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https://github.com/hwchase17/langchain
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d9abcf1aae
Adding custom graph memory to Cypher chain --------- Co-authored-by: Erick Friis <erick@langchain.dev>
147 lines
4.5 KiB
Python
147 lines
4.5 KiB
Python
from typing import Any, Dict, List
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from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
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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.memory import ChatMessageHistory
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel
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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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# 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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def convert_messages(input: List[Dict[str, Any]]) -> ChatMessageHistory:
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history = ChatMessageHistory()
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for item in input:
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history.add_user_message(item["result"]["question"])
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history.add_ai_message(item["result"]["answer"])
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return history
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def get_history(input: Dict[str, Any]) -> ChatMessageHistory:
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input.pop("question")
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# Lookback conversation window
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window = 3
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data = graph.query(
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"""
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MATCH (u:User {id:$user_id})-[:HAS_SESSION]->(s:Session {id:$session_id}),
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(s)-[:LAST_MESSAGE]->(last_message)
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MATCH p=(last_message)<-[:NEXT*0.."""
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+ str(window)
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+ """]-()
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WITH p, length(p) AS length
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ORDER BY length DESC LIMIT 1
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UNWIND reverse(nodes(p)) AS node
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MATCH (node)-[:HAS_ANSWER]->(answer)
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RETURN {question:node.text, answer:answer.text} AS result
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""",
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params=input,
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)
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history = convert_messages(data)
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return history.messages
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def save_history(input):
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input.pop("response")
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# store history to database
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graph.query(
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"""MERGE (u:User {id: $user_id})
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WITH u
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OPTIONAL MATCH (u)-[:HAS_SESSION]->(s:Session{id: $session_id}),
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(s)-[l:LAST_MESSAGE]->(last_message)
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FOREACH (_ IN CASE WHEN last_message IS NULL THEN [1] ELSE [] END |
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CREATE (u)-[:HAS_SESSION]->(s1:Session {id:$session_id}),
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(s1)-[:LAST_MESSAGE]->(q:Question {text:$question, cypher:$query, date:datetime()}),
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(q)-[:HAS_ANSWER]->(:Answer {text:$output}))
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FOREACH (_ IN CASE WHEN last_message IS NOT NULL THEN [1] ELSE [] END |
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CREATE (last_message)-[:NEXT]->(q:Question
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{text:$question, cypher:$query, date:datetime()}),
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(q)-[:HAS_ANSWER]->(:Answer {text:$output}),
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(s)-[:LAST_MESSAGE]->(q)
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DELETE l) """,
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params=input,
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)
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# Return LLM response to the chain
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return input["output"]
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# Generate Cypher statement based on natural language input
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cypher_template = """This is important for my career.
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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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Question: {question}
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Cypher query:""" # noqa: E501
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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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MessagesPlaceholder(variable_name="history"),
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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(schema=lambda _: graph.get_schema, history=get_history)
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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}""" # noqa: E501
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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 "
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"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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| RunnablePassthrough.assign(
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output=response_prompt | qa_llm | StrOutputParser(),
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)
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| save_history
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)
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# Add typing for input
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class Question(BaseModel):
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question: str
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user_id: str
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session_id: str
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chain = chain.with_types(input_type=Question)
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