mirror of
https://github.com/hwchase17/langchain
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50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import Neo4jVector
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# Typical RAG retriever
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typical_rag = Neo4jVector.from_existing_index(
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OpenAIEmbeddings(), index_name="typical_rag"
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)
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# Parent retriever
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parent_query = """
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MATCH (node)<-[:HAS_CHILD]-(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 LIMIT 1
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"""
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parent_vectorstore = Neo4jVector.from_existing_index(
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OpenAIEmbeddings(),
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index_name="parent_document",
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retrieval_query=parent_query,
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)
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# Hypothetic questions retriever
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hypothetic_question_query = """
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MATCH (node)<-[:HAS_QUESTION]-(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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hypothetic_question_vectorstore = Neo4jVector.from_existing_index(
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OpenAIEmbeddings(),
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index_name="hypothetical_questions",
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retrieval_query=hypothetic_question_query,
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)
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# Summary retriever
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summary_query = """
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MATCH (node)<-[:HAS_SUMMARY]-(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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summary_vectorstore = Neo4jVector.from_existing_index(
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OpenAIEmbeddings(),
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index_name="summary",
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retrieval_query=summary_query,
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)
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