From 7249c8058109d570d45a718b0fa3a513b8cddcd4 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Thu, 11 Sep 2025 08:23:44 -0400 Subject: [PATCH 01/37] Add viz dependency group --- libs/langchain-mongodb/pyproject.toml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index 40d0ae0e..60f85757 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -44,6 +44,15 @@ dev = [ "typing-extensions>=4.12.2", ] +viz = [ + "networkx", + "pandas", + "numpy", + "holoviews", + "jupyter", + "scikit-image", +] + [tool.pytest.ini_options] minversion = "7" addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" From 93993bdb8a0c96475c1255852f9ddcb25a227acd Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Thu, 25 Sep 2025 10:36:17 -0400 Subject: [PATCH 02/37] Added viz to project.optional-dependencies --- libs/langchain-mongodb/pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index 60f85757..d4a26abe 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -44,6 +44,7 @@ dev = [ "typing-extensions>=4.12.2", ] +[project.optional-dependencies] viz = [ "networkx", "pandas", @@ -51,6 +52,7 @@ viz = [ "holoviews", "jupyter", "scikit-image", + "pyparsing", ] [tool.pytest.ini_options] From 8060667e4a578b71baac9a75aabee3aadfbef318 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 29 Sep 2025 12:59:40 -0400 Subject: [PATCH 03/37] Split out networkx. Cleaned up API. Testing via notebook not yet checked in. --- .../langchain_mongodb/graphrag/__init__.py | 3 + .../graphrag/delete_green.py | 49 + .../langchain_mongodb/graphrag/graph.py | 146 +- libs/langchain-mongodb/pyproject.toml | 14 +- libs/langchain-mongodb/uv.lock | 5867 ++++++++++++----- uv.lock | 4 + 6 files changed, 4459 insertions(+), 1624 deletions(-) create mode 100644 libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py index e69de29b..f392f064 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/__init__.py @@ -0,0 +1,3 @@ +from langchain_mongodb.graphrag.graph import MongoDBGraphStore + +__all__ = ["MongoDBGraphStore"] diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py new file mode 100644 index 00000000..2dff3860 --- /dev/null +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py @@ -0,0 +1,49 @@ +""" +# Greener Build + + +Hi team, IHAC who is using `count_documents({ext_type: 4, ext_lastmod: { $gt:1758102623, $lte:1758124036 }})` method +and internally it's converting it into +`[{ $match: { ext_type:4, ext_lastmod: { $gt:1758102623, $lte:1758124036 } } }, { $group: { _id:1, n: { $sum:1 } } }]` +and query results looks like: `keysExamined:3720495 docsExamined:3720491`. +I try the same query in mongosh and it looks like: `keysExamined:3720495 docsExamined:0`, +am I missing something here? The customer concern is like it looks like a `COLLSCAN` due to the number of docs examined. Any ideas? + + +""" + +import os +from turtledemo.chaos import coosys + +from langchain_mongodb.graphrag.graph import MongoDBGraphStore + +CONNECTION_STRING = os.environ.get("MONGODB_URI", "") # Use Prakul's Cluster 0 +DB_NAME = "langchain_test_db" +COLLECTION_NAME = "langchain_test_graphrag" +CONNECTION_STRING = "mongodb+srv://prakul_test:x8Qyc2CyUGxcWgpc@cluster0.8by45wo.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" + +''' +graph_store = MongoDBGraphStore( + connection_string=CONNECTION_STRING, + database_name=DB_NAME, + collection_name=COLLECTION_NAME, + entity_extraction_model=None, +) +coll = graph_store.collection +''' + + + + +filter1 = {"_id": {"$in": ['ACME Corporation', 'GreenTech Ltd.']}} +filter = {"$match": filter1} + +n = coll.count_documents(filter1) + +filter_gt = {"relationships.attributes.since[0]": {"$gt": "2000"}} + +coll.count_documents(filter_gt) + + +# TODO - Need something that includes $gt, $lte + diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py index 52417444..bb1b6353 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py @@ -3,7 +3,9 @@ import json import logging from copy import deepcopy -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union + +from importlib.metadata import version +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union from langchain_core.documents import Document from langchain_core.language_models.chat_models import BaseChatModel @@ -22,13 +24,16 @@ if TYPE_CHECKING: try: - from typing import TypeAlias # type:ignore[attr-defined] # Python 3.10+ + from typing import TypeAlias # type:ignore[attr-defined] # Python 3.10+ except ImportError: from typing_extensions import TypeAlias # Python 3.9 fallback Entity: TypeAlias = Dict[str, Any] """Represents an Entity in the knowledge graph with specific schema. See .schema""" + import holoviews # type: ignore[import-untyped] + import networkx + logger = logging.getLogger(__name__) @@ -546,3 +551,140 @@ def chat_response( entity_schema=entity_schema, ) ) + + def to_networkx(self, **kwargs: Any) -> networkx.DiGraph: + """Utility converts Entity Collection to `NetworkX DiGraph `_ + + NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + + Args: + **kwargs: Keywork arguments passed to json formatting, and constructors of graph, nodes, and edges. + + Returns: networkx.DiGraph + """ + + try: + import json + + import networkx as nx + except ImportError as e: + raise ImportError( + "Install optional-dependency `viz` for networkx or to view in Holoviews" + ) from e + + def _safe_get(lst: list, i: int, default: Any = "") -> Any: + return lst[i] if i < len(lst) else default + + # First pass: Add all nodes with their attributes + nx_graph = nx.DiGraph(**kwargs) + for doc in self.collection.find({}): + # Add node with all attributes + node_id = doc["_id"] + node_attrs = {} + node_attrs["type"] = json.dumps(doc.get("type", ""), **kwargs) + node_attrs["attributes"] = json.dumps(doc.get("attributes", {}), **kwargs) + nx_graph.add_node(node_id, **node_attrs, **kwargs) + + # Second pass: Add edges based on relationships + for doc in self.collection.find({}): + source_id = doc["_id"] + relationships = doc.get("relationships", {}) + # relationships can contain numerous target_ids, each with type and attributes + target_ids = relationships.get("target_ids", []) + n_targets = len(target_ids) + types = relationships.get("types", []) + attrs = relationships.get("attributes", []) + + for t in range(n_targets): + # Add edge and attributes + edge_attrs = {} + edge_attrs["type"] = json.dumps(_safe_get(types, t), **kwargs) + edge_attrs["attributes"] = json.dumps(_safe_get(attrs, t), **kwargs) + if nx_graph.has_node(target_ids[t]): + nx_graph.add_edge(source_id, target_ids[t], **edge_attrs, **kwargs) + else: + logger.warning( + f"{source_id=} references {target_ids[t]=} not found in collection" + ) + + return nx_graph + + def view( + self, + layout: Optional[Callable] = None, + nx_opts: Optional[dict] = None, + edge_opts: Optional[dict] = None, + node_opts: Optional[dict] = None, + ) -> holoviews.Graph: + """Draws a Knowledge Graph as Holoviews/Bokeh interactive plot. + + We first convert the entity collection to a NetworkX Graph, + and then convert it to a Holoviews Graph via their API. + + Both of these libraries are incredibly feature rich. + We encourage those interested in visualization and/or graph analysis + to dig deeper into their documentation. + The customization options are truly stunning. + + The default layout chosen is the spring_layout. + This maximizes the distance between nodes. As our entities have a type field, + however, another good layout choice is: + `layout=nx.multipartite_layout, nx_opts["subset_key"]= "type"` + + NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + + Args: + layout: `networkx layout. `_ + Defaults to networkx.spring_layout. + nx_opts: Keyword arguments for layout function. + edge_opts: Keyword arguments to draw edges. + node_opts: Keyword arguments to draw nodes. + + Returns: `holoviews.Graph `_ + + """ + try: + import holoviews as hv + import networkx as nx + + hv.extension("bokeh") + except ImportError as e: + raise ImportError("To view graph, install optional-dependency `viz`") from e + + hv.opts.defaults( + hv.opts.Graph(xaxis=None, yaxis=None), hv.opts.Nodes(xaxis=None, yaxis=None) + ) + + if layout is None: + layout = nx.spring_layout + # Convert entity collection to NetworkX graph. + nx_opts = {} if nx_opts is None else nx_opts + nx_graph = self.to_networkx(**nx_opts) + # Convert to HoloViews Graph + hv_graph = hv.Graph.from_networkx(nx_graph, layout, **nx_opts) + # Display with hover tools over edges and nodes + edge_opts = {} if edge_opts is None else edge_opts + node_opts = {} if node_opts is None else node_opts + return hv_graph.opts( + inspection_policy="edges", **edge_opts + ) * hv_graph.nodes.opts(**node_opts) + + +# TODO +# Discuss API +# Document +# Choose reasonable defaults +# - Currently I have sidestepped them leaving standard defaults. +# - dpi, width, height +# - where do we specify these? hv.opts? +# - Documents layouts. => +# - spring_layout, (100-1000 nodes) +# - nx.kamada_kawai_layout, (< 100 nodes) +# - # These others require: brew install graphviz. pip install pydot graphviz +# layout=nx.nx_pydot.graphviz_layout, nx_opts={"prog":"sfdp"}) (1000+ nodes) , +# others: prog="dot" for Hierarchical Knowledge Graphs, prog=". +# ** layout=nx.multipartite_layout, subset_key = "type", # If you have different node types (e.g., Person, Organization, Location) +# lambda x: nx.nx_pydot.graphviz_layout(x, prog="twopi") +# Export collection to play with in notebook ==> view_graphstore.ipynb +# Create a much larger graph to test defaults. => Use Claude Code to build tests. +# Test save. 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-904,13 +904,17 @@ dependencies = [ [package.metadata] requires-dist = [ + { name = "holoviews", marker = "extra == 'viz'" }, + { name = "jupyter", marker = "extra == 'viz'" }, { name = "langchain", specifier = ">=0.3" }, { name = "langchain-core", specifier = ">=0.3" }, { name = "langchain-text-splitters", specifier = ">=0.3" }, { name = "lark", specifier = ">=1.1.9,<2.0.0" }, + { name = "networkx", marker = "extra == 'viz'" }, { name = "numpy", specifier = ">=1.26" }, { name = "pymongo", specifier = ">=4.6.1" }, ] +provides-extras = ["viz"] [package.metadata.requires-dev] dev = [ From 2d866b9b839f6daeef1cf9c31faa1b96601a6845 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Fri, 3 Oct 2025 10:11:27 -0400 Subject: [PATCH 04/37] Adds API TODO --- libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py index bb1b6353..ba795cae 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py @@ -671,7 +671,7 @@ def view( # TODO -# Discuss API +# Discuss API => split out json_opts and nx_opts # Document # Choose reasonable defaults # - Currently I have sidestepped them leaving standard defaults. From 4e4da4416afbd3b1788fb91bd18c77d5d001e548 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 15:25:00 -0400 Subject: [PATCH 05/37] Add .ipynb_checkpoints to .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index c281a23b..63859da6 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ __pycache__ .env .venv* .local_atlas_uri +.ipynb_checkpoints docs/langchain_mongodb docs/langgraph_checkpoint_mongodb docs/index.md From e31023df8fc8e0da04a448fc5ad4aae7f9f6b824 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 15:29:20 -0400 Subject: [PATCH 06/37] Added basic GraphRAG notebook --- .../langchain-mongodb/examples/GraphRAG.ipynb | 1791 +++++++++++++++++ libs/langchain-mongodb/pyproject.toml | 4 +- libs/langchain-mongodb/uv.lock | 11 + 3 files changed, 1804 insertions(+), 2 deletions(-) create mode 100644 libs/langchain-mongodb/examples/GraphRAG.ipynb diff --git a/libs/langchain-mongodb/examples/GraphRAG.ipynb b/libs/langchain-mongodb/examples/GraphRAG.ipynb new file mode 100644 index 00000000..c0eb4b4b --- /dev/null +++ b/libs/langchain-mongodb/examples/GraphRAG.ipynb @@ -0,0 +1,1791 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "62c5b493-9b7d-45dc-be3b-9ea1c77ad7d8", + "metadata": {}, + "source": [ + "# Knowledge Graphs via MongoDBGraphStore\n", + "\n", + "GraphRAG is augments a ChatModel with information retrieved\n", + "based on a Knowledge Graph that an LLM is used to create.\n", + "\n", + "Our Knowledge Graph `MongoDBGraphStore` is based on a collection of entities, each holding attributes and relationships to other entities\n", + "\n", + "* One can use `similarity_search`, and `related_entities`, to use the MongoDB Collection as a Graph Database via `$graphLookup`.\n", + "* `chat_response` provides a simple interface to ask questions of an LLM informed with results of `$graphLookup`\n", + "* `view`: This notebook highlights the `view` method, which adds the ability to visualize the Knowledge Graph using [Holoview's Network Graph](https://holoviews.org/user_guide/Network_Graphs.html) built upon the using dynamic plotting library [bokeh](https://bokeh.org/).\n", + "* `to_networkx`: We also point out that we use [NetworkX](https://networkx.org/) under the hood. This representation may be very useful for Graph Analytics, and as a starting point for one's own preferred visualization library.\n", + "\n", + "Note that both HoloViews and NetworkX are extensible. We encourage you to play with further customization. Some extensions wil require additional packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3f3fb616-c1c1-4f82-b093-ad3f37a728e6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import holoviews as hv\n", + "import networkx as nx\n", + "from holoviews import opts\n", + "\n", + "from langchain_mongodb.graphrag.graph import MongoDBGraphStore" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5474a3a4-fcd6-4acb-b01e-2f3afe7ff549", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this 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trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " 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BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function 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document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if 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bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || 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JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " 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(window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " let retries = 0;\n", + " const open = () => {\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else if (retries > 3) {\n", + " console.warn('Comm target never activated')\n", + " } else {\n", + " retries += 1\n", + " setTimeout(open, 500)\n", + " }\n", + " }\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else {\n", + " setTimeout(open, 500)\n", + " }\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " })\n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof 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\n", + "
