77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
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"""The example shows how to provide a custom prompt to Text2CypherRetriever.
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Example using 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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from neo4j_graphrag.schema import get_schema
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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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# (also see get_structured_schema and get_schema functions)
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neo4j_schema = """
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Node properties:
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User {name: STRING}
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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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(:User)-[:REVIEWED]->(:Movie)
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"""
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prompt = """Task: Generate a Cypher statement for querying a Neo4j graph database from a user input.
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Do not use any properties or relationships not included in the schema.
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Do not include triple backticks ``` or any additional text except the generated Cypher statement in your response.
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Always filter movies that have not already been reviewed by the user with name: '{user_name}' using for instance:
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(m:Movie)<-[:REVIEWED]-(:User {{name: <the_user_name>}})
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Schema:
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{schema}
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Input:
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{query_text}
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Cypher query:
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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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# here we provide a custom prompt
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custom_prompt=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(
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retriever.search(
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query_text=query_text,
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prompt_params={
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# you have to specify all placeholder except the {query_text} one
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"schema": get_schema(driver),
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"user_name": "the user asking question",
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},
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)
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)
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