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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['OPENAI_API_KEY'] = \"\"\n",
"\n",
"import logging\n",
"import sys\n",
"\n",
"logging.basicConfig(\n",
" stream=sys.stdout, level=logging.INFO\n",
") # logging.DEBUG for more verbose output\n",
"\n",
"\n",
"# define LLM\n",
"from llama_index.llms.openai import OpenAI\n",
"from llama_index.core import Settings\n",
"\n",
"Settings.llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo-0125\")\n",
"Settings.chunk_size = 512"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: langchain in /local/home/pbhandari/miniconda3/envs/graph_rag/lib/python3.9/site-packages (0.1.16)\n",
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]
}
],
"source": [
"%pip install langchain neo4j openai wikipedia tiktoken langchain_openai"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from langchain.graphs import Neo4jGraph\n",
"\n",
"url = \"neo4j+s://2f409740.databases.neo4j.io\"\n",
"username =\"neo4j\"\n",
"password = \"\"\n",
"graph = Neo4jGraph(\n",
" url=url,\n",
" username=username,\n",
" password=password\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.graphs.graph_document import (\n",
" Node as BaseNode,\n",
" Relationship as BaseRelationship,\n",
" GraphDocument,\n",
")\n",
"from langchain.schema import Document\n",
"from typing import List, Dict, Any, Optional\n",
"from langchain.pydantic_v1 import Field, BaseModel\n",
"\n",
"class Property(BaseModel):\n",
" \"\"\"A single property consisting of key and value\"\"\"\n",
" key: str = Field(..., description=\"key\")\n",
" value: str = Field(..., description=\"value\")\n",
"\n",
"class Node(BaseNode):\n",
" properties: Optional[List[Property]] = Field(\n",
" None, description=\"List of node properties\")\n",
"\n",
"class Relationship(BaseRelationship):\n",
" properties: Optional[List[Property]] = Field(\n",
" None, description=\"List of relationship properties\"\n",
" )\n",
"\n",
"class KnowledgeGraph(BaseModel):\n",
" \"\"\"Generate a knowledge graph with entities and relationships.\"\"\"\n",
" nodes: List[Node] = Field(\n",
" ..., description=\"List of nodes in the knowledge graph\")\n",
" rels: List[Relationship] = Field(\n",
" ..., description=\"List of relationships in the knowledge graph\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def format_property_key(s: str) -> str:\n",
" words = s.split()\n",
" if not words:\n",
" return s\n",
" first_word = words[0].lower()\n",
" capitalized_words = [word.capitalize() for word in words[1:]]\n",
" return \"\".join([first_word] + capitalized_words)\n",
"\n",
"def props_to_dict(props) -> dict:\n",
" \"\"\"Convert properties to a dictionary.\"\"\"\n",
" properties = {}\n",
" if not props:\n",
" return properties\n",
" for p in props:\n",
" properties[format_property_key(p.key)] = p.value\n",
" return properties\n",
"\n",
"def map_to_base_node(node: Node) -> BaseNode:\n",
" \"\"\"Map the KnowledgeGraph Node to the base Node.\"\"\"\n",
" properties = props_to_dict(node.properties) if node.properties else {}\n",
" # Add name property for better Cypher statement generation\n",
" properties[\"name\"] = node.id.title()\n",
" return BaseNode(\n",
" id=node.id.title(), type=node.type.capitalize(), properties=properties\n",
" )\n",
"\n",
"\n",
"def map_to_base_relationship(rel: Relationship) -> BaseRelationship:\n",
" \"\"\"Map the KnowledgeGraph Relationship to the base Relationship.\"\"\"\n",
" source = map_to_base_node(rel.source)\n",
" target = map_to_base_node(rel.target)\n",
" properties = props_to_dict(rel.properties) if rel.properties else {}\n",
" return BaseRelationship(\n",
" source=source, target=target, type=rel.type, properties=properties\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.chains.openai_functions import (\n",
" create_openai_fn_chain,\n",
" create_structured_output_chain,\n",
")\n",
"from langchain_openai import ChatOpenAI\n",
"from langchain.prompts import ChatPromptTemplate\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-16k\", temperature=0)\n",
"\n",
