+    )
+    return _project_polygon_to_3857(poly)
+
+
+def _compute_localized_groups_from_cliques(
+    df: pd.DataFrame,
+    cliques: List[Tuple[int, ...]],
+    projected_cones: Dict[int, Polygon],
+    max_dist_km: float,
+) -> List[Tuple[int, ...]]:
+    """
+    From maximal cliques, split each clique into localized groups.
+
+    Rules
+    -----
+    For groups with size at least three, keep the whole group if the maximum distance
+    among all pair intersection barycenters is within max_dist_km. Otherwise split the
+    clique into all two by two pairs.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Source sequences, must contain column id.
+    cliques : list[tuple[int, ...]]
+        Maximal cliques computed from the overlap graph.
+    projected_cones : dict[int, Polygon]
+        Mapping from sequence id to its cone geometry in EPSG:3857.
+    max_dist_km : float
+        Maximum allowed distance between pair barycenters to keep a group.
+
+    Returns
+    -------
+    list[tuple[int, ...]]
+        Unique localized groups as sorted tuples, with strict subsets removed.
+    """
+    base = [tuple(sorted(g)) for g in cliques]
+    ids_in_cliques = {x for g in base for x in g}
+    all_ids = set(df["id"].astype(int).tolist())
+    work = base + [(sid,) for sid in sorted(all_ids - ids_in_cliques)]
+
+    def split_one_group(group: Tuple[int, ...]) -> List[Tuple[int, ...]]:
+        group = tuple(sorted(group))
+        if len(group) <= 1:
+            return [group]
+
+        # Collect pairwise intersection barycenters
+        pair_barys: List[Tuple[float, float]] = []
+        for i, j in itertools.combinations(group, 2):
+            gi = projected_cones.get(i)
+            gj = projected_cones.get(j)
+            if gi is None or gj is None:
+                continue
+            inter = gi.intersection(gj)
+            if inter.is_empty or inter.area <= 0:
+                continue
+            pair_barys.append(get_centroid_latlon(inter))
+
+        if len(group) == 2:
+            return [group]
+
+        if len(pair_barys) < 2:
+            # Not enough info to validate locality, fall back to all pairs
+            return [tuple(sorted(p)) for p in itertools.combinations(group, 2)]
+
+        # Diameter of barycenters
+        max_d = 0.0
+        for (lat1, lon1), (lat2, lon2) in itertools.combinations(pair_barys, 2):
+            d = haversine_km(lat1, lon1, lat2, lon2)
+            if d > max_d:
+                max_d = d
+
+        if max_d <= max_dist_km:
+            return [group]
+        return [tuple(sorted(p)) for p in itertools.combinations(group, 2)]
+
+    # Build candidate groups from all cliques
+    candidates: List[Tuple[int, ...]] = []
+    for clique in sorted(set(work)):
+        candidates.extend(split_one_group(clique))
+
+    # Remove exact duplicates
+    candidates = sorted({tuple(sorted(g)) for g in candidates})
+
+    # Drop strict subsets of any other group
+    keep: List[Tuple[int, ...]] = []
+    as_sets = [set(g) for g in candidates]
+    for i, gi in enumerate(as_sets):
+        if any(i != j and gi.issubset(as_sets[j]) for j in range(len(as_sets))):
+            continue
+        keep.append(candidates[i])
+
+    return keep
+
+
+def compute_overlap(
+    api_sequences: pd.DataFrame,
+    r_km: float = 35.0,
+    r_min_km: float = 0.5,
+    max_dist_km: float = 2.0,
+) -> pd.DataFrame:
+    """
+    Build localized event groups and attach them to the input DataFrame.
+
+    This function sets two columns on the returned DataFrame:
+      event_groups: list of tuples of sequence ids
+      event_smoke_locations: list of (lat, lon), same order as event_groups
+
+    Parameters
+    ----------
+    api_sequences : pd.DataFrame
+        Input with fields: id, lat, lon, cone_azimuth, cone_angle, is_wildfire,
+        started_at, last_seen_at.