\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Graph.I :Graph [start,end] (attributes,type)\n", + " .Nodes.I :Nodes [x,y,index] (attributes,type)" + ] + }, + "execution_count": 12, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "09ba8557-5e8f-4f54-83cd-de39afea6593" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Multipartite (grouping by type)\n", + "by_type = hv.Graph.from_networkx(nx_graph, nx.multipartite_layout, subset_key=\"type\")\n", + "by_type.opts(inspection_policy=\"edges\") * by_type.nodes" + ] + } + ], + "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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index d41a5f26..6f4c73a4 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -48,11 +48,11 @@ dev = [ viz = [ "networkx", "holoviews", - "jupyter", # TODO All of the follow provide further functionality and layouts. + "jupyter", + "pyparsing", # TODO All of the follow provide further functionality and layouts. # "pandas", # "numpy", # "scikit-image", -# "pyparsing", # "datashader", # TODO - Review inclusion of datashader, used for grouping nodes in very large graphs # "dask", # "graphviz", TODO - Review inclusion of graphviz, used for additional plotting layouts diff --git a/libs/langchain-mongodb/uv.lock b/libs/langchain-mongodb/uv.lock index 79b79708..541999f2 100644 --- a/libs/langchain-mongodb/uv.lock +++ b/libs/langchain-mongodb/uv.lock @@ -2017,6 +2017,7 @@ viz = [ { name = "networkx", version = "3.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" }, { name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, { name = "networkx", version = "3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pyparsing" }, ] [package.dev-dependencies] @@ -2056,6 +2057,7 @@ requires-dist = [ { name = "networkx", marker = "extra == 'viz'" }, { name = "numpy", specifier = ">=1.26" }, { name = "pymongo", specifier = ">=4.6.1" }, + { name = "pyparsing", marker = "extra == 'viz'" }, ] provides-extras = ["viz"] @@ -4046,6 +4048,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/20/90/e16ab0396ca3ce061fdf297bfa4d0f9e8befffa7bc50502b1a8e72267add/pymongo-4.15.2-cp39-cp39-win_arm64.whl", hash = "sha256:5b72de1a2f8cc52561c65a5062ade72e06da76e0899d4f11ae1c1bced1534ff8", size = 753468, upload-time = "2025-10-01T21:25:47.689Z" }, ] +[[package]] +name = "pyparsing" +version = "3.2.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f2/a5/181488fc2b9d093e3972d2a472855aae8a03f000592dbfce716a512b3359/pyparsing-3.2.5.tar.gz", hash = "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6", size = 1099274, upload-time = "2025-09-21T04:11:06.277Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/10/5e/1aa9a93198c6b64513c9d7752de7422c06402de6600a8767da1524f9570b/pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e", size = 113890, upload-time = "2025-09-21T04:11:04.117Z" }, +] + [[package]] name = "pypdf" version = "6.1.1" From fbdaf4db1d535d47403f8b502cdb6b18fd25c4af Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 15:50:55 -0400 Subject: [PATCH 07/37] Removed viz packages only needed for advanced graph layouts --- libs/langchain-mongodb/pyproject.toml | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index 6f4c73a4..a8a63a3a 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -49,14 +49,7 @@ viz = [ "networkx", "holoviews", "jupyter", - "pyparsing", # TODO All of the follow provide further functionality and layouts. -# "pandas", -# "numpy", -# "scikit-image", -# "datashader", # TODO - Review inclusion of datashader, used for grouping nodes in very large graphs -# "dask", -# "graphviz", TODO - Review inclusion of graphviz, used for additional plotting layouts -# "pydot", + "pyparsing", ] [tool.pytest.ini_options] From 7214bb356cce75e0c0525b988134ef28d2700104 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 17:09:45 -0400 Subject: [PATCH 08/37] Removes script added by mistake --- .../graphrag/delete_green.py | 49 ------------------- 1 file changed, 49 deletions(-) delete mode 100644 libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py deleted file mode 100644 index 2dff3860..00000000 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/delete_green.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -# Greener Build - - -Hi team, IHAC who is using `count_documents({ext_type: 4, ext_lastmod: { $gt:1758102623, $lte:1758124036 }})` method -and internally it's converting it into -`[{ $match: { ext_type:4, ext_lastmod: { $gt:1758102623, $lte:1758124036 } } }, { $group: { _id:1, n: { $sum:1 } } }]` -and query results looks like: `keysExamined:3720495 docsExamined:3720491`. -I try the same query in mongosh and it looks like: `keysExamined:3720495 docsExamined:0`, -am I missing something here? The customer concern is like it looks like a `COLLSCAN` due to the number of docs examined. Any ideas? - - -""" - -import os -from turtledemo.chaos import coosys - -from langchain_mongodb.graphrag.graph import MongoDBGraphStore - -CONNECTION_STRING = os.environ.get("MONGODB_URI", "") # Use Prakul's Cluster 0 -DB_NAME = "langchain_test_db" -COLLECTION_NAME = "langchain_test_graphrag" -CONNECTION_STRING = "mongodb+srv://prakul_test:x8Qyc2CyUGxcWgpc@cluster0.8by45wo.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" - -''' -graph_store = MongoDBGraphStore( - connection_string=CONNECTION_STRING, - database_name=DB_NAME, - collection_name=COLLECTION_NAME, - entity_extraction_model=None, -) -coll = graph_store.collection -''' - - - - -filter1 = {"_id": {"$in": ['ACME Corporation', 'GreenTech Ltd.']}} -filter = {"$match": filter1} - -n = coll.count_documents(filter1) - -filter_gt = {"relationships.attributes.since[0]": {"$gt": "2000"}} - -coll.count_documents(filter_gt) - - -# TODO - Need something that includes $gt, $lte - From df53045ec9e5dd1173a2d7cc42d11b8ba80a7dbf Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 17:11:18 -0400 Subject: [PATCH 09/37] Finishes to_network, and view API (opts kwargs) and adds documentation. --- .../langchain_mongodb/graphrag/graph.py | 66 +++++++++---------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py index ba795cae..3802c58e 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py @@ -3,8 +3,6 @@ import json import logging from copy import deepcopy - -from importlib.metadata import version from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union from langchain_core.documents import Document @@ -552,13 +550,20 @@ def chat_response( ) ) - def to_networkx(self, **kwargs: Any) -> networkx.DiGraph: + def to_networkx( + self, + nx_opts: Optional[dict] = None, + json_opts: Optional[dict] = None, + **kwargs: Any, + ) -> networkx.DiGraph: """Utility converts Entity Collection to `NetworkX DiGraph `_ NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` Args: - **kwargs: Keywork arguments passed to json formatting, and constructors of graph, nodes, and edges. + nx_opts: Keyword arguments for networkx calls. + json_opts: Keyword arguments for printing of node attributes and types. + **kwargs: Keywork arguments available for compatibility. Returns: networkx.DiGraph """ @@ -575,15 +580,20 @@ def to_networkx(self, **kwargs: Any) -> networkx.DiGraph: def _safe_get(lst: list, i: int, default: Any = "") -> Any: return lst[i] if i < len(lst) else default + nx_opts = {} if nx_opts is None else nx_opts + json_opts = {} if json_opts is None else json_opts + # First pass: Add all nodes with their attributes - nx_graph = nx.DiGraph(**kwargs) + nx_graph = nx.DiGraph(**nx_opts) for doc in self.collection.find({}): # Add node with all attributes node_id = doc["_id"] node_attrs = {} - node_attrs["type"] = json.dumps(doc.get("type", ""), **kwargs) - node_attrs["attributes"] = json.dumps(doc.get("attributes", {}), **kwargs) - nx_graph.add_node(node_id, **node_attrs, **kwargs) + node_attrs["type"] = json.dumps(doc.get("type", ""), **json_opts) + node_attrs["attributes"] = json.dumps( + doc.get("attributes", {}), **json_opts + ) + nx_graph.add_node(node_id, **node_attrs, **json_opts) # Second pass: Add edges based on relationships for doc in self.collection.find({}): @@ -598,10 +608,10 @@ def _safe_get(lst: list, i: int, default: Any = "") -> Any: for t in range(n_targets): # Add edge and attributes edge_attrs = {} - edge_attrs["type"] = json.dumps(_safe_get(types, t), **kwargs) - edge_attrs["attributes"] = json.dumps(_safe_get(attrs, t), **kwargs) + edge_attrs["type"] = json.dumps(_safe_get(types, t), **json_opts) + edge_attrs["attributes"] = json.dumps(_safe_get(attrs, t), **json_opts) if nx_graph.has_node(target_ids[t]): - nx_graph.add_edge(source_id, target_ids[t], **edge_attrs, **kwargs) + nx_graph.add_edge(source_id, target_ids[t], **edge_attrs, **nx_opts) else: logger.warning( f"{source_id=} references {target_ids[t]=} not found in collection" @@ -613,8 +623,10 @@ def view( self, layout: Optional[Callable] = None, nx_opts: Optional[dict] = None, + json_opts: Optional[dict] = None, edge_opts: Optional[dict] = None, node_opts: Optional[dict] = None, + **kwargs: Any, ) -> holoviews.Graph: """Draws a Knowledge Graph as Holoviews/Bokeh interactive plot. @@ -633,12 +645,19 @@ def view( NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + You can save the view as any HoloViews object with `.save`. + The type will be inferred from the filename's suffix, + (e.g., hv.save(graph, "graph.html")) or by clicking the download widget + on the Bokeh plot from a Jupyter notebook. + Args: layout: `networkx layout. `_ Defaults to networkx.spring_layout. - nx_opts: Keyword arguments for layout function. + nx_opts: Keyword arguments for to_networkx function. + json_opts: Keyword arguments for printing of node attributes and types. edge_opts: Keyword arguments to draw edges. node_opts: Keyword arguments to draw nodes. + **kwargs: Keywork arguments available for compatibility. Returns: `holoviews.Graph `_ @@ -659,7 +678,8 @@ def view( layout = nx.spring_layout # Convert entity collection to NetworkX graph. nx_opts = {} if nx_opts is None else nx_opts - nx_graph = self.to_networkx(**nx_opts) + json_opts = {} if json_opts is None else json_opts + nx_graph = self.to_networkx(**nx_opts, **json_opts) # Convert to HoloViews Graph hv_graph = hv.Graph.from_networkx(nx_graph, layout, **nx_opts) # Display with hover tools over edges and nodes @@ -668,23 +688,3 @@ def view( return hv_graph.opts( inspection_policy="edges", **edge_opts ) * hv_graph.nodes.opts(**node_opts) - - -# TODO -# Discuss API => split out json_opts and nx_opts -# Document -# Choose reasonable defaults -# - Currently I have sidestepped them leaving standard defaults. -# - dpi, width, height -# - where do we specify these? hv.opts? -# - Documents layouts. => -# - spring_layout, (100-1000 nodes) -# - nx.kamada_kawai_layout, (< 100 nodes) -# - # These others require: brew install graphviz. pip install pydot graphviz -# layout=nx.nx_pydot.graphviz_layout, nx_opts={"prog":"sfdp"}) (1000+ nodes) , -# others: prog="dot" for Hierarchical Knowledge Graphs, prog=". -# ** layout=nx.multipartite_layout, subset_key = "type", # If you have different node types (e.g., Person, Organization, Location) -# lambda x: nx.nx_pydot.graphviz_layout(x, prog="twopi") -# Export collection to play with in notebook ==> view_graphstore.ipynb -# Create a much larger graph to test defaults. => Use Claude Code to build tests. -# Test save. (Then Document it.) From 41ea33b0839e48e80c93826f60db009dae8f817c Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 17:12:41 -0400 Subject: [PATCH 10/37] Adds small working GraphStore visualization notebook. Requires existing collection. --- .../langchain-mongodb/examples/GraphRAG.ipynb | 77 +++++++++++-------- 1 file changed, 44 insertions(+), 33 deletions(-) diff --git a/libs/langchain-mongodb/examples/GraphRAG.ipynb b/libs/langchain-mongodb/examples/GraphRAG.ipynb index c0eb4b4b..3aace235 100644 --- a/libs/langchain-mongodb/examples/GraphRAG.ipynb +++ b/libs/langchain-mongodb/examples/GraphRAG.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "5474a3a4-fcd6-4acb-b01e-2f3afe7ff549", "metadata": {}, "outputs": [ @@ -235,7 +235,7 @@ " document.body.appendChild(element);\n", " }\n", "\n", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.8.1/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.0.min.js\", \"https://cdn.holoviz.org/panel/1.8.1/dist/panel.min.js\"];\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.8.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.0.min.js\", \"https://cdn.holoviz.org/panel/1.8.2/dist/panel.min.js\"];\n", " const js_modules = [];\n", " const js_exports = {};\n", " const css_urls = [];\n", @@ -316,7 +316,7 @@ " setTimeout(load_or_wait, 100)\n", "}(window));" ], - "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; 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i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.0.min.js\", \"https://cdn.holoviz.org/panel/1.8.2/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -587,12 +587,12 @@ "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ - "
\n", - "
\n", + "
\n", + "
\n", "
\n", "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.8.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.0.min.js\", \"https://cdn.holoviz.org/panel/1.8.2/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == 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console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = 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&& Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || 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messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " let retries = 0;\n", + " const open = () => {\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else if (retries > 3) {\n", + " console.warn('Comm target never activated')\n", + " } else {\n", + " retries += 1\n", + " setTimeout(open, 500)\n", + " }\n", + " }\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else {\n", + " setTimeout(open, 500)\n", + " }\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " })\n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== 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\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ⓘ\n", + "
