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참고/neo4j-graphrag-python-main/examples/customize/answer/custom_prompt.py

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+"""End to end example of building a RAG pipeline backed by a Neo4j database.
+Requires OPENAI_API_KEY to be in the env var.
+
+This example illustrates:
+- VectorCypherRetriever with a custom formatter function to extract relevant
+    context from neo4j result
+- Use of a custom prompt for RAG
+- Logging configuration
+"""
+
+import neo4j
+from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
+from neo4j_graphrag.generation import GraphRAG, RagTemplate
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.retrievers import VectorCypherRetriever
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+DATABASE = "neo4j"
+INDEX = "moviePlotsEmbedding"
+
+
+driver = neo4j.GraphDatabase.driver(
+    URI,
+    auth=AUTH,
+)
+
+embedder = OpenAIEmbeddings()
+
+retriever = VectorCypherRetriever(
+    driver,
+    index_name=INDEX,
+    retrieval_query="WITH node, score RETURN node.title as title, node.plot as plot",
+    embedder=embedder,
+    neo4j_database=DATABASE,
+)
+
+llm = OpenAILLM(model_name="gpt-5", model_params={"temperature": 0})
+
+template = RagTemplate(
+    template="""You are an expert at movies and actors. Your task is to
+    answer the user's question based on the provided context. Use only the
+    information within that context.
+
+    Context:
+    {context}
+
+    Question:
+    {query_text}
+
+    Answer:
+    """
+)
+
+rag = GraphRAG(retriever=retriever, llm=llm, prompt_template=template)
+
+result = rag.search(
+    "Tell me more about Avatar movies",
+    return_context=True,
+)
+print(result.answer)
+
+driver.close()