54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
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"""The example leverages the Text2CypherRetriever to fetch some context.
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It uses the OpenAILLM, hence the OPENAI_API_KEY needs to be set in the
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environment for this example to run.
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"""
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import neo4j
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from neo4j_graphrag.llm import OpenAILLM
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from neo4j_graphrag.retrievers import Text2CypherRetriever
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# Define database credentials
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URI = "neo4j+s://demo.neo4jlabs.com"
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AUTH = ("recommendations", "recommendations")
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DATABASE = "recommendations"
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# Create LLM object
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llm = OpenAILLM(model_name="gpt-5", model_params={"temperature": 0})
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# (Optional) Specify your own Neo4j schema
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neo4j_schema = """
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Node properties:
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Person {name: STRING, born: INTEGER}
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Movie {tagline: STRING, title: STRING, released: INTEGER}
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Relationship properties:
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ACTED_IN {roles: LIST}
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DIRECTED {}
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REVIEWED {summary: STRING, rating: INTEGER}
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The relationships:
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(:Person)-[:ACTED_IN]->(:Movie)
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(:Person)-[:DIRECTED]->(:Movie)
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(:Person)-[:REVIEWED]->(:Movie)
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"""
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# (Optional) Provide user input/query pairs for the LLM to use as examples
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examples = [
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"USER INPUT: 'Which actors starred in the Matrix?' QUERY: MATCH (p:Person)-[:ACTED_IN]->(m:Movie) WHERE m.title = 'The Matrix' RETURN p.name"
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]
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with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
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# Initialize the retriever
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retriever = Text2CypherRetriever(
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driver=driver,
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llm=llm,
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neo4j_schema=neo4j_schema,
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examples=examples,
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# optionally, you can also provide your own prompt
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# for the text2Cypher generation step
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# custom_prompt="",
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neo4j_database=DATABASE,
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)
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# Generate a Cypher query using the LLM, send it to the Neo4j database, and return the results
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query_text = "Which movies did Hugo Weaving star in?"
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print(retriever.search(query_text=query_text))
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