"def get_extraction_chain(\n",
" allowed_nodes: Optional[List[str]] = None,\n",
" allowed_rels: Optional[List[str]] = None\n",
" ):\n",
" prompt = ChatPromptTemplate.from_messages(\n",
" [(\n",
" \"system\",\n",
" f\"\"\"# Knowledge Graph Instructions for GPT-4\n",
"## 1. Overview\n",
"You are a sophisticated algorithm tailored for parsing Wikipedia pages to construct a knowledge graph about chemotherapy and related cancer treatments.\n",
"- **Nodes** symbolize entities such as medical conditions, drugs, symptoms, treatments, and associated medical concepts.\n",
"- The goal is to create a precise and comprehensible knowledge graph, serving as a reliable resource for medical practitioners and scholarly research.\n",
"\n",
"## 2. Labeling Nodes\n",
"- **Consistency**: Utilize uniform labels for node types to maintain clarity.\n",
" - For instance, consistently label drugs as **\"Drug\"**, symptoms as **\"Symptom\"**, and treatments as **\"Treatment\"**.\n",
"- **Node IDs**: Apply descriptive, legible identifiers for node IDs, sourced directly from the text.\n",
"\n",
"{'- **Allowed Node Labels:**' + \", \".join(['Drug', 'Symptom', 'Treatment', 'MedicalCondition', 'ResearchStudy']) if allowed_nodes else \"\"}\n",
"{'- **Allowed Relationship Types**:' + \", \".join(['Treats', 'Causes', 'Researches', 'Recommends']) if allowed_rels else \"\"}\n",
"\n",
"## 3. Handling Numerical Data and Dates\n",
"- Integrate numerical data and dates as attributes of the corresponding nodes.\n",
"- **No Isolated Nodes for Dates/Numbers**: Directly associate dates and numerical figures as attributes with pertinent nodes.\n",
"- **Property Format**: Follow a straightforward key-value pattern for properties, with keys in camelCase, for example, `approvedYear`, `dosageAmount`.\n",
"\n",
"## 4. Coreference Resolution\n",
"- **Entity Consistency**: Guarantee uniform identification of each entity across the graph.\n",
" - For example, if \"Methotrexate\" and \"MTX\" reference the same medication, uniformly apply \"Methotrexate\" as the node ID.\n",
"\n",
"## 5. Relationship Naming Conventions\n",
"- **Clarity and Standardization**: Utilize clear and standardized relationship names, preferring uppercase with underscores for readability.\n",
" - For instance, use \"HAS_SIDE_EFFECT\" instead of \"HASSIDEEFFECT\", use \"CAN_RESULT_FROM\" instead of \"CANRESULTFROM\" etc. You keep making the same mistakes of storing the relationships without the \"_\" in between the words. Any further similar errors will lead to termination.\n",
"- **Relevance and Specificity**: Choose relationship names that accurately reflect the connection between nodes, such as \"INHIBITS\" or \"ACTIVATES\" for interactions between substances.\n",
"\n",
"## 6. Strict Compliance\n",
"Rigorous adherence to these instructions is essential. Failure to comply with the specified formatting and labeling norms will necessitate output revision or discard.\n",
" \"\"\"),\n",
" (\"human\", \"Use the given format to extract information from the following input: {input}\"),\n",
" (\"human\", \"Tip: Precision in the node and relationship creation is vital for the integrity of the knowledge graph.\"),\n",
" ])\n",
" return create_structured_output_chain(KnowledgeGraph, llm, prompt)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def extract_and_store_graph(\n",
" document: Document,\n",
" nodes:Optional[List[str]] = None,\n",
" rels:Optional[List[str]]=None) -> None:\n",
" # Extract graph data using OpenAI functions\n",
" extract_chain = get_extraction_chain(nodes, rels)\n",
" data = extract_chain.invoke(document.page_content)['function']\n",
" # Construct a graph document\n",
" graph_document = GraphDocument(\n",
" nodes = [map_to_base_node(node) for node in data.nodes],\n",
" relationships = [map_to_base_relationship(rel) for rel in data.rels],\n",
" source = document\n",
" )\n",
" # Store information into a graph\n",
" graph.add_graph_documents([graph_document])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import WikipediaLoader\n",
"from langchain.text_splitter import TokenTextSplitter\n",