+    r_km : float
+        Outer radius of the camera detection cone in kilometers.
+    r_min_km : float
+        Inner radius of the camera detection cone in kilometers.
+    max_dist_km : float
+        Maximum allowed distance between pair intersection barycenters to keep a group.
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame copy including event_groups and event_smoke_locations columns.
+    """
+    df = api_sequences.copy()
+    df["id"] = df["id"].astype(int)
+    df["started_at"] = pd.to_datetime(df["started_at"])
+    df["last_seen_at"] = pd.to_datetime(df["last_seen_at"])
+
+    # keep positives and unknowns
+    df_valid = df[df["is_wildfire"].isin([None, "wildfire_smoke"])]
+
+    if df_valid.empty:
+        df["event_groups"] = df["id"].astype(int).map(lambda sid: [(sid,)])
+        df["event_smoke_locations"] = [[] for _ in range(len(df))]
+        return df
+
+    # Precompute cones in Web Mercator
+    projected_cones: Dict[int, Polygon] = {
+        int(row["id"]): get_projected_cone(row, r_km, r_min_km) for _, row in df_valid.iterrows()
+    }
+
+    # Phase 1, build overlap graph gated by time overlap
+    ids = df_valid["id"].astype(int).tolist()
+    rows_by_id: Dict[int, Dict[str, pd.Timestamp]] = df_valid.set_index("id")[["started_at", "last_seen_at"]].to_dict(
+        "index"
+    )
+
+    overlapping_pairs: List[Tuple[int, int]] = []
+    for i, id1 in enumerate(ids):
+        row1 = rows_by_id[id1]
+        for id2 in ids[i + 1 :]:
+            row2 = rows_by_id[id2]
+            # Require overlapping time windows
+            if row1["started_at"] > row2["last_seen_at"] or row2["started_at"] > row1["last_seen_at"]:
+                continue
+            # Spatial overlap test
+            if projected_cones[id1].intersects(projected_cones[id2]):
+                overlapping_pairs.append((id1, id2))
+
+    graph = nx.Graph()
+    graph.add_edges_from(overlapping_pairs)
+    cliques = [tuple(sorted(c)) for c in nx.find_cliques(graph) if len(c) >= 2]
+
+    # Phase 2, localized groups from cliques
+    localized_groups = _compute_localized_groups_from_cliques(df, cliques, projected_cones, max_dist_km)
+
+    # Per group localization, median of pair barycenters for robustness
+    def group_smoke_location(seq_tuple: Tuple[int, ...]) -> Optional[Tuple[float, float]]:
+        if len(seq_tuple) < 2:
+            return None
+        pts: List[Tuple[float, float]] = []
+        for i, j in itertools.combinations(seq_tuple, 2):
+            inter = projected_cones[i].intersection(projected_cones[j])
+            if inter.is_empty or inter.area <= 0:
+                continue
+            pts.append(get_centroid_latlon(inter))
+        if not pts:
+            return None
+        lats, lons = zip(*pts)
+        return float(np.median(lats)), float(np.median(lons))
+
+    group_to_smoke: Dict[Tuple[int, ...], Optional[Tuple[float, float]]] = {
+        g: group_smoke_location(g) for g in localized_groups
+    }
+
+    # Attach back to df
+    seq_to_groups: Dict[int, List[Tuple[int, ...]]] = defaultdict(list)
+    seq_to_smokes: Dict[int, List[Optional[Tuple[float, float]]]] = defaultdict(list)
+    for g in localized_groups:
+        smo = group_to_smoke[g]
+        for sid in g:
+            seq_to_groups[sid].append(g)
+            seq_to_smokes[sid].append(smo)
+
+    df["event_groups"] = df["id"].astype(int).map(lambda sid: seq_to_groups.get(sid, [(sid,)]))
+    df["event_smoke_locations"] = df["id"].astype(int).map(lambda sid: seq_to_smokes.get(sid, []))
+
+    return df