Diffbot Graph Transformer / Neo4j Graph document ingestion

docs/extras/use_cases/more/graph/diffbot_graphtransformer.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7f0b0c06-ee70-468c-8bf5-b023f9e5e0a2",
+   "metadata": {},
+   "source": [
+    "# Diffbot Graph Transformer\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/more/graph/diffbot_transformer.ipynb)\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "Text data often contain rich relationships and insights that can be useful for various analytics, recommendation engines, or knowledge management applications.\n",
+    "\n",
+    "Diffbot's NLP API allows for the extraction of entities, relationships, and semantic meaning from unstructured text data.\n",
+    "\n",
+    "By coupling Diffbot's NLP API with Neo4j, a graph database, you can create powerful, dynamic graph structures based on the information extracted from text. These graph structures are fully queryable and can be integrated into various applications.\n",
+    "\n",
+    "This combination allows for use cases such as:\n",
+    "\n",
+    "* Building knowledge graphs from textual documents, websites, or social media feeds.\n",
+    "* Generating recommendations based on semantic relationships in the data.\n",
+    "* Creating advanced search features that understand the relationships between entities.\n",
+    "* Building analytics dashboards that allow users to explore the hidden relationships in data.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "LangChain provides tools to interact with Graph Databases:\n",
+    "\n",
+    "1. `Construct knowledge graphs from text` using graph transformer and store integrations \n",
+    "2. `Query a graph database` using chains for query creation and execution\n",
+    "3. `Interact with a graph database` using agents for robust and flexible querying \n",
+    "\n",
+    "## Quickstart\n",
+    "\n",
+    "First, get required packages and set environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "975648da-b24f-4164-a671-6772179e12df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain langchain-experimental openai neo4j wikipedia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77718977-629e-46c2-b091-f9191b9ec569",
+   "metadata": {},
+   "source": [
+    "## Diffbot NLP Service\n",
+    "\n",
+    "Diffbot's NLP service is a tool for extracting entities, relationships, and semantic context from unstructured text data.\n",
+    "This extracted information can be used to construct a knowledge graph.\n",
+    "To use their service, you'll need to obtain an API key from [Diffbot](https://www.diffbot.com/products/natural-language/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2cbf97d0-3682-439b-8750-b695ff726789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer\n",
+    "\n",
+    "diffbot_api_key = \"DIFFBOT_API_KEY\"\n",
+    "diffbot_nlp = DiffbotGraphTransformer(diffbot_api_key=diffbot_api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e3b894a-e3ee-46c7-8116-f8377f8f0159",
+   "metadata": {},
+   "source": [
+    "This code fetches Wikipedia articles about \"Baldur's Gate 3\" and then uses `DiffbotGraphTransformer` to extract entities and relationships.\n",
+    "The `DiffbotGraphTransformer` outputs a structured data `GraphDocument`, which can be used to populate a graph database.\n",
+    "Note that text chunking is avoided due to Diffbot's [character limit per API request](https://docs.diffbot.com/reference/introduction-to-natural-language-api)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "53f8df86-47a1-44a1-9a0f-6725b90703bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WikipediaLoader\n",
+    "\n",
+    "query = \"Warren Buffett\"\n",
+    "raw_documents = WikipediaLoader(query=query).load()\n",
+    "graph_documents = diffbot_nlp.convert_to_graph_documents(raw_documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31bb851a-aab4-4b97-a6b7-fce397d32b47",
+   "metadata": {},
+   "source": [
+    "## Loading the data into a knowledge graph\n",
+    "\n",
+    "You will need to have a running Neo4j instance. One option is to create a [free Neo4j database instance in their Aura cloud service](https://neo4j.com/cloud/platform/aura-graph-database/). You can also run the database locally using the [Neo4j Desktop application](https://neo4j.com/download/), or running a docker container. You can run a local docker container by running the executing the following script:\n",
+    "```\n",
+    "docker run \\\n",
+    "    --name neo4j \\\n",
+    "    -p 7474:7474 -p 7687:7687 \\\n",
+    "    -d \\\n",
+    "    -e NEO4J_AUTH=neo4j/pleaseletmein \\\n",
+    "    -e NEO4J_PLUGINS=\\[\\\"apoc\\\"\\]  \\\n",
+    "    neo4j:latest\n",
+    "```    \n",
+    "If you are using the docker container, you need to wait a couple of second for the database to start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0b2b6641-5a5d-467c-b148-e6aad5e4baa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.graphs import Neo4jGraph\n",
+    "\n",
+    "url=\"bolt://localhost:7687\"\n",
+    "username=\"neo4j\"\n",
+    "password=\"pleaseletmein\"\n",
+    "\n",
+    "graph = Neo4jGraph(\n",
+    "    url=url,\n",
+    "    username=username, \n",
+    "    password=password\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b15e840-fe6f-45db-9193-1b4e2df5c12c",
+   "metadata": {},
+   "source": [
+    "The `GraphDocuments` can be loaded into a knowledge graph using the `add_graph_documents` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1a67c4a8-955c-42a2-9c5d-de3ac0e640ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.add_graph_documents(graph_documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed411e05-2b03-460d-997e-938482774f40",
+   "metadata": {},
+   "source": [
+    "## Refresh graph schema information\n",
+    "If the schema of database changes, you can refresh the schema information needed to generate Cypher statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "904c9ee3-787c-403f-857d-459ce5ad5a1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.refresh_schema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f19d1387-5899-4258-8c94-8ef5fa7db464",
+   "metadata": {},
+   "source": [
+    "## Querying the graph\n",
+    "We can now use the graph cypher QA chain to ask question of the graph. It is advisable to use **gpt-4** to construct Cypher queries to get the best experience."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9393b732-67c8-45c1-9ec2-089f49c62448",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import GraphCypherQAChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    cypher_llm=ChatOpenAI(temperature=0, model_name=\"gpt-4\"),\n",
+    "    qa_llm=ChatOpenAI(temperature=0, model_name=\"gpt-3.5-turbo\"),\n",
+    "    graph=graph, verbose=True,\n",
+    "    \n",
+    ")\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1a9b3652-b436-404d-aa25-5fb576f23dc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (p:Person {name: \"Warren Buffett\"})-[:EDUCATED_AT]->(o:Organization)\n",
+      "RETURN o.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'o.name': 'New York Institute of Finance'}, {'o.name': 'Alice Deal Junior High School'}, {'o.name': 'Woodrow Wilson High School'}, {'o.name': 'University of Nebraska'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Warren Buffett attended the University of Nebraska.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Which university did Warren Buffett attend?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "adc0ba0f-a62c-4875-89ce-da717f3ab148",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[r:EMPLOYEE_OR_MEMBER_OF]->(o:Organization) WHERE o.name = 'Berkshire Hathaway' RETURN p.name\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'p.name': 'Charlie Munger'}, {'p.name': 'Oliver Chace'}, {'p.name': 'Howard Buffett'}, {'p.name': 'Howard'}, {'p.name': 'Susan Buffett'}, {'p.name': 'Warren Buffett'}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Charlie Munger, Oliver Chace, Howard Buffett, Susan Buffett, and Warren Buffett are or were working at Berkshire Hathaway.'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Who is or was working at Berkshire Hathaway?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d636954b-d967-4e96-9489-92e11c74af35",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/experimental/langchain_experimental/graph_transformers/__init__.py

@@ -0,0 +1,5 @@
+from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer
+
+__all__ = [
+    "DiffbotGraphTransformer",
+]