mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
ebf998acb6
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
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from typing import List, Optional
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from langchain.chat_models import ChatOpenAI
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from langchain.graphs import Neo4jGraph
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from langchain.prompts import ChatPromptTemplate
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnablePassthrough
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from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
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from langchain.chains.openai_functions import create_structured_output_chain
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try:
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from pydantic.v1.main import BaseModel, Field
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except ImportError:
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from pydantic.main import BaseModel, Field
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# Connection to Neo4j
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graph = Neo4jGraph()
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# Cypher validation tool for relationship directions
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corrector_schema = [
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Schema(el["start"], el["type"], el["end"])
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for el in graph.structured_schema.get("relationships")
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]
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cypher_validation = CypherQueryCorrector(corrector_schema)
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# LLMs
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cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
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qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
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# Extract entities from text
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class Entities(BaseModel):
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"""Identifying information about entities."""
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names: List[str] = Field(
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...,
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description="All the person, organization, or business entities that appear in the text",
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are extracting organization and person entities from the text.",
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),
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(
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"human",
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"Use the given format to extract information from the following input: {question}",
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),
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]
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)
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# Fulltext index query
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def map_to_database(entities: Entities) -> Optional[str]:
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result = ""
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for entity in entities.names:
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response = graph.query(
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"CALL db.index.fulltext.queryNodes('entity', $entity + '*', {limit:1})"
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" YIELD node,score RETURN node.name AS result",
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{"entity":entity})
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try:
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result += f"{entity} maps to {response[0]['result']} in database\n"
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except IndexError:
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pass
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return result
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entity_chain = create_structured_output_chain(
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Entities, qa_llm, prompt
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)
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# Generate Cypher statement based on natural language input
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cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
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{schema}
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Entities in the question map to the following database values:
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{entities_list}
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Question: {question}
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Cypher query:"""
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cypher_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"Given an input question, convert it to a Cypher query. No pre-amble.",
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),
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("human", cypher_template),
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]
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)
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cypher_response = (
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RunnablePassthrough.assign(names=entity_chain)
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| RunnablePassthrough.assign(
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entities_list=lambda x: map_to_database(x['names']['function']),
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schema=lambda _: graph.get_schema,
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)
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| cypher_prompt
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| cypher_llm.bind(stop=["\nCypherResult:"])
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| StrOutputParser()
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)
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# Generate natural language response based on database results
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response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
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Question: {question}
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Cypher query: {query}
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Cypher Response: {response}"""
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response_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"Given an input question and Cypher response, convert it to a natural language answer. No pre-amble.",
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),
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("human", response_template),
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]
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)
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chain = (
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RunnablePassthrough.assign(query=cypher_response)
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| RunnablePassthrough.assign(
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response=lambda x: graph.query(cypher_validation(x["query"])),
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)
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| response_prompt
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| qa_llm
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| StrOutputParser()
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)
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