\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hv.extension(\"bokeh\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "52fc6635-87e9-4179-b6a4-404c988904c5", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up default plot dimensions for better viewing of large graphs\n", + "defaults = dict(width=1200, height=800)\n", + "hv.opts.defaults(\n", + " opts.EdgePaths(**defaults), opts.Graph(**defaults), opts.Nodes(**defaults)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "config-section", + "metadata": {}, + "source": [ + "## Configuration\n", + "\n", + "Set up MongoDB connection and LLM for entity extraction." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "69026170-5d6d-43a1-9fbb-9a52461e1e0b", + "metadata": {}, + "outputs": [], + "source": [ + "CONNECTION_STRING = os.environ.get(\"MONGODB_URI\", \"\")\n", + "DB_NAME = \"langchain_test_db\"\n", + "COLLECTION_NAME = \"langchain_graphrag_large_example\"\n", + "\n", + "# Configure the LLM for entity extraction\n", + "# Using gpt-4o for high-quality entity extraction\n", + "entity_extraction_model = ChatOpenAI(\n", + " model=\"gpt-4o\", temperature=0.0, cache=False, seed=12345\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "data-loading-section", + "metadata": {}, + "source": [ + "## Load and Chunk Text Data\n", + "\n", + "Load the large text dataset and split it into paragraph-sized chunks." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "load-data", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded text file with 13764 characters\n", + "Preview:\n", + "TechVision 2024 Global Conference Opens in Austin\n", + "\n", + "Austin, TX — The TechVision 2024 Global Conference kicked off on March 15th at the Austin Convention Center, drawing over 5,000 attendees from around the world. The conference, organized by Global Tech Events Inc., focuses on emerging technologies i...\n" + ] + } + ], + "source": [ + "# Load the text data\n", + "data_file = Path(\"../tests/integration_tests/data/large_graph_dataset.txt\")\n", + "text_content = data_file.read_text()\n", + "\n", + "print(f\"Loaded text file with {len(text_content)} characters\")\n", + "print(f\"Preview:\\n{text_content[:300]}...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "chunk-text", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split text into 20 chunks\n", + "Created 20 Document objects\n" + ] + } + ], + "source": [ + "# Split text into chunks using RecursiveCharacterTextSplitter\n", + "# This creates natural document boundaries at paragraph breaks\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " separators=[\"\\n\\n\", \"\\n\", \" \", \"\"],\n", + " chunk_size=1000,\n", + " chunk_overlap=100,\n", + " length_function=len,\n", + ")\n", + "\n", + "text_chunks = text_splitter.split_text(text_content)\n", + "print(f\"Split text into {len(text_chunks)} chunks\")\n", + "\n", + "# Convert to Document objects\n", + "documents = [Document(page_content=chunk) for chunk in text_chunks]\n", + "print(f\"Created {len(documents)} Document objects\")" + ] + }, + { + "cell_type": "markdown", + "id": "graph-construction-section", + "metadata": {}, + "source": [ + "## Build Knowledge Graph\n", + "\n", + "Create `MongoDBGraphStore` and extract entities from the documents.\n", + "\n", + "**Note:** This step takes several minutes as the LLM processes each chunk to extract entities and relationships." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "create-graph-store", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created MongoDBGraphStore connected to langchain_test_db.langchain_graphrag_large_example\n" + ] + } + ], + "source": [ + "# Create the graph store\n", + "graph_store = MongoDBGraphStore(\n", + " connection_string=CONNECTION_STRING,\n", + " database_name=DB_NAME,\n", + " collection_name=COLLECTION_NAME,\n", + " entity_extraction_model=entity_extraction_model,\n", + " max_depth=3, # Allow deeper graph traversal for complex queries\n", + ")\n", + "\n", + "print(f\"Created MongoDBGraphStore connected to {DB_NAME}.{COLLECTION_NAME}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "extract-entities", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 20 documents...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Targets, types, and attributes do not have the same length for TechVision 2024 Global Conference!\n", + "Targets, types, and attributes do not have the same length for Quantum Dynamics Corp!\n", + "Targets, types, and attributes do not have the same length for TechVision Conference!\n", + "Targets, types, and attributes do not have the same length for AI Ethics Initiative!\n", + "Targets, types, and attributes do not have the same length for Green Computing Alliance!\n", + "Targets, types, and attributes do not have the same length for James Wilson!\n", + "Targets, types, and attributes do not have the same length for Carbon Zero Data Center Project!\n", + "Targets, types, and attributes do not have the same length for SolarTech Industries!\n", + "Targets, types, and attributes do not have the same length for Marcus Brown!\n", + "Targets, types, and attributes do not have the same length for Blockchain Healthcare Consortium!\n", + "Targets, types, and attributes do not have the same length for Blockchain Healthcare Consortium!\n", + "Targets, types, and attributes do not have the same length for MediChain Solutions!\n", + "Targets, types, and attributes do not have the same length for HealthSecure Systems!\n", + "Targets, types, and attributes do not have the same length for PatientFirst Technologies!\n", + "Targets, types, and attributes do not have the same length for CareConnect Networks!\n", + "Targets, types, and attributes do not have the same length for VitalData Corp!\n", + "Targets, types, and attributes do not have the same length for Dr. Amanda Foster!\n", + "Targets, types, and attributes do not have the same length for Dr. Thomas Anderson!\n", + "Targets, types, and attributes do not have the same length for SkyLink Project!\n", + "Targets, types, and attributes do not have the same length for Rachel Martinez!\n", + "Targets, types, and attributes do not have the same length for Cybersecurity Summit!\n", + "Targets, types, and attributes do not have the same length for SecureNet Solutions!\n", + "Targets, types, and attributes do not have the same length for CryptoGuard Technologies!\n", + "Targets, types, and attributes do not have the same length for Dr. Kevin O'Brien!\n", + "Targets, types, and attributes do not have the same length for Professor Alice Thompson!\n", + "Targets, types, and attributes do not have the same length for Dr. Yuki Tanaka!\n", + "Targets, types, and attributes do not have the same length for QuantumShield!\n", + "Targets, types, and attributes do not have the same length for BioTech Innovations!\n", + "Targets, types, and attributes do not have the same length for Dr. Maria Santos!\n", + "Targets, types, and attributes do not have the same length for Dr. James Patterson!\n", + "Targets, types, and attributes do not have the same length for Genomics Research Center!\n", + "Targets, types, and attributes do not have the same length for Molecular Computing Corp!\n", + "Targets, types, and attributes do not have the same length for Oncology Research Institute!\n", + "Targets, types, and attributes do not have the same length for Memorial Medical Center!\n", + "Targets, types, and attributes do not have the same length for TechVision 2024!\n", + "Targets, types, and attributes do not have the same length for Smart Cities Initiative!\n", + "Targets, types, and attributes do not have the same length for Carlos Rodriguez!\n", + "Targets, types, and attributes do not have the same length for Patricia Williams!\n", + "Targets, types, and attributes do not have the same length for Renewable Energy Tech Summit!\n", + "Targets, types, and attributes do not have the same length for SunPower Innovations!\n", + "Targets, types, and attributes do not have the same length for Advanced Solar Materials!\n", + "Targets, types, and attributes do not have the same length for Global Energy Solutions!\n", + "Targets, types, and attributes do not have the same length for EduVerse Technologies!\n", + "Targets, types, and attributes do not have the same length for Dr. Christopher Lee!\n", + "Targets, types, and attributes do not have the same length for Digital Learning Alliance!\n", + "Targets, types, and attributes do not have the same length for Interactive Education Systems!\n", + "Targets, types, and attributes do not have the same length for Global School Network!\n", + "Targets, types, and attributes do not have the same length for Educational Content Creators Guild!\n", + "Targets, types, and attributes do not have the same length for International Education Standards Board!\n", + "Targets, types, and attributes do not have the same length for Samantha Brown!\n", + "Targets, types, and attributes do not have the same length for Autonomous Vehicle Consortium!\n", + "Targets, types, and attributes do not have the same length for Self-Drive Motors!\n", + "Targets, types, and attributes do not have the same length for Open Banking Coalition!\n", + "Targets, types, and attributes do not have the same length for Digital Payments Corp!\n", + "Targets, types, and attributes do not have the same length for Mobile Banking Solutions!\n", + "Targets, types, and attributes do not have the same length for Investment Tech Group!\n", + "Targets, types, and attributes do not have the same length for InsureTech Innovations!\n", + "Targets, types, and attributes do not have the same length for LendingTree Technologies!\n", + "Targets, types, and attributes do not have the same length for Michael Zhang!\n", + "Targets, types, and attributes do not have the same length for Federal Banking Regulators!\n", + "Targets, types, and attributes do not have the same length for Financial Security Council!\n", + "Targets, types, and attributes do not have the same length for International Banking Standards Organization!\n", + "Targets, types, and attributes do not have the same length for API Standards Institute!\n", + "Targets, types, and attributes do not have the same length for Financial Data Alliance!\n", + "Targets, types, and attributes do not have the same length for Ocean Monitoring Network!\n", + "Targets, types, and attributes do not have the same length for Marine Research Institute!\n", + "Targets, types, and attributes do not have the same length for EdgeCloud Technologies!\n", + "Targets, types, and attributes do not have the same length for Brian Murphy!\n", + "Targets, types, and attributes do not have the same length for Dr. Susan Park!\n", + "Targets, types, and attributes do not have the same length for TranslateTech Global!\n", + "Targets, types, and attributes do not have the same length for NanoMed Devices!\n", + "Targets, types, and attributes do not have the same length for Fusion Dynamics Corporation!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed 20 document chunks\n", + "Entity extraction complete!\n" + ] + } + ], + "source": [ + "# Extract entities from documents\n", + "# This may take 1-2 minutes depending on the number of chunks and LLM response time\n", + "print(f\"Processing {len(documents)} documents...\")\n", + "bulkwrite_results = graph_store.add_documents(documents)\n", + "\n", + "print(f\"Processed {len(bulkwrite_results)} document chunks\")\n", + "print(\"Entity extraction complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "stats-section", + "metadata": {}, + "source": [ + "## Graph Statistics\n", + "\n", + "Examine the extracted knowledge graph." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "graph-stats", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total entities extracted: 181\n", + "\n", + "Entity types:\n", + " Organization: 126\n", + " Person: 36\n", + " Event: 6\n", + " Location: 5\n", + " Product: 3\n", + " Project: 3\n", + " Technology: 1\n", + " Concept: 1\n", + "\n", + "Relationships:\n", + " Entities with relationships: 84\n", + " Total relationship edges: 212\n", + " Average relationships per connected entity: 2.5\n" + ] + } + ], + "source": [ + "# Count entities\n", + "entity_count = graph_store.collection.count_documents({})\n", + "print(f\"Total entities extracted: {entity_count}\")\n", + "\n", + "# Get all entities\n", + "entities = list(graph_store.collection.find({}))\n", + "\n", + "# Analyze entity types\n", + "entity_types = {}\n", + "for entity in entities:\n", + " entity_type = entity.get(\"type\", \"Unknown\")\n", + " entity_types[entity_type] = entity_types.get(entity_type, 0) + 1\n", + "\n", + "print(\"\\nEntity types:\")\n", + "for entity_type, count in sorted(\n", + " entity_types.items(), key=lambda x: x[1], reverse=True\n", + "):\n", + " print(f\" {entity_type}: {count}\")\n", + "\n", + "# Analyze relationships\n", + "entities_with_relationships = [\n", + " entity for entity in entities if entity.get(\"relationships\", {}).get(\"target_ids\")\n", + "]\n", + "\n", + "total_relationships = sum(\n", + " len(entity.get(\"relationships\", {}).get(\"target_ids\", []))\n", + " for entity in entities_with_relationships\n", + ")\n", + "\n", + "print(\"\\nRelationships:\")\n", + "print(f\" Entities with relationships: {len(entities_with_relationships)}\")\n", + "print(f\" Total relationship edges: {total_relationships}\")\n", + "print(\n", + " f\" Average relationships per connected entity: {total_relationships / len(entities_with_relationships):.1f}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "sample-entities-section", + "metadata": {}, + "source": [ + "## Sample Entities\n", + "\n", + "Display a few example entities from the graph." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "show-samples", + "metadata": {}, + "outputs": [], + "source": "# Show some sample entities with relationships\nprint(\"Sample entities with relationships:\\n\")\nfor i, entity in enumerate(entities_with_relationships[:5]):\n print(f\"{i+1}. {entity['_id']} ({entity.get('type')})\")\n relationships = entity.get(\"relationships\", {})\n target_ids = relationships.get(\"target_ids\", [])\n rel_types = relationships.get(\"types\", [])\n print(f\" Relationships: {len(target_ids)}\")\n for target, rel_type in zip(target_ids[:3], rel_types[:3]):\n print(f\" - {rel_type} -> {target}\")\n if len(target_ids) > 3:\n print(f\" ... and {len(target_ids) - 3} more\")\n print()" + }, + { + "cell_type": "markdown", + "id": "query-section", + "metadata": {}, + "source": [ + "## Query the Knowledge Graph\n", + "\n", + "Test querying the graph to find related entities." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "query-graph", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted entities from query: ['Quantum Dynamics Corp', 'NanoTech Materials Ltd']\n", + "\n", + "Found 5 related entities:\n", + " - Quantum Processor (Technology)\n", + " - Dr. Michael Torres (Person)\n", + " - Quantum Dynamics Corp (Organization)\n", + " - NanoTech Materials Ltd. (Organization)\n", + " - QX-7000 (Product)\n" + ] + } + ], + "source": [ + "# Example query about relationships\n", + "query = (\n", + " \"What is the connection between Quantum Dynamics Corp and NanoTech Materials Ltd?\"\n", + ")\n", + "\n", + "# Extract entity names from query\n", + "entity_names = graph_store.extract_entity_names(query)\n", + "print(f\"Extracted entities from query: {entity_names}\")\n", + "\n", + "# Find related entities through graph traversal\n", + "if entity_names:\n", + " related_entities = graph_store.related_entities(entity_names)\n", + " print(f\"\\nFound {len(related_entities)} related entities:\")\n", + " for entity in related_entities[:10]:\n", + " print(f\" - {entity['_id']} ({entity.get('type')})\")\n", + " if len(related_entities) > 10:\n", + " print(f\" ... and {len(related_entities) - 10} more\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "chat-response", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chat Response:\n", + "Quantum Dynamics Corp and NanoTech Materials Ltd. are connected through a partnership, with NanoTech Materials Ltd. providing superconducting materials to Quantum Dynamics Corp.\n" + ] + } + ], + "source": [ + "# Get a natural language response using the knowledge graph\n", + "answer = graph_store.chat_response(query)\n", + "print(f\"Chat Response:\\n{answer.content}\")" + ] + }, + { + "cell_type": "markdown", + "id": "viz-section", + "metadata": {}, + "source": [ + "## Visualize Knowledge Graph\n", + "\n", + "Create interactive visualizations of the knowledge graph using HoloViews and NetworkX.\n", + "\n", + "### Basic Graph View\n", + "\n", + "First, we'll create a basic visualization of the entire graph." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "basic-view", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + 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\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Graph.I :Graph [start,end] (attributes,type)\n", + " .Nodes.I :Nodes [x,y,index] (attributes,type)" + ] + }, + "execution_count": 13, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "ace31b53-6216-459e-9cd7-5669144aebff" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Create basic view of the entire graph\n", + "basic_view = graph_store.view()\n", + "basic_view" + ] + }, + { + "cell_type": "markdown", + "id": "networkx-section", + "metadata": {}, + "source": [ + "### NetworkX Graph Layouts\n", + "\n", + "Convert to NetworkX for advanced layout algorithms and analytics." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "to-networkx", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NetworkX graph: 181 nodes, 212 edges\n", + "Graph density: 0.0065\n", + "Is connected: False\n", + "Number of connected components: 23\n", + "Largest component size: 16\n" + ] + } + ], + "source": [ + "# Convert to NetworkX graph\n", + "nx_graph = graph_store.to_networkx()\n", + "\n", + "print(\n", + " f\"NetworkX graph: {nx_graph.number_of_nodes()} nodes, {nx_graph.number_of_edges()} edges\"\n", + ")\n", + "print(f\"Graph density: {nx.density(nx_graph):.4f}\")\n", + "\n", + "# Check if graph is connected\n", + "if nx_graph.number_of_nodes() > 0:\n", + " is_connected = nx.is_connected(nx_graph.to_undirected())\n", + " print(f\"Is connected: {is_connected}\")\n", + "\n", + " if not is_connected:\n", + " components = list(nx.connected_components(nx_graph.to_undirected()))\n", + " print(f\"Number of connected components: {len(components)}\")\n", + " print(f\"Largest component size: {len(max(components, key=len))}\")" + ] + }, + { + "cell_type": "markdown", + "id": "multipartite-section", + "metadata": {}, + "source": [ + "### Multipartite Layout (by Entity Type)\n", + "\n", + "Visualize the graph with entities grouped by type." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "multipartite-view", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "
\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Graph.I :Graph [start,end] (attributes,type)\n", + " .Nodes.I :Nodes [x,y,index] (attributes,type)" + ] + }, + "execution_count": 15, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "72e27789-896e-4b17-b03e-e82812da0519" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Create multipartite layout grouping entities by type\n", + "by_type = hv.Graph.from_networkx(nx_graph, nx.multipartite_layout, subset_key=\"type\")\n", + "type_view = by_type.opts(\n", + " inspection_policy=\"edges\",\n", + " tools=[\"hover\", \"tap\"],\n", + " node_color=\"type\",\n", + " cmap=\"Category20\",\n", + " node_size=10,\n", + " edge_line_width=1,\n", + " edge_alpha=0.5,\n", + ") * by_type.nodes.opts(\n", + " size=10,\n", + " color=\"type\",\n", + " cmap=\"Category20\",\n", + " alpha=0.8,\n", + ")\n", + "\n", + "type_view" + ] + }, + { + "cell_type": "markdown", + "id": "spring-section", + "metadata": {}, + "source": [ + "### Spring Layout\n", + "\n", + "Use a force-directed layout for organic graph visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "spring-view", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "
\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Graph.I :Graph [start,end] (attributes,type)\n", + " .Nodes.I :Nodes [x,y,index] (attributes,type)" + ] + }, + "execution_count": 16, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "8912618d-b8fb-42c9-aa4c-398619ae5931" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Create spring layout (force-directed)\n", + "spring_layout = hv.Graph.from_networkx(nx_graph, nx.spring_layout, k=0.5, iterations=50)\n", + "spring_view = spring_layout.opts(\n", + " inspection_policy=\"edges\",\n", + " tools=[\"hover\", \"tap\"],\n", + " node_color=\"type\",\n", + " cmap=\"Category20\",\n", + " node_size=10,\n", + " edge_line_width=1,\n", + " edge_alpha=0.4,\n", + ") * spring_layout.nodes.opts(\n", + " size=10,\n", + " color=\"type\",\n", + " cmap=\"Category20\",\n", + " alpha=0.8,\n", + ")\n", + "\n", + "spring_view" + ] + }, + { + "cell_type": "markdown", + "id": "subgraph-section", + "metadata": {}, + "source": [ + "### Focused Subgraph Visualization\n", + "\n", + "Visualize a subgraph around specific entities for detailed exploration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "subgraph-view", + "metadata": {}, + "outputs": [], + "source": "# Pick an entity with many connections\nif entities_with_relationships:\n # Find entity with most relationships\n most_connected = max(\n entities_with_relationships,\n key=lambda e: len(e.get(\"relationships\", {}).get(\"target_ids\", [])),\n )\n\n print(\n f\"Most connected entity: {most_connected['_id']} ({most_connected.get('type')})\"\n )\n print(\n f\"Number of relationships: {len(most_connected.get('relationships', {}).get('target_ids', []))}\"\n )\n\n # Get subgraph around this entity\n focus_entity = most_connected[\"_id\"]\n related = graph_store.related_entities([focus_entity], max_depth=2)\n\n print(f\"\\nSubgraph contains {len(related)} entities\")\n\n # Create a subgraph from these entities\n subgraph_nodes = {entity[\"_id\"] for entity in related}\n subgraph = nx_graph.subgraph(subgraph_nodes).copy()\n\n print(\n f\"Subgraph: {subgraph.number_of_nodes()} nodes, {subgraph.number_of_edges()} edges\"\n )\n\n # Visualize subgraph\n if subgraph.number_of_nodes() > 0:\n sub_spring = hv.Graph.from_networkx(\n subgraph, nx.spring_layout, k=0.8, iterations=50\n )\n subgraph_view = sub_spring.opts(\n inspection_policy=\"edges\",\n tools=[\"hover\", \"tap\"],\n node_color=\"type\",\n cmap=\"Category20\",\n node_size=15,\n edge_line_width=2,\n edge_alpha=0.6,\n title=f\"Subgraph around {focus_entity}\",\n ) * sub_spring.nodes.opts(\n size=15,\n color=\"type\",\n cmap=\"Category20\",\n alpha=0.9,\n )\n\n# Display the subgraph view\nsubgraph_view # noqa: F821" + }, + { + "cell_type": "markdown", + "id": "export-section", + "metadata": {}, + "source": [ + "## Export Visualizations\n", + "\n", + "Save the visualizations as HTML files for sharing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "export-viz", + "metadata": {}, + "outputs": [], + "source": [ + "# Save visualizations\n", + "hv.save(type_view, \"graph_by_type.html\")\n", + "print(\"Saved graph_by_type.html\")\n", + "\n", + "hv.save(spring_view, \"graph_spring.html\")\n", + "print(\"Saved graph_spring.html\")\n", + "\n", + "print(\"\\nVisualizations exported successfully!\")" + ] + }, + { + "cell_type": "markdown", + "id": "summary-section", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we:\n", + "\n", + "1. ✅ Loaded a large text corpus with ~100 entities\n", + "2. ✅ Chunked text into documents using `RecursiveCharacterTextSplitter`\n", + "3. ✅ Built a knowledge graph using `MongoDBGraphStore` and LLM-based entity extraction\n", + "4. ✅ Analyzed graph statistics (entities, types, relationships)\n", + "5. ✅ Queried the graph for related entities and chat responses\n", + "6. ✅ Visualized the graph using multiple layout algorithms\n", + "7. ✅ Explored focused subgraphs around highly-connected entities\n", + "8. ✅ Exported interactive visualizations as HTML\n", + "\n", + "The resulting knowledge graph can be used for:\n", + "- Complex multi-hop queries\n", + "- Entity relationship discovery\n", + "- Context-aware chat responses\n", + "- Graph analytics and network analysis" + ] + } + ], + "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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5212e0c7badaa44072bb87ce423636f22bc03945 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 7 Oct 2025 20:07:22 -0400 Subject: [PATCH 13/37] Improve entity extraction prompts for relationship array alignment --- .../graphrag/example_templates.py | 36 +++++++++++-------- .../langchain_mongodb/graphrag/prompts.py | 34 +++++++++++++++++- 2 files changed, 54 insertions(+), 16 deletions(-) diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/example_templates.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/example_templates.py index 236bbf71..c374ba07 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/example_templates.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/example_templates.py @@ -25,7 +25,7 @@ "startDate": ["2018-01-01"] }}, "relationships": {{ - "targets": ["Jasbinder Kaur", "Jarnail Singh"], + "target_ids": ["Jasbinder Kaur", "Jarnail Singh"], "types": ["Friend", "Friend"], "attributes": [ {{ "since": ["2019-05-01"] }}, @@ -37,7 +37,7 @@ "_id": "Jarnail Singh", "type": "Person", "relationships": {{ - "targets": ["Alice Palace"], + "target_ids": ["Alice Palace"], "types": ["Friend"], "attributes": [{{ "since": ["2019-05-01"] }}] }} @@ -46,7 +46,7 @@ "_id": "Jasbinder Kaur", "type": "Person", "relationships": {{ - "targets": ["Alice Palace"], + "target_ids": ["Alice Palace"], "types": ["Friend"], "attributes": [{{ "since": ["2015-05-01"], "frequency": ["weekly"] }}] }} @@ -71,8 +71,9 @@ "location": ["San Francisco"] }}, "relationships": {{ - "targets": ["Elon Musk", "Sam Altman"], - "types": ["Speaker", "Speaker"] + "target_ids": ["Elon Musk", "Sam Altman"], + "types": ["Speaker", "Speaker"], + "attributes": [{{}}, {{}}] }} }}, {{ "_id": "Elon Musk", "type": "Person" }}, @@ -92,8 +93,9 @@ "_id": "Quantum Computing", "type": "Concept", "relationships": {{ - "targets": ["Quantum Mechanics"], - "types": ["Based On"] + "target_ids": ["Quantum Mechanics"], + "types": ["Based On"], + "attributes": [{{}}] }} }}, {{ "_id": "Quantum Mechanics", "type": "Concept" }} @@ -114,8 +116,9 @@ "type": "Event", "attributes": {{ "date": ["2023-03-01"] }}, "relationships": {{ - "targets": ["NASA"], - "types": ["Managed By"] + "target_ids": ["NASA"], + "types": ["Managed By"], + "attributes": [{{}}] }} }}, {{ @@ -126,8 +129,9 @@ "_id": "Bill Nelson", "type": "Person", "relationships": {{ - "targets": ["Artemis II Mission"], - "types": ["Praised By"] + "target_ids": ["Artemis II Mission"], + "types": ["Praised By"], + "attributes": [{{}}] }} }} ] @@ -146,16 +150,18 @@ "_id": "Rust", "type": "Programming Language", "relationships": {{ - "targets": ["Memory Safety"], - "types": ["Ensures"] + "target_ids": ["Memory Safety"], + "types": ["Ensures"], + "attributes": [{{}}] }} }}, {{ "_id": "Memory Safety", "type": "Concept", "relationships": {{ - "targets": ["Ownership Model"], - "types": ["Uses"] + "target_ids": ["Ownership Model"], + "types": ["Uses"], + "attributes": [{{}}] }} }}, {{ "_id": "Ownership Model", "type": "Concept" }} diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/prompts.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/prompts.py index c7e1775b..0efa37a5 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/prompts.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/prompts.py @@ -46,6 +46,37 @@ Instead of using specific and momentary types such as 'worked_at', use more general and timeless relationship types like 'employee'. Add details as attributes. Make sure to use general and timeless relationship types! +### CRITICAL: Array Length Alignment +The relationships object contains three arrays: `target_ids`, `types`, and `attributes`. +**These three arrays MUST have EXACTLY the same length.** +- Each position (index) in these arrays describes ONE complete relationship. +- Position 0 in `target_ids`, `types`, and `attributes` together describe the first relationship. +- Position 1 in `target_ids`, `types`, and `attributes` together describe the second relationship. +- And so on... + +If a relationship has no attributes, you MUST still include an empty object `{{}}` in the `attributes` array at that position. + +Example of CORRECT alignment: +```json +"relationships": {{ + "target_ids": ["Entity A", "Entity B"], + "types": ["partners", "supplier"], + "attributes": [ + {{"since": ["2020"]}}, + {{}} + ] +}} +``` + +Example of INCORRECT (DO NOT DO THIS): +```json +"relationships": {{ + "target_ids": ["Entity A", "Entity B"], + "types": ["partners"], + "attributes": [{{"since": ["2020"]}}] +}} +``` + **Allowed Relationship Types**: - Extract ONLY relationships whose `type` matches one of the following: {allowed_relationship_types}. - If this list is empty, ANY relationship type is permitted. @@ -64,7 +95,8 @@ 1. Validate that all extracted entities have an `_id` and `type`. 2. Validate that all `type` values are in {allowed_entity_types}. 3. Validate that all relationships use keys in {allowed_relationship_types}. -4. Exclude any entities or relationships failing validation. +4. **CRITICAL**: For each entity with relationships, verify that `target_ids`, `types`, and `attributes` arrays have EXACTLY the same length. +5. Exclude any entities or relationships failing validation. ## Output Schema Output a valid JSON document with a single top-level key, `entities`, as an array of objects. From 2795d9ecab24f867911955ebe5553dde450d48cb Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Wed, 8 Oct 2025 13:53:59 -0400 Subject: [PATCH 14/37] Completed self-standing GraphRAG example. Removed second one. --- .../examples/GraphRAG-large.ipynb | 2466 ---------------- .../langchain-mongodb/examples/GraphRAG.ipynb | 2606 ++++++++++++++++- uv.lock | 1 + 3 files changed, 2508 insertions(+), 2565 deletions(-) delete mode 100644 libs/langchain-mongodb/examples/GraphRAG-large.ipynb diff --git a/libs/langchain-mongodb/examples/GraphRAG-large.ipynb b/libs/langchain-mongodb/examples/GraphRAG-large.ipynb deleted file mode 100644 index ff9335a8..00000000 --- a/libs/langchain-mongodb/examples/GraphRAG-large.ipynb +++ /dev/null @@ -1,2466 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "62c5b493-9b7d-45dc-be3b-9ea1c77ad7d8", - "metadata": {}, - "source": [ - "# Large-Scale Knowledge Graph Construction and Visualization\n", - "\n", - "This notebook demonstrates building a substantial knowledge graph from a text corpus containing approximately 100 entities.\n", - "\n", - "We will:\n", - "1. Load a large text dataset about technology companies, people, and projects\n", - "2. Chunk the text into paragraph-sized documents using `RecursiveCharacterTextSplitter`\n", - "3. Extract entities and relationships using `MongoDBGraphStore`\n", - "4. Visualize the resulting knowledge graph using HoloViews and NetworkX\n", - "\n", - "The dataset contains news-style articles about:\n", - "- Organizations (tech companies, research institutions, nonprofits)\n", - "- People (CEOs, scientists, directors)\n", - "- Projects and initiatives\n", - "- Locations and facilities\n", - "- Technologies and products" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3f3fb616-c1c1-4f82-b093-ad3f37a728e6", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "import holoviews as hv\n", - "import networkx as nx\n", - "from holoviews import opts\n", - "from langchain_core.documents import Document\n", - "from langchain_openai import ChatOpenAI\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "\n", - "from langchain_mongodb.graphrag.graph import MongoDBGraphStore" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": 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setTimeout(load_or_wait, 100)\n", - "}(window));" - ], - "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if 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element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.0.min.js\", \"https://cdn.holoviz.org/panel/1.8.2/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n 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\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hv.extension(\"bokeh\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "52fc6635-87e9-4179-b6a4-404c988904c5", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up default plot dimensions for better viewing of large graphs\n", - "defaults = dict(width=1200, height=800)\n", - "hv.opts.defaults(\n", - " opts.EdgePaths(**defaults), opts.Graph(**defaults), opts.Nodes(**defaults)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "config-section", - "metadata": {}, - "source": [ - "## Configuration\n", - "\n", - "Set up MongoDB connection and LLM for entity extraction." