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https://github.com/hwchase17/langchain
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ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
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from langchain.prompts import ChatPromptTemplate
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.graphs import Neo4jGraph
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables 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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# 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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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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("human", cypher_template),
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]
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)
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cypher_response = (
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RunnablePassthrough.assign(
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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}""" # 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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| response_prompt
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| qa_llm
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| StrOutputParser()
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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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chain = chain.with_types(input_type=Question)
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