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참고/neo4j-graphrag-python-main/examples/retrieve/text2cypher_search.py

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+"""The example leverages the Text2CypherRetriever to fetch some context.
+It uses 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
+
+# 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
+neo4j_schema = """
+Node properties:
+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)
+(:Person)-[:REVIEWED]->(:Movie)
+"""
+
+# (Optional) Provide user input/query pairs for the LLM to use as examples
+examples = [
+    "USER INPUT: 'Which actors starred in the Matrix?' QUERY: MATCH (p:Person)-[:ACTED_IN]->(m:Movie) WHERE m.title = 'The Matrix' RETURN p.name"
+]
+
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
+    # Initialize the retriever
+    retriever = Text2CypherRetriever(
+        driver=driver,
+        llm=llm,
+        neo4j_schema=neo4j_schema,
+        examples=examples,
+        # optionally, you can also provide your own prompt
+        # for the text2Cypher generation step
+        # custom_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))