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "69026170-5d6d-43a1-9fbb-9a52461e1e0b", - "metadata": {}, - "outputs": [], - "source": [ - "CONNECTION_STRING = os.environ.get(\"MONGODB_URI\", \"\")\n", - "DB_NAME = \"langchain_test_db\"\n", - "COLLECTION_NAME = \"langchain_graphrag_large_example\"\n", - "\n", - "# Configure the LLM for entity extraction\n", - "# Using gpt-4o for high-quality entity extraction\n", - "entity_extraction_model = ChatOpenAI(\n", - " model=\"gpt-4o\", temperature=0.0, cache=False, seed=12345\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "data-loading-section", - "metadata": {}, - "source": [ - "## Load and Chunk Text Data\n", - "\n", - "Load the large text dataset and split it into paragraph-sized chunks." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "load-data", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded text file with 13764 characters\n", - "Preview:\n", - "TechVision 2024 Global Conference Opens in Austin\n", - "\n", - "Austin, TX — The TechVision 2024 Global Conference kicked off on March 15th at the Austin Convention Center, drawing over 5,000 attendees from around the world. The conference, organized by Global Tech Events Inc., focuses on emerging technologies i...\n" - ] - } - ], - "source": [ - "# Load the text data\n", - "data_file = Path(\"../tests/integration_tests/data/large_graph_dataset.txt\")\n", - "text_content = data_file.read_text()\n", - "\n", - "print(f\"Loaded text file with {len(text_content)} characters\")\n", - "print(f\"Preview:\\n{text_content[:300]}...\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "chunk-text", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Split text into 20 chunks\n", - "Created 20 Document objects\n" - ] - } - ], - "source": [ - "# Split text into chunks using RecursiveCharacterTextSplitter\n", - "# This creates natural document boundaries at paragraph breaks\n", - "text_splitter = RecursiveCharacterTextSplitter(\n", - " separators=[\"\\n\\n\", \"\\n\", \" \", \"\"],\n", - " chunk_size=1000,\n", - " chunk_overlap=100,\n", - " length_function=len,\n", - ")\n", - "\n", - "text_chunks = text_splitter.split_text(text_content)\n", - "print(f\"Split text into {len(text_chunks)} chunks\")\n", - "\n", - "# Convert to Document objects\n", - "documents = [Document(page_content=chunk) for chunk in text_chunks]\n", - "print(f\"Created {len(documents)} Document objects\")" - ] - }, - { - "cell_type": "markdown", - "id": "graph-construction-section", - "metadata": {}, - "source": [ - "## Build Knowledge Graph\n", - "\n", - "Create `MongoDBGraphStore` and extract entities from the documents.\n", - "\n", - "**Note:** This step takes several minutes as the LLM processes each chunk to extract entities and relationships." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "create-graph-store", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created MongoDBGraphStore connected to langchain_test_db.langchain_graphrag_large_example\n" - ] - } - ], - "source": [ - "# Create the graph store\n", - "graph_store = MongoDBGraphStore(\n", - " connection_string=CONNECTION_STRING,\n", - " database_name=DB_NAME,\n", - " collection_name=COLLECTION_NAME,\n", - " entity_extraction_model=entity_extraction_model,\n", - " max_depth=3, # Allow deeper graph traversal for complex queries\n", - ")\n", - "\n", - "print(f\"Created MongoDBGraphStore connected to {DB_NAME}.{COLLECTION_NAME}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "extract-entities", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing 20 documents...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Targets, types, and attributes do not have the same length for TechVision 2024 Global Conference!\n", - "Targets, types, and attributes do not have the same length for Quantum Dynamics Corp!\n", - "Targets, types, and attributes do not have the same length for TechVision Conference!\n", - "Targets, types, and attributes do not have the same length for AI Ethics Initiative!\n", - "Targets, types, and attributes do not have the same length for Green Computing Alliance!\n", - "Targets, types, and attributes do not have the same length for James Wilson!\n", - "Targets, types, and attributes do not have the same length for Carbon Zero Data Center Project!\n", - "Targets, types, and attributes do not have the same length for SolarTech Industries!\n", - "Targets, types, and attributes do not have the same length for Marcus Brown!\n", - "Targets, types, and attributes do not have the same length for Blockchain Healthcare Consortium!\n", - "Targets, types, and attributes do not have the same length for Blockchain Healthcare Consortium!\n", - "Targets, types, and attributes do not have the same length for MediChain Solutions!\n", - "Targets, types, and attributes do not have the same length for HealthSecure Systems!\n", - "Targets, types, and attributes do not have the same length for PatientFirst Technologies!\n", - "Targets, types, and attributes do not have the same length for CareConnect Networks!\n", - "Targets, types, and attributes do not have the same length for VitalData Corp!\n", - "Targets, types, and attributes do not have the same length for Dr. Amanda Foster!\n", - "Targets, types, and attributes do not have the same length for Dr. Thomas Anderson!\n", - "Targets, types, and attributes do not have the same length for SkyLink Project!\n", - "Targets, types, and attributes do not have the same length for Rachel Martinez!\n", - "Targets, types, and attributes do not have the same length for Cybersecurity Summit!\n", - "Targets, types, and attributes do not have the same length for SecureNet Solutions!\n", - "Targets, types, and attributes do not have the same length for CryptoGuard Technologies!\n", - "Targets, types, and attributes do not have the same length for Dr. Kevin O'Brien!\n", - "Targets, types, and attributes do not have the same length for Professor Alice Thompson!\n", - "Targets, types, and attributes do not have the same length for Dr. Yuki Tanaka!\n", - "Targets, types, and attributes do not have the same length for QuantumShield!\n", - "Targets, types, and attributes do not have the same length for BioTech Innovations!\n", - "Targets, types, and attributes do not have the same length for Dr. Maria Santos!\n", - "Targets, types, and attributes do not have the same length for Dr. James Patterson!\n", - "Targets, types, and attributes do not have the same length for Genomics Research Center!\n", - "Targets, types, and attributes do not have the same length for Molecular Computing Corp!\n", - "Targets, types, and attributes do not have the same length for Oncology Research Institute!\n", - "Targets, types, and attributes do not have the same length for Memorial Medical Center!\n", - "Targets, types, and attributes do not have the same length for TechVision 2024!\n", - "Targets, types, and attributes do not have the same length for Smart Cities Initiative!\n", - "Targets, types, and attributes do not have the same length for Carlos Rodriguez!\n", - "Targets, types, and attributes do not have the same length for Patricia Williams!\n", - "Targets, types, and attributes do not have the same length for Renewable Energy Tech Summit!\n", - "Targets, types, and attributes do not have the same length for SunPower Innovations!\n", - "Targets, types, and attributes do not have the same length for Advanced Solar Materials!\n", - "Targets, types, and attributes do not have the same length for Global Energy Solutions!\n", - "Targets, types, and attributes do not have the same length for EduVerse Technologies!\n", - "Targets, types, and attributes do not have the same length for Dr. Christopher Lee!\n", - "Targets, types, and attributes do not have the same length for Digital Learning Alliance!\n", - "Targets, types, and attributes do not have the same length for Interactive Education Systems!\n", - "Targets, types, and attributes do not have the same length for Global School Network!\n", - "Targets, types, and attributes do not have the same length for Educational Content Creators Guild!\n", - "Targets, types, and attributes do not have the same length for International Education Standards Board!\n", - "Targets, types, and attributes do not have the same length for Samantha Brown!\n", - "Targets, types, and attributes do not have the same length for Autonomous Vehicle Consortium!\n", - "Targets, types, and attributes do not have the same length for Self-Drive Motors!\n", - "Targets, types, and attributes do not have the same length for Open Banking Coalition!\n", - "Targets, types, and attributes do not have the same length for Digital Payments Corp!\n", - "Targets, types, and attributes do not have the same length for Mobile Banking Solutions!\n", - "Targets, types, and attributes do not have the same length for Investment Tech Group!\n", - "Targets, types, and attributes do not have the same length for InsureTech Innovations!\n", - "Targets, types, and attributes do not have the same length for LendingTree Technologies!\n", - "Targets, types, and attributes do not have the same length for Michael Zhang!\n", - "Targets, types, and attributes do not have the same length for Federal Banking Regulators!\n", - "Targets, types, and attributes do not have the same length for Financial Security Council!\n", - "Targets, types, and attributes do not have the same length for International Banking Standards Organization!\n", - "Targets, types, and attributes do not have the same length for API Standards Institute!\n", - "Targets, types, and attributes do not have the same length for Financial Data Alliance!\n", - "Targets, types, and attributes do not have the same length for Ocean Monitoring Network!\n", - "Targets, types, and attributes do not have the same length for Marine Research Institute!\n", - "Targets, types, and attributes do not have the same length for EdgeCloud Technologies!\n", - "Targets, types, and attributes do not have the same length for Brian Murphy!\n", - "Targets, types, and attributes do not have the same length for Dr. Susan Park!\n", - "Targets, types, and attributes do not have the same length for TranslateTech Global!\n", - "Targets, types, and attributes do not have the same length for NanoMed Devices!\n", - "Targets, types, and attributes do not have the same length for Fusion Dynamics Corporation!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processed 20 document chunks\n", - "Entity extraction complete!\n" - ] - } - ], - "source": [ - "# Extract entities from documents\n", - "# This may take 1-2 minutes depending on the number of chunks and LLM response time\n", - "print(f\"Processing {len(documents)} documents...\")\n", - "bulkwrite_results = graph_store.add_documents(documents)\n", - "\n", - "print(f\"Processed {len(bulkwrite_results)} document chunks\")\n", - "print(\"Entity extraction complete!\")" - ] - }, - { - "cell_type": "markdown", - "id": "stats-section", - "metadata": {}, - "source": [ - "## Graph Statistics\n", - "\n", - "Examine the extracted knowledge graph." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "graph-stats", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total entities extracted: 181\n", - "\n", - "Entity types:\n", - " Organization: 126\n", - " Person: 36\n", - " Event: 6\n", - " Location: 5\n", - " Product: 3\n", - " Project: 3\n", - " Technology: 1\n", - " Concept: 1\n", - "\n", - "Relationships:\n", - " Entities with relationships: 84\n", - " Total relationship edges: 212\n", - " Average relationships per connected entity: 2.5\n" - ] - } - ], - "source": [ - "# Count entities\n", - "entity_count = graph_store.collection.count_documents({})\n", - "print(f\"Total entities extracted: {entity_count}\")\n", - "\n", - "# Get all entities\n", - "entities = list(graph_store.collection.find({}))\n", - "\n", - "# Analyze entity types\n", - "entity_types = {}\n", - "for entity in entities:\n", - " entity_type = entity.get(\"type\", \"Unknown\")\n", - " entity_types[entity_type] = entity_types.get(entity_type, 0) + 1\n", - "\n", - "print(\"\\nEntity types:\")\n", - "for entity_type, count in sorted(\n", - " entity_types.items(), key=lambda x: x[1], reverse=True\n", - "):\n", - " print(f\" {entity_type}: {count}\")\n", - "\n", - "# Analyze relationships\n", - "entities_with_relationships = [\n", - " entity for entity in entities if entity.get(\"relationships\", {}).get(\"target_ids\")\n", - "]\n", - "\n", - "total_relationships = sum(\n", - " len(entity.get(\"relationships\", {}).get(\"target_ids\", []))\n", - " for entity in entities_with_relationships\n", - ")\n", - "\n", - "print(\"\\nRelationships:\")\n", - "print(f\" Entities with relationships: {len(entities_with_relationships)}\")\n", - "print(f\" Total relationship edges: {total_relationships}\")\n", - "print(\n", - " f\" Average relationships per connected entity: {total_relationships / len(entities_with_relationships):.1f}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "sample-entities-section", - "metadata": {}, - "source": [ - "## Sample Entities\n", - "\n", - "Display a few example entities from the graph." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "show-samples", - "metadata": {}, - "outputs": [], - "source": "# Show some sample entities with relationships\nprint(\"Sample entities with relationships:\\n\")\nfor i, entity in enumerate(entities_with_relationships[:5]):\n print(f\"{i+1}. {entity['_id']} ({entity.get('type')})\")\n relationships = entity.get(\"relationships\", {})\n target_ids = relationships.get(\"target_ids\", [])\n rel_types = relationships.get(\"types\", [])\n print(f\" Relationships: {len(target_ids)}\")\n for target, rel_type in zip(target_ids[:3], rel_types[:3]):\n print(f\" - {rel_type} -> {target}\")\n if len(target_ids) > 3:\n print(f\" ... and {len(target_ids) - 3} more\")\n print()" - }, - { - "cell_type": "markdown", - "id": "query-section", - "metadata": {}, - "source": [ - "## Query the Knowledge Graph\n", - "\n", - "Test querying the graph to find related entities." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "query-graph", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracted entities from query: ['Quantum Dynamics Corp', 'NanoTech Materials Ltd']\n", - "\n", - "Found 5 related entities:\n", - " - Quantum Processor (Technology)\n", - " - Dr. Michael Torres (Person)\n", - " - Quantum Dynamics Corp (Organization)\n", - " - NanoTech Materials Ltd. (Organization)\n", - " - QX-7000 (Product)\n" - ] - } - ], - "source": [ - "# Example query about relationships\n", - "query = (\n", - " \"What is the connection between Quantum Dynamics Corp and NanoTech Materials Ltd?