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참고/neo4j-graphrag-python-main/examples/customize/retrievers/text2cypher_custom_prompt.py

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+"""The example shows how to provide a custom prompt to Text2CypherRetriever.
+
+Example using the OpenAILLM, hence the OPENAI_API_KEY needs to be set in the
+environment for this example to run.
+"""
+
+import neo4j
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.retrievers import Text2CypherRetriever
+from neo4j_graphrag.schema import get_schema
+
+# Define database credentials
+URI = "neo4j+s://demo.neo4jlabs.com"
+AUTH = ("recommendations", "recommendations")
+DATABASE = "recommendations"
+
+# Create LLM object
+llm = OpenAILLM(model_name="gpt-5", model_params={"temperature": 0})
+
+# (Optional) Specify your own Neo4j schema
+# (also see get_structured_schema and get_schema functions)
+neo4j_schema = """
+Node properties:
+User {name: STRING}
+Person {name: STRING, born: INTEGER}
+Movie {tagline: STRING, title: STRING, released: INTEGER}
+Relationship properties:
+ACTED_IN {roles: LIST}
+DIRECTED {}
+REVIEWED {summary: STRING, rating: INTEGER}
+The relationships:
+(:Person)-[:ACTED_IN]->(:Movie)
+(:Person)-[:DIRECTED]->(:Movie)
+(:User)-[:REVIEWED]->(:Movie)
+"""
+
+prompt = """Task: Generate a Cypher statement for querying a Neo4j graph database from a user input.
+
+Do not use any properties or relationships not included in the schema.
+Do not include triple backticks ``` or any additional text except the generated Cypher statement in your response.
+
+Always filter movies that have not already been reviewed by the user with name: '{user_name}' using for instance:
+(m:Movie)<-[:REVIEWED]-(:User {{name: <the_user_name>}})
+
+Schema:
+{schema}
+
+Input:
+{query_text}
+
+Cypher query:
+"""
+
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
+    # Initialize the retriever
+    retriever = Text2CypherRetriever(
+        driver=driver,
+        llm=llm,
+        neo4j_schema=neo4j_schema,
+        # here we provide a custom prompt
+        custom_prompt=prompt,
+        neo4j_database=DATABASE,
+    )
+
+    # Generate a Cypher query using the LLM, send it to the Neo4j database, and return the results
+    query_text = "Which movies did Hugo Weaving star in?"
+    print(
+        retriever.search(
+            query_text=query_text,
+            prompt_params={
+                # you have to specify all placeholder except the {query_text} one
+                "schema": get_schema(driver),
+                "user_name": "the user asking question",
+            },
+        )
+    )