mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
from langchain_community.vectorstores import Neo4jVector
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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retrieval_query = """
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MATCH (node)-[:HAS_PARENT]->(parent)
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WITH parent, max(score) AS score // deduplicate parents
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RETURN parent.text AS text, score, {} AS metadata
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"""
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def format_docs(docs):
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return "\n\n".join(doc.page_content for doc in docs)
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vectorstore = Neo4jVector.from_existing_index(
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OpenAIEmbeddings(),
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index_name="retrieval",
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node_label="Child",
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embedding_node_property="embedding",
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retrieval_query=retrieval_query,
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)
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retriever = vectorstore.as_retriever()
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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model = ChatOpenAI()
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chain = (
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RunnableParallel(
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{"context": retriever | format_docs, "question": RunnablePassthrough()}
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)
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| prompt
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| model
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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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__root__: str
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chain = chain.with_types(input_type=Question)
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