\"\n", - ")\n", - "\n", - "# Extract entity names from query\n", - "entity_names = graph_store.extract_entity_names(query)\n", - "print(f\"Extracted entities from query: {entity_names}\")\n", - "\n", - "# Find related entities through graph traversal\n", - "if entity_names:\n", - " related_entities = graph_store.related_entities(entity_names)\n", - " print(f\"\\nFound {len(related_entities)} related entities:\")\n", - " for entity in related_entities[:10]:\n", - " print(f\" - {entity['_id']} ({entity.get('type')})\")\n", - " if len(related_entities) > 10:\n", - " print(f\" ... and {len(related_entities) - 10} more\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "chat-response", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chat Response:\n", - "Quantum Dynamics Corp and NanoTech Materials Ltd. are connected through a partnership, with NanoTech Materials Ltd. providing superconducting materials to Quantum Dynamics Corp.\n" - ] - } - ], - "source": [ - "# Get a natural language response using the knowledge graph\n", - "answer = graph_store.chat_response(query)\n", - "print(f\"Chat Response:\\n{answer.content}\")" - ] - }, - { - "cell_type": "markdown", - "id": "viz-section", - "metadata": {}, - "source": [ - "## Visualize Knowledge Graph\n", - "\n", - "Create interactive visualizations of the knowledge graph using HoloViews and NetworkX.\n", - "\n", - "### Basic Graph View\n", - "\n", - "First, we'll create a basic visualization of the entire graph." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "basic-view", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - 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\n", - "" - ], - "text/plain": [ - ":Overlay\n", - " .Graph.I :Graph [start,end] (attributes,type)\n", - " .Nodes.I :Nodes [x,y,index] (attributes,type)" - ] - }, - "execution_count": 13, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "ace31b53-6216-459e-9cd7-5669144aebff" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# Create basic view of the entire graph\n", - "basic_view = graph_store.view()\n", - "basic_view" - ] - }, - { - "cell_type": "markdown", - "id": "networkx-section", - "metadata": {}, - "source": [ - "### NetworkX Graph Layouts\n", - "\n", - "Convert to NetworkX for advanced layout algorithms and analytics." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "to-networkx", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NetworkX graph: 181 nodes, 212 edges\n", - "Graph density: 0.0065\n", - "Is connected: False\n", - "Number of connected components: 23\n", - "Largest component size: 16\n" - ] - } - ], - "source": [ - "# Convert to NetworkX graph\n", - "nx_graph = graph_store.to_networkx()\n", - "\n", - "print(\n", - " f\"NetworkX graph: {nx_graph.number_of_nodes()} nodes, {nx_graph.number_of_edges()} edges\"\n", - ")\n", - "print(f\"Graph density: {nx.density(nx_graph):.4f}\")\n", - "\n", - "# Check if graph is connected\n", - "if nx_graph.number_of_nodes() > 0:\n", - " is_connected = nx.is_connected(nx_graph.to_undirected())\n", - " print(f\"Is connected: {is_connected}\")\n", - "\n", - " if not is_connected:\n", - " components = list(nx.connected_components(nx_graph.to_undirected()))\n", - " print(f\"Number of connected components: {len(components)}\")\n", - " print(f\"Largest component size: {len(max(components, key=len))}\")" - ] - }, - { - "cell_type": "markdown", - "id": "multipartite-section", - "metadata": {}, - "source": [ - "### Multipartite Layout (by Entity Type)\n", - "\n", - "Visualize the graph with entities grouped by type." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "multipartite-view", - "metadata": {}, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "" - ], - "text/plain": [ - ":Overlay\n", - " .Graph.I :Graph [start,end] (attributes,type)\n", - " .Nodes.I :Nodes [x,y,index] (attributes,type)" - ] - }, - "execution_count": 15, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "72e27789-896e-4b17-b03e-e82812da0519" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# Create multipartite layout grouping entities by type\n", - "by_type = hv.Graph.from_networkx(nx_graph, nx.multipartite_layout, subset_key=\"type\")\n", - "type_view = by_type.opts(\n", - " inspection_policy=\"edges\",\n", - " tools=[\"hover\", \"tap\"],\n", - " node_color=\"type\",\n", - " cmap=\"Category20\",\n", - " node_size=10,\n", - " edge_line_width=1,\n", - " edge_alpha=0.5,\n", - ") * by_type.nodes.opts(\n", - " size=10,\n", - " color=\"type\",\n", - " cmap=\"Category20\",\n", - " alpha=0.8,\n", - ")\n", - "\n", - "type_view" - ] - }, - { - "cell_type": "markdown", - "id": "spring-section", - "metadata": {}, - "source": [ - "### Spring Layout\n", - "\n", - "Use a force-directed layout for organic graph visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "spring-view", - "metadata": {}, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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"### Focused Subgraph Visualization\n", - "\n", - "Visualize a subgraph around specific entities for detailed exploration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "subgraph-view", - "metadata": {}, - "outputs": [], - "source": "# Pick an entity with many connections\nif entities_with_relationships:\n # Find entity with most relationships\n most_connected = max(\n entities_with_relationships,\n key=lambda e: len(e.get(\"relationships\", {}).get(\"target_ids\", [])),\n )\n\n print(\n f\"Most connected entity: {most_connected['_id']} ({most_connected.get('type')})\"\n )\n print(\n f\"Number of relationships: {len(most_connected.get('relationships', {}).get('target_ids', []))}\"\n )\n\n # Get subgraph around this entity\n focus_entity = most_connected[\"_id\"]\n related = graph_store.related_entities([focus_entity], max_depth=2)\n\n print(f\"\\nSubgraph contains {len(related)} entities\")\n\n # Create a subgraph from these entities\n subgraph_nodes = {entity[\"_id\"] for entity in related}\n subgraph = nx_graph.subgraph(subgraph_nodes).copy()\n\n print(\n f\"Subgraph: {subgraph.number_of_nodes()} nodes, {subgraph.number_of_edges()} edges\"\n )\n\n # Visualize subgraph\n if subgraph.number_of_nodes() > 0:\n sub_spring = hv.Graph.from_networkx(\n subgraph, nx.spring_layout, k=0.8, iterations=50\n )\n subgraph_view = sub_spring.opts(\n inspection_policy=\"edges\",\n tools=[\"hover\", \"tap\"],\n node_color=\"type\",\n cmap=\"Category20\",\n node_size=15,\n edge_line_width=2,\n edge_alpha=0.6,\n title=f\"Subgraph around {focus_entity}\",\n ) * sub_spring.nodes.opts(\n size=15,\n color=\"type\",\n cmap=\"Category20\",\n alpha=0.9,\n )\n\n# Display the subgraph view\nsubgraph_view # noqa: F821" - }, - { - "cell_type": "markdown", - "id": "export-section", - "metadata": {}, - "source": [ - "## Export Visualizations\n", - "\n", - "Save the visualizations as HTML files for sharing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "export-viz", - "metadata": {}, - "outputs": [], - "source": [ - "# Save visualizations\n", - "hv.save(type_view, \"graph_by_type.html\")\n", - "print(\"Saved graph_by_type.html\")\n", - "\n", - "hv.save(spring_view, \"graph_spring.html\")\n", - "print(\"Saved graph_spring.html\")\n", - "\n", - "print(\"\\nVisualizations exported successfully!\")" - ] - }, - { - "cell_type": "markdown", - "id": "summary-section", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In this notebook, we:\n", - "\n", - "1. ✅ Loaded a large text corpus with ~100 entities\n", - "2. ✅ Chunked text into documents using `RecursiveCharacterTextSplitter`\n", - "3. ✅ Built a knowledge graph using `MongoDBGraphStore` and LLM-based entity extraction\n", - "4. ✅ Analyzed graph statistics (entities, types, relationships)\n", - "5. ✅ Queried the graph for related entities and chat responses\n", - "6. ✅ Visualized the graph using multiple layout algorithms\n", - "7. ✅ Explored focused subgraphs around highly-connected entities\n", - "8. ✅ Exported interactive visualizations as HTML\n", - "\n", - "The resulting knowledge graph can be used for:\n", - "- Complex multi-hop queries\n", - "- Entity relationship discovery\n", - "- Context-aware chat responses\n", - "- Graph analytics and network analysis" - ] - } - ], - "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": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/libs/langchain-mongodb/examples/GraphRAG.ipynb b/libs/langchain-mongodb/examples/GraphRAG.ipynb index 3aace235..a8e1e56b 100644 --- a/libs/langchain-mongodb/examples/GraphRAG.ipynb +++ b/libs/langchain-mongodb/examples/GraphRAG.ipynb @@ -5,19 +5,22 @@ "id": "62c5b493-9b7d-45dc-be3b-9ea1c77ad7d8", "metadata": {}, "source": [ - "# Knowledge Graphs via MongoDBGraphStore\n", + "# Large-Scale Knowledge Graph Construction and Visualization\n", "\n", - "GraphRAG is augments a ChatModel with information retrieved\n", - "based on a Knowledge Graph that an LLM is used to create.\n", + "This notebook demonstrates building a knowledge graph from a text corpus containing approximately 100 entities.\n", "\n", - "Our Knowledge Graph `MongoDBGraphStore` is based on a collection of entities, each holding attributes and relationships to other entities\n", + "We will:\n", + "1. Load a large text dataset about technology companies, people, and projects\n", + "2. Chunk the text into paragraph-sized documents using `RecursiveCharacterTextSplitter`\n", + "3. Extract entities and relationships using `MongoDBGraphStore`\n", + "4. Visualize the resulting knowledge graph using HoloViews and NetworkX\n", "\n", - "* One can use `similarity_search`, and `related_entities`, to use the MongoDB Collection as a Graph Database via `$graphLookup`.\n", - "* `chat_response` provides a simple interface to ask questions of an LLM informed with results of `$graphLookup`\n", - "* `view`: This notebook highlights the `view` method, which adds the ability to visualize the Knowledge Graph using [Holoview's Network Graph](https://holoviews.org/user_guide/Network_Graphs.html) built upon the using dynamic plotting library [bokeh](https://bokeh.org/).\n", - "* `to_networkx`: We also point out that we use [NetworkX](https://networkx.org/) under the hood. This representation may be very useful for Graph Analytics, and as a starting point for one's own preferred visualization library.\n", - "\n", - "Note that both HoloViews and NetworkX are extensible. We encourage you to play with further customization. Some extensions wil require additional packages." + "The dataset contains news-style articles about:\n", + "- Organizations (tech companies, research institutions, nonprofits)\n", + "- People (CEOs, scientists, directors)\n", + "- Projects and initiatives\n", + "- Locations and facilities\n", + "- Technologies and products" ] }, { @@ -28,10 +31,14 @@ "outputs": [], "source": [ "import os\n", + "from pathlib import Path\n", "\n", "import holoviews as hv\n", "import networkx as nx\n", "from holoviews import opts\n", + "from langchain_core.documents import Document\n", + "from langchain_openai import ChatOpenAI\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "from langchain_mongodb.graphrag.graph import MongoDBGraphStore" ] @@ -587,12 +594,12 @@ "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ - "
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\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ⓘ\n", + "
\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Graph.I :Graph [start,end] (attributes,type)\n", + " .Nodes.I :Nodes [x,y,index] (attributes,type)" + ] + }, + "execution_count": 15, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "310d70ed-c4ea-41c0-860b-cad3f982e509" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "spring_view = graph_store.view(\n", + " layout=nx.spring_layout,\n", + " nx_opts=dict(k=0.5, iterations=100),\n", + " edge_opts=dict(\n", + " edge_line_width=0.5,\n", + " node_color=\"type\",\n", + " cmap=\"Category20\",\n", + " node_size=10,\n", + " ), #\n", + " node_opts=dict(size=10, color=\"type\", cmap=\"Category20\", alpha=0.8),\n", + ")\n", + "spring_view" + ] + }, + { + "cell_type": "markdown", + "id": "multipartite-section", + "metadata": {}, + "source": [ + "### Multipartite Layout (by Entity Type)\n", + "\n", + "NetworkX has many different layouts available. Here we visualize the graph with entities grouped by type." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b88adefb-34c0-402f-84f1-24ce06a71fb6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.8.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.0.min.js\", \"https://cdn.holoviz.org/panel/1.8.2/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, 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document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + 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+ + + + + From dcd836dfa6d208a7dc4b342b19795f421ca52576 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Fri, 10 Oct 2025 16:45:24 -0400 Subject: [PATCH 27/37] INTPYTHON-787 Added check for AZURE_OPENAI_ENDPOINT test_graphrag.py and cleaned up closing clients (#232) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [Issue Key](https://jira.mongodb.org/browse/{ISSUE_KEY}) This is a small bugfix. When we switched from including OPENAI_API_KEY in CI secrets to using AZURE_OPENAI_ENDPOINTS, we missed this one. - Tests are run in github and evergreen. A passing patch build will be included. - [ na] Did you update the changelog (if necessary)? - [X] Is the intention of the code captured in relevant tests? - [na] If there are new TODOs, has a related JIRA ticket been created? - [X] Has a MongoDB Employee run [the patch build of this PR](https://github.com/mongodb-labs/ai-ml-pipeline-testing?tab=readme-ov-file#running-a-patch-build-of-a-given-pr)? - [x] Does the title of the PR reference a JIRA Ticket? - [x] Do you fully understand the implementation? (Would you be comfortable explaining how this code works to someone else?) - [x] Have you checked for spelling & grammar errors? - [x] Is all relevant documentation (README or docstring) updated? The author had thought of updating MongoDBGraphStore to be a context manager. All this would do is close the client. We decided to leave it with a close method only. --- .../tests/integration_tests/test_graphrag.py | 41 ++++++------------- 1 file changed, 13 insertions(+), 28 deletions(-) diff --git a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py index e1e0496d..cc653f40 100644 --- a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py +++ b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py @@ -201,27 +201,13 @@ def test_additional_entity_examples(entity_extraction_model, entity_example, doc assert len(new_entities) >= 2 -def test_chat_response(graph_store, query_connection): - """Displays querying an existing Knowledge Graph Database""" - answer = graph_store.chat_response(query_connection) - assert isinstance(answer, AIMessage) - assert "acme corporation" in answer.content.lower() - - -def test_similarity_search(graph_store, query_connection): - docs = graph_store.similarity_search(query_connection) - assert len(docs) >= 4 - assert all({"_id", "type", "relationships"}.issubset(set(d.keys())) for d in docs) - assert any("depth" in d.keys() for d in docs) - assert any("attributes" in d.keys() for d in docs) - - def test_validator(documents, entity_extraction_model): # Case 1. No existing collection. - client = MongoClient(CONNECTION_STRING) clxn_name = f"{COLLECTION_NAME}_validation" - client[DB_NAME][clxn_name].drop() - client.close() + + with MongoClient(CONNECTION_STRING) as client: + client[DB_NAME][clxn_name].drop() + # now we call with validation that can be added without db admin privileges store = MongoDBGraphStore( connection_string=CONNECTION_STRING, @@ -243,16 +229,15 @@ def test_validator(documents, entity_extraction_model): collection = client[DB_NAME][clxn_name] collection.delete_many({}) - store = MongoDBGraphStore( - collection=collection, - entity_extraction_model=entity_extraction_model, - validate=True, - validation_action="error", - ) - bulkwrite_results = store.add_documents(documents) - assert len(bulkwrite_results) == len(documents) - collection.drop() - store.close() + store = MongoDBGraphStore( + collection=collection, + entity_extraction_model=entity_extraction_model, + validate=True, + validation_action="error", + ) + bulkwrite_results = store.add_documents(documents) + assert len(bulkwrite_results) == len(documents) + collection.delete_many({}) # Case 3: Existing collection without a validator with MongoClient(CONNECTION_STRING) as client: From e3423aaedeefbb13cb194210b85636efe97b60f6 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 13 Oct 2025 15:32:45 -0400 Subject: [PATCH 28/37] Added basic tests of to_networkx and view. Added pytest mark called viz --- libs/langchain-mongodb/pyproject.toml | 1 + .../tests/integration_tests/test_graphrag.py | 43 ++++++++++++++++++- 2 files changed, 42 insertions(+), 2 deletions(-) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index 8f35bd68..bc1e2f09 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -61,6 +61,7 @@ xfail_strict = true markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them", + "viz: marks tests as requiring optional-dependency group `viz`", ] asyncio_mode = "auto" asyncio_default_fixture_loop_scope = "function" diff --git a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py index cc653f40..4476f8f0 100644 --- a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py +++ b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py @@ -1,12 +1,11 @@ from __future__ import annotations import os -from typing import Generator +from typing import TYPE_CHECKING, Generator import pytest from langchain_core.documents import Document from langchain_core.language_models.chat_models import BaseChatModel -from langchain_core.messages import AIMessage from langchain_openai import AzureChatOpenAI, ChatOpenAI from pymongo import MongoClient from pymongo.collection import Collection @@ -17,6 +16,10 @@ from ..utils import CONNECTION_STRING, DB_NAME +if TYPE_CHECKING: + pass + + COLLECTION_NAME = "langchain_test_graphrag" @@ -303,3 +306,39 @@ def test_allowed_relationship_types(documents, entity_extraction_model): relationships.update(set(ent.get("relationships", {}).get("types", []))) assert relationships == {"partner"} store.close() + + +@pytest.mark.viz +def test_networkx(graph_store): + """Basic test that MongoDBGraphStore can be exported to NetworkX Graph. + + See examples for self-contained Jupyter notebook example. + To run: `pytest -m viz` + """ + try: + import networkx as nx + except ImportError: + pytest.skip("This test requires optional-dependency `viz`") + + nx_graph = graph_store.to_networkx() + # assert nx_graph.number_of_nodes() == len(nx_graph.nodes) + assert isinstance(nx_graph, nx.DiGraph) + assert nx_graph.number_of_nodes() == graph_store.collection.count_documents({}) + assert nx_graph.number_of_edges() > 0 + + +@pytest.mark.viz +def test_view(graph_store): + """Basic test that MongoDBGraphStore can be exported to NetworkX Graph. + + See examples for self-contained Jupyter notebook example. + To run: `pytest -m viz` + """ + try: + import holoviews as hv + except ImportError: + pytest.skip("This test requires optional-dependency `viz`") + + view = graph_store.view() + assert isinstance(view, hv.Overlay) + assert len(view) == 2 From 5c3bfed86bf72d6df3e47566d771e63f949d1320 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 13 Oct 2025 15:58:53 -0400 Subject: [PATCH 29/37] Removed test_graph_large, moved its data to examples, removed html there, and cleared output of cells. --- .../langchain-mongodb/examples/GraphRAG.ipynb | 3643 +---------------- .../data/articles.txt} | 0 .../examples/spring_view.html | 68 - .../integration_tests/test_graph_large.py | 269 -- 4 files changed, 41 insertions(+), 3939 deletions(-) rename libs/langchain-mongodb/{tests/integration_tests/data/large_graph_dataset.txt => examples/data/articles.txt} (100%) delete mode 100644 libs/langchain-mongodb/examples/spring_view.html delete mode 100644 libs/langchain-mongodb/tests/integration_tests/test_graph_large.py diff --git a/libs/langchain-mongodb/examples/GraphRAG.ipynb b/libs/langchain-mongodb/examples/GraphRAG.ipynb index a8e1e56b..5b4b7b37 100644 --- a/libs/langchain-mongodb/examples/GraphRAG.ipynb +++ b/libs/langchain-mongodb/examples/GraphRAG.ipynb @@ -5,7 +5,7 @@ "id": "62c5b493-9b7d-45dc-be3b-9ea1c77ad7d8", "metadata": {}, "source": [ - "# Large-Scale Knowledge Graph Construction and Visualization\n", + "# Knowledge Graph Construction, querying, and Visualization\n", "\n", "This notebook demonstrates building a knowledge graph from a text corpus containing approximately 100 entities.\n", "\n", @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "3f3fb616-c1c1-4f82-b093-ad3f37a728e6", "metadata": {}, "outputs": [], @@ -45,746 +45,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "5474a3a4-fcd6-4acb-b01e-2f3afe7ff549", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = 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\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "hv.extension(\"bokeh\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "52fc6635-87e9-4179-b6a4-404c988904c5", "metadata": {}, "outputs": [], @@ -798,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "4fa9f1e9-48b8-42b2-83fb-3cd374c678ca", "metadata": {}, "outputs": [], @@ -821,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "69026170-5d6d-43a1-9fbb-9a52461e1e0b", "metadata": {}, "outputs": [], @@ -849,25 +120,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "load-data", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded text file with 13764 characters\n", - "Preview:\n", - "TechVision 2024 Global Conference Opens in Austin\n", - "\n", - "Austin, TX — The TechVision 2024 Global Conference kicked off on March 15th at the Austin Convention Center, drawing over 5,000 attendees from around the world. The conference, organized by Global Tech Events Inc., focuses on emerging technologies i...\n" - ] - } - ], + "outputs": [], "source": [ "# Load the text data\n", - "data_file = Path(\"../tests/integration_tests/data/large_graph_dataset.txt\")\n", + "data_file = Path(\"data/articles.txt\")\n", "text_content = data_file.read_text()\n", "\n", "print(f\"Loaded text file with {len(text_content)} characters\")\n", @@ -876,19 +135,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "chunk-text", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Split text into 20 chunks\n", - "Created 20 Document objects\n" - ] - } - ], + "outputs": [], "source": [ "# Split text into chunks using RecursiveCharacterTextSplitter\n", "# This creates natural document boundaries at paragraph breaks\n", @@ -921,18 +171,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "create-graph-store", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created MongoDBGraphStore connected to langchain_test_db.langchain_graphrag_large_example\n" - ] - } - ], + "outputs": [], "source": [ "# Create the graph store\n", "graph_store = MongoDBGraphStore(\n", @@ -948,33 +190,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "extract-entities", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing 20 documents...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Targets, types, and attributes do not have the same length for Renewable Energy Tech Summit!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processed 20 document chunks\n", - "Entity extraction complete!\n" - ] - } - ], + "outputs": [], "source": [ "# Extract entities from documents\n", "# This may take 1-2 minutes depending on the number of chunks and LLM response time\n", @@ -997,34 +216,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "graph-stats", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total entities extracted: 185\n", - "\n", - "Entity types:\n", - " Organization: 126\n", - " Person: 36\n", - " Event: 6\n", - " Project: 5\n", - " Location: 5\n", - " Product: 3\n", - " Platform: 2\n", - " Technology: 1\n", - " Concept: 1\n", - "\n", - "Relationships:\n", - " Entities with relationships: 83\n", - " Total relationship edges: 377\n", - " Average relationships per connected entity: 4.5\n" - ] - } - ], + "outputs": [], "source": [ "# Count entities\n", "entity_count = graph_store.collection.count_documents({})\n", @@ -1075,47 +270,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "show-samples", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample entities with relationships:\n", - "\n", - "1. TechVision 2024 Global Conference (Event)\n", - " Relationships: 4\n", - " - Organized By -> Global Tech Events Inc.\n", - " - Speaker -> Dr. Sarah Chen\n", - " - Organized By -> Global Tech Events Inc.\n", - " ... and 1 more\n", - "\n", - "2. Dr. Sarah Chen (Person)\n", - " Relationships: 2\n", - " - Speaker -> TechVision 2024 Global Conference\n", - " - CEO -> Global Tech Events Inc.\n", - "\n", - "3. Quantum Dynamics Corp (Organization)\n", - " Relationships: 8\n", - " - Developed -> Quantum Processor\n", - " - Developed -> QX-7000\n", - " - Employee -> Dr. Michael Torres\n", - " ... and 5 more\n", - "\n", - "4. QX-7000 (Product)\n", - " Relationships: 1\n", - " - Launched At -> TechVision Conference\n", - "\n", - "5. Dr. Michael Torres (Person)\n", - " Relationships: 2\n", - " - Demonstrated -> QX-7000\n", - " - Employee -> Quantum Dynamics Corp\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Show some sample entities with relationships\n", "print(\"Sample entities with relationships:\\n\")\n", @@ -1144,26 +302,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "query-graph", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracted entities from query: ['Quantum Dynamics Corp', 'NanoTech Materials Ltd']\n", - "\n", - "Found 6 related entities:\n", - " - QX-7000 (Product)\n", - " - TechVision Conference (Event)\n", - " - Quantum Dynamics Corp (Organization)\n", - " - Dr. Michael Torres (Person)\n", - " - NanoTech Materials Ltd. (Organization)\n", - " - Quantum Processor (Technology)\n" - ] - } - ], + "outputs": [], "source": [ "# Example query about relationships\n", "query = (\n", @@ -1186,19 +328,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "chat-response", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chat Response:\n", - "Quantum Dynamics Corp and NanoTech Materials Ltd. are connected through a partnership, with NanoTech Materials Ltd. supplying specialized superconducting materials to Quantum Dynamics Corp.\n" - ] - } - ], + "outputs": [], "source": [ "# Get a natural language response using the knowledge graph\n", "answer = graph_store.chat_response(query)\n", @@ -1221,833 +354,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "basic-view", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - 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initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && 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- - - - - diff --git a/libs/langchain-mongodb/tests/integration_tests/test_graph_large.py b/libs/langchain-mongodb/tests/integration_tests/test_graph_large.py deleted file mode 100644 index ebf956a8..00000000 --- a/libs/langchain-mongodb/tests/integration_tests/test_graph_large.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Test large knowledge graph construction from a substantial text corpus. - -This test demonstrates building a knowledge graph from a text file containing -approximately 100 entities across multiple organizations, people, projects, and locations. -The text is chunked by paragraph using RecursiveCharacterTextSplitter to create -documents suitable for entity extraction. -""" - -from __future__ import annotations - -import os -from pathlib import Path -from typing import Generator - -import pytest -from langchain_core.documents import Document -from langchain_core.language_models.chat_models import BaseChatModel -from langchain_core.messages import AIMessage -from langchain_openai import AzureChatOpenAI, ChatOpenAI -from langchain_text_splitters import RecursiveCharacterTextSplitter -from pymongo import MongoClient -from pymongo.collection import Collection - -from langchain_mongodb.graphrag.graph import MongoDBGraphStore - -from ..utils import CONNECTION_STRING, DB_NAME - -COLLECTION_NAME = "langchain_test_graphrag_large" - - -@pytest.fixture(scope="module") -def collection() -> Generator[Collection, None, None]: - """Create a fresh MongoDB collection for testing.""" - client = MongoClient(CONNECTION_STRING) - db = client[DB_NAME] - db[COLLECTION_NAME].drop() - collection = db.create_collection(COLLECTION_NAME) - yield collection - client.close() - - -if not ("OPENAI_API_KEY" in os.environ or "AZURE_OPENAI_ENDPOINT" in os.environ): - pytest.skip( - "GraphRAG tests require OpenAI for chat responses.", allow_module_level=True - ) - - -@pytest.fixture(scope="module") -def entity_extraction_model() -> BaseChatModel | None: - """LLM for converting documents into Graph of Entities and Relationships.""" - try: - if "AZURE_OPENAI_ENDPOINT" in os.environ: - return AzureChatOpenAI( - model="gpt-4o", temperature=0.0, cache=False, seed=12345 - ) - return ChatOpenAI(model="gpt-4o", temperature=0.0, cache=False, seed=12345) - except Exception: - pass - return None - - -@pytest.fixture(scope="module") -def large_text_content() -> str: - """Load the large text dataset from file.""" - test_dir = Path(__file__).parent - data_file = test_dir / "data" / "large_graph_dataset.txt" - return data_file.read_text() - - -@pytest.fixture(scope="module") -def text_chunks(large_text_content: str) -> list[str]: - """Split text into paragraph-sized chunks using RecursiveCharacterTextSplitter.""" - # Split by double newlines (paragraphs) with some overlap - # This creates natural document boundaries at paragraph breaks - text_splitter = RecursiveCharacterTextSplitter( - separators=["\n\n", "\n", " ", ""], - chunk_size=1000, - chunk_overlap=100, - length_function=len, - ) - chunks = text_splitter.split_text(large_text_content) - return chunks - - -@pytest.fixture(scope="module") -def graph_store( - collection: Collection, - entity_extraction_model: BaseChatModel, - text_chunks: list[str], -) -> Generator[MongoDBGraphStore, None, None]: - """Create MongoDBGraphStore and populate it with entities from text