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https://github.com/hwchase17/langchain
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37 lines
1006 B
Python
37 lines
1006 B
Python
from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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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 RunnableParallel, RunnablePassthrough
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from langchain.vectorstores import Neo4jVector
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retrieval_query = """
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MATCH (node)-[:HAS_PARENT]->(parent)
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RETURN parent.text AS text, score, {} AS metadata
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"""
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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({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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) |