"\n",
"# Read the wikipedia article\n",
"raw_documents = WikipediaLoader(query=\"Chemotherapy\").load()\n",
"# Define chunking strategy\n",
"text_splitter = TokenTextSplitter(chunk_size=4096, chunk_overlap=96)\n",
"\n",
"# Only take the first the raw_documents\n",
"documents = text_splitter.split_documents(raw_documents[:5])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/5 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/5 [01:25<?, ?it/s]\n"
]
},
{
"ename": "TypeError",
"evalue": "'KnowledgeGraph' object is not subscriptable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[14], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, d \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(documents), total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(documents)):\n\u001b[0;32m----> 4\u001b[0m \u001b[43mextract_and_store_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[12], line 7\u001b[0m, in \u001b[0;36mextract_and_store_graph\u001b[0;34m(document, nodes, rels)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mextract_and_store_graph\u001b[39m(\n\u001b[1;32m 2\u001b[0m document: Document,\n\u001b[1;32m 3\u001b[0m nodes:Optional[List[\u001b[38;5;28mstr\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4\u001b[0m rels:Optional[List[\u001b[38;5;28mstr\u001b[39m]]\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Extract graph data using OpenAI functions\u001b[39;00m\n\u001b[1;32m 6\u001b[0m extract_chain \u001b[38;5;241m=\u001b[39m get_extraction_chain(nodes, rels)\n\u001b[0;32m----> 7\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mextract_chain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocument\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpage_content\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfunction\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Construct a graph document\u001b[39;00m\n\u001b[1;32m 9\u001b[0m graph_document \u001b[38;5;241m=\u001b[39m GraphDocument(\n\u001b[1;32m 10\u001b[0m nodes \u001b[38;5;241m=\u001b[39m [map_to_base_node(node) \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m data\u001b[38;5;241m.\u001b[39mnodes],\n\u001b[1;32m 11\u001b[0m relationships \u001b[38;5;241m=\u001b[39m [map_to_base_relationship(rel) \u001b[38;5;28;01mfor\u001b[39;00m rel \u001b[38;5;129;01min\u001b[39;00m data\u001b[38;5;241m.\u001b[39mrels],\n\u001b[1;32m 12\u001b[0m source \u001b[38;5;241m=\u001b[39m document\n\u001b[1;32m 13\u001b[0m )\n",
"\u001b[0;31mTypeError\u001b[0m: 'KnowledgeGraph' object is not subscriptable"
]
}
],
"source": [
"from tqdm import tqdm\n",
"\n",
"for i, d in tqdm(enumerate(documents), total=len(documents)):\n",
" extract_and_store_graph(d)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Query the knowledge graph in a RAG application\n",
"from langchain.chains import GraphCypherQAChain\n",
"\n",
"graph.refresh_schema()\n",
"\n",
"cypher_chain = GraphCypherQAChain.from_llm(\n",
" graph=graph,\n",
" cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-4\"),\n",
" qa_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
" #validate_cypher=True, # Validate relationship directions\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (t:Treatment {name: \"Induction Chemotherapy\"})-[:CONTROLS]->(mc) RETURN mc.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'mc.name': 'Malignant Lymphomas'}, {'mc.name': 'Head And Neck Squamous Cell Carcinomas'}, {'mc.name': 'Malignant Lymphomas'}, {'mc.name': 'Head And Neck Squamous Cell Carcinomas'}]\u001b[0m\n",
"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'What does Induction Chemotherapy control?',\n",
" 'result': 'Induction Chemotherapy controls Malignant Lymphomas and Head And Neck Squamous Cell Carcinomas.'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cypher_chain.invoke({\"query\": \"What does Induction Chemotherapy control?\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "my_project_env",
"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": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|