chunks.""" - # Create the graph store - store = MongoDBGraphStore( - collection=collection, - entity_extraction_model=entity_extraction_model, - max_depth=3, # Allow deeper graph traversal for complex queries - ) - - # Convert text chunks to Document objects - documents = [Document(page_content=chunk) for chunk in text_chunks] - - # Add documents and extract entities - bulkwrite_results = store.add_documents(documents) - - # Verify that we processed all chunks - assert len(bulkwrite_results) == len(documents) - - yield store - store.close() - - -def test_large_graph_entity_count(graph_store: MongoDBGraphStore): - """Verify that the large graph contains more than 50 entities. - - The dataset is designed to generate approximately 100 entities including: - - Organizations (companies, nonprofits, research institutions) - - People (CEOs, scientists, directors) - - Projects and initiatives - - Locations (cities, facilities) - - Technologies and products - """ - entity_count = graph_store.collection.count_documents({}) - print(f"\nExtracted {entity_count} entities from the text corpus") - - # Verify we have a substantial knowledge graph - assert entity_count > 50, ( - f"Expected more than 50 entities, but found {entity_count}. " - "The entity extraction may need adjustment." - ) - - -def test_large_graph_entity_types(graph_store: MongoDBGraphStore): - """Verify diverse entity types are extracted.""" - entities = list(graph_store.collection.find({})) - - # Collect all entity types - entity_types = {entity.get("type") for entity in entities} - - print(f"\nFound entity types: {entity_types}") - - # We expect at least Organization and Person types - assert len(entity_types) >= 2, "Expected at least 2 different entity types" - - -def test_large_graph_relationships(graph_store: MongoDBGraphStore): - """Verify that entities have relationships connecting them.""" - entities = list(graph_store.collection.find({})) - - # Count entities with relationships - entities_with_relationships = [ - entity - for entity in entities - if entity.get("relationships", {}).get("target_ids") - ] - - relationship_percentage = ( - len(entities_with_relationships) / len(entities) * 100 if entities else 0 - ) - - print( - f"\n{len(entities_with_relationships)} out of {len(entities)} entities " - f"({relationship_percentage:.1f}%) have relationships" - ) - - # At least 30% of entities should have relationships - assert ( - len(entities_with_relationships) >= len(entities) * 0.3 - ), "Expected at least 30% of entities to have relationships" - - -def test_large_graph_sample_queries(graph_store: MongoDBGraphStore): - """Test sample queries on the large knowledge graph.""" - # Query about relationships between entities - query = "What is the connection between Quantum Dynamics Corp and NanoTech Materials Ltd?" - - # Extract entity names from query - entity_names = graph_store.extract_entity_names(query) - print(f"\nExtracted entities from query: {entity_names}") - - # Find related entities - related_entities = graph_store.related_entities(entity_names) - print(f"Found {len(related_entities)} related entities") - - # Should find some related entities - assert len(related_entities) >= 1, "Expected to find related entities" - - -def test_large_graph_similarity_search(graph_store: MongoDBGraphStore): - """Test similarity search functionality on the large graph.""" - # Use a query that includes actual entity names from the dataset - query = ( - "Tell me about SolarTech Industries and their work with " - "the Green Computing Alliance on renewable energy." - ) - - docs = graph_store.similarity_search(query) - - print(f"\nSimilarity search returned {len(docs)} entities") - - # Should return relevant entities if the entity names were extracted - # If entity extraction finds the names, we should get results - if len(docs) > 0: - # Verify structure of returned documents - for doc in docs[:3]: # Check first 3 - assert "_id" in doc - assert "type" in doc - assert len(docs) >= 2, "Expected similarity search to return multiple entities" - else: - # If no entities were extracted from query, that's ok too - # This can happen if the LLM doesn't recognize the entity names - print("Note: No entities extracted from query - this is acceptable") - - -def test_large_graph_multi_hop_query(graph_store: MongoDBGraphStore): - """Test multi-hop graph traversal to answer complex queries.""" - # This query includes specific entity names from the dataset - query = ( - "What are the connections between MediChain Solutions, " - "HealthSecure Systems, and the Blockchain Healthcare Consortium?" - ) - - entity_names = graph_store.extract_entity_names(query) - - print(f"\nExtracted entity names: {entity_names}") - - # Even if we start with few entities, graph traversal should find more - if len(entity_names) > 0: - related_entities = graph_store.related_entities(entity_names, max_depth=3) - - print( - f"Multi-hop traversal from {len(entity_names)} starting entities " - f"found {len(related_entities)} related entities" - ) - - # Multi-hop traversal should expand the result set - assert ( - len(related_entities) >= 1 - ), "Expected multi-hop traversal to find at least the starting entities" - else: - # If no entities extracted, test that we can still query the graph directly - # Pick some entities from the database - sample_entities = list(graph_store.collection.find({}, {"_id": 1}).limit(3)) - if sample_entities: - entity_ids = [e["_id"] for e in sample_entities] - print(f"Testing with sample entities: {entity_ids}") - related = graph_store.related_entities(entity_ids, max_depth=2) - assert len(related) >= 1, "Expected to find entities in graph traversal" - - -def test_chunk_count(text_chunks: list[str]): - """Verify that text was properly chunked.""" - assert len(text_chunks) >= 10, ( - f"Expected at least 10 chunks, got {len(text_chunks)}. " - "Text splitting may need adjustment." - ) - print(f"\nText split into {len(text_chunks)} chunks") - - -def test_graph_store_chat_response(graph_store: MongoDBGraphStore): - """Test the chat response functionality with the large graph.""" - query = ( - "Tell me about the AI Ethics Initiative and which companies are supporting it." - ) - - answer = graph_store.chat_response(query) - - assert isinstance(answer, AIMessage) - assert len(answer.content) > 0, "Expected a non-empty response" - print(f"\nChat response preview: {answer.content[:200]}...") From 2986e260e689c784831db465d05d4d4be31312e8 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Fri, 17 Oct 2025 16:07:00 -0400 Subject: [PATCH 30/37] Updated capitalization in title. --- libs/langchain-mongodb/examples/GraphRAG.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langchain-mongodb/examples/GraphRAG.ipynb b/libs/langchain-mongodb/examples/GraphRAG.ipynb index 5b4b7b37..f33361ad 100644 --- a/libs/langchain-mongodb/examples/GraphRAG.ipynb +++ b/libs/langchain-mongodb/examples/GraphRAG.ipynb @@ -5,7 +5,7 @@ "id": "62c5b493-9b7d-45dc-be3b-9ea1c77ad7d8", "metadata": {}, "source": [ - "# Knowledge Graph Construction, querying, and Visualization\n", + "# Knowledge Graph Construction, Querying, and Visualization\n", "\n", "This notebook demonstrates building a knowledge graph from a text corpus containing approximately 100 entities.\n", "\n", From c4f54a06f87947c626f71e96fa681d8bfc5d23cf Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 20 Oct 2025 16:11:29 -0400 Subject: [PATCH 31/37] Fixed up details broken in rebase --- .../tests/integration_tests/test_graphrag.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py index 4476f8f0..b10e5a1a 100644 --- a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py +++ b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py @@ -1,11 +1,12 @@ from __future__ import annotations import os -from typing import TYPE_CHECKING, Generator +from typing import Generator import pytest from langchain_core.documents import Document from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import AIMessage from langchain_openai import AzureChatOpenAI, ChatOpenAI from pymongo import MongoClient from pymongo.collection import Collection @@ -16,10 +17,6 @@ from ..utils import CONNECTION_STRING, DB_NAME -if TYPE_CHECKING: - pass - - COLLECTION_NAME = "langchain_test_graphrag" @@ -335,7 +332,7 @@ def test_view(graph_store): To run: `pytest -m viz` """ try: - import holoviews as hv + import holoviews as hv # type: ignore except ImportError: pytest.skip("This test requires optional-dependency `viz`") From 26f7ba70fec18e438686055076cf72324a7e86e9 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 20 Oct 2025 16:50:21 -0400 Subject: [PATCH 32/37] Remove jupyter and pyparsing from viz dep group --- libs/langchain-mongodb/pyproject.toml | 2 - libs/langchain-mongodb/uv.lock | 1355 +------------------------ 2 files changed, 13 insertions(+), 1344 deletions(-) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index bc1e2f09..19dacc0b 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -48,8 +48,6 @@ dev = [ viz = [ "networkx>=3.0", "holoviews>=1.19", - "jupyter>=1.1", - "pyparsing>=3.1", ] [tool.pytest.ini_options] diff --git a/libs/langchain-mongodb/uv.lock b/libs/langchain-mongodb/uv.lock index a6829c4d..b064e970 100644 --- a/libs/langchain-mongodb/uv.lock +++ b/libs/langchain-mongodb/uv.lock @@ -199,97 +199,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" }, ] -[[package]] -name = "appnope" -version = "0.1.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } -wheels = [ - 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[[package]] name = "wrapt" version = "1.17.3" From c29429e61882b0aa28b5a29431432aedb7afb0ce Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 20 Oct 2025 17:36:02 -0400 Subject: [PATCH 33/37] Updated docstrings of view and to_networkx as suggested. --- libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py index 0453554b..c02133fd 100644 --- a/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py +++ b/libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py @@ -558,7 +558,7 @@ def to_networkx( ) -> networkx.DiGraph: """Utility converts Entity Collection to `NetworkX DiGraph `_ - NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + NOTE: Requires optional-dependency "viz", i.e. `pip install "langchain-mongodb[viz]"`. Args: nx_opts: Keyword arguments for networkx calls. @@ -643,7 +643,7 @@ def view( however, another good layout choice is: `layout=nx.multipartite_layout, nx_opts["subset_key"]= "type"` - NOTE: Requires optional-dependency "viz", i.e. `uv sync --extra viz` + NOTE: Requires optional-dependency "viz", i.e. `pip install "langchain-mongodb[viz]"`. You can save the view as any HoloViews object with `.save`. The type will be inferred from the filename's suffix, From caf9d7383e52dc67dd13d99d153329b9510d6084 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Tue, 21 Oct 2025 10:10:33 -0400 Subject: [PATCH 34/37] Updated monorepo/docs lock file. --- uv.lock | 2 -- 1 file changed, 2 deletions(-) diff --git a/uv.lock b/uv.lock index 05a73fab..f9182a70 100644 --- a/uv.lock +++ b/uv.lock @@ -905,7 +905,6 @@ dependencies = [ [package.metadata] requires-dist = [ { name = "holoviews", marker = "extra == 'viz'", specifier = ">=1.19" }, - { name = "jupyter", marker = "extra == 'viz'", specifier = ">=1.1" }, { name = "langchain", specifier = ">=0.3" }, { name = "langchain-core", specifier = ">=0.3" }, { name = "langchain-text-splitters", specifier = ">=0.3" }, @@ -913,7 +912,6 @@ requires-dist = [ { name = "networkx", marker = "extra == 'viz'", specifier = ">=3.0" }, { name = "numpy", specifier = ">=1.26" }, { name = "pymongo", specifier = ">=4.6.1" }, - { name = "pyparsing", marker = "extra == 'viz'", specifier = ">=3.1" }, ] provides-extras = ["viz"] From 68f6ec5fab8824e76430055198218c5b2ff354d0 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 27 Oct 2025 12:20:06 -0400 Subject: [PATCH 35/37] changed upper bounds on lanchain packages < 1.0 --- libs/langchain-mongodb/pyproject.toml | 6 +++--- libs/langchain-mongodb/uv.lock | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml index 19dacc0b..5efbf7c7 100644 --- a/libs/langchain-mongodb/pyproject.toml +++ b/libs/langchain-mongodb/pyproject.toml @@ -10,10 +10,10 @@ description = "An integration package connecting MongoDB and LangChain" readme = "README.md" requires-python = ">=3.9" dependencies = [ - "langchain-core>=0.3", - "langchain>=0.3", + "langchain-core>=0.3,<1.0", + "langchain>=0.3,<1.0", "pymongo>=4.6.1", - "langchain-text-splitters>=0.3", + "langchain-text-splitters>=0.3,<1.0", "numpy>=1.26", "lark<2.0.0,>=1.1.9", ] diff --git a/libs/langchain-mongodb/uv.lock b/libs/langchain-mongodb/uv.lock index b064e970..cc7e6d6d 100644 --- a/libs/langchain-mongodb/uv.lock +++ b/libs/langchain-mongodb/uv.lock @@ -1422,9 +1422,9 @@ dev = [ [package.metadata] requires-dist = [ { name = "holoviews", marker = "extra == 'viz'", specifier = ">=1.19" }, - { name = "langchain", specifier = ">=0.3" }, - { name = "langchain-core", specifier = ">=0.3" }, - { name = "langchain-text-splitters", specifier = ">=0.3" }, + { name = "langchain", specifier = ">=0.3,<1.0" }, + { name = "langchain-core", specifier = ">=0.3,<1.0" }, + { name = "langchain-text-splitters", specifier = ">=0.3,<1.0" }, { name = "lark", specifier = ">=1.1.9,<2.0.0" }, { name = "networkx", marker = "extra == 'viz'", specifier = ">=3.0" }, { name = "numpy", specifier = ">=1.26" }, From 4c6331b703c3e41d87554b21ba3379aa2915ccb1 Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 27 Oct 2025 12:25:02 -0400 Subject: [PATCH 36/37] Uploaded locks for top-level docs build --- uv.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/uv.lock b/uv.lock index f9182a70..6599d974 100644 --- a/uv.lock +++ b/uv.lock @@ -905,9 +905,9 @@ dependencies = [ [package.metadata] requires-dist = [ { name = "holoviews", marker = "extra == 'viz'", specifier = ">=1.19" }, - { name = "langchain", specifier = ">=0.3" }, - { name = "langchain-core", specifier = ">=0.3" }, - { name = "langchain-text-splitters", specifier = ">=0.3" }, + { name = "langchain", specifier = ">=0.3,<1.0" }, + { name = "langchain-core", specifier = ">=0.3,<1.0" }, + { name = "langchain-text-splitters", specifier = ">=0.3,<1.0" }, { name = "lark", specifier = ">=1.1.9,<2.0.0" }, { name = "networkx", marker = "extra == 'viz'", specifier = ">=3.0" }, { name = "numpy", specifier = ">=1.26" }, From 6237ea1cebf80157b0a7cfaf476ccac38a30904a Mon Sep 17 00:00:00 2001 From: Casey Clements Date: Mon, 27 Oct 2025 17:34:22 -0400 Subject: [PATCH 37/37] Reinstated assertion commented out by accident. --- libs/langchain-mongodb/tests/integration_tests/test_graphrag.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py index b10e5a1a..ec056e65 100644 --- a/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py +++ b/libs/langchain-mongodb/tests/integration_tests/test_graphrag.py @@ -318,7 +318,7 @@ def test_networkx(graph_store): pytest.skip("This test requires optional-dependency `viz`") nx_graph = graph_store.to_networkx() - # assert nx_graph.number_of_nodes() == len(nx_graph.nodes) + assert nx_graph.number_of_nodes() == len(nx_graph.nodes) assert isinstance(nx_graph, nx.DiGraph) assert nx_graph.number_of_nodes() == graph_store.collection.count_documents({}) assert nx_graph.number_of_edges() > 0