참고소스 수정본

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

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+"""The LLM interface is compatible with LangChain chat API,
+ so any LangChain implementation can be used. Same for embedders.
+ For instance, in GraphRAG:
+
+Requires OPENAI_API_KEY to be in the env var.
+"""
+
+import neo4j
+from langchain_openai.chat_models import ChatOpenAI
+from langchain_openai.embeddings import OpenAIEmbeddings
+from neo4j_graphrag.generation import GraphRAG
+from neo4j_graphrag.retrievers import VectorCypherRetriever
+
+# Define database credentials
+URI = "neo4j+s://demo.neo4jlabs.com"
+AUTH = ("recommendations", "recommendations")
+DATABASE = "recommendations"
+INDEX = "moviePlotsEmbedding"
+
+
+driver = neo4j.GraphDatabase.driver(
+    URI,
+    auth=AUTH,
+)
+
+embedder = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+retriever = VectorCypherRetriever(
+    driver,
+    index_name=INDEX,
+    retrieval_query="WITH node, score RETURN node.title as title, node.plot as plot",
+    embedder=embedder,  # type: ignore[arg-type, unused-ignore]
+    neo4j_database=DATABASE,
+)
+
+llm = ChatOpenAI(model="gpt-5", temperature=0)
+
+rag = GraphRAG(
+    retriever=retriever,
+    llm=llm,  # type: ignore[arg-type, unused-ignore]
+)
+
+result = rag.search(
+    "Tell me more about Avatar movies",
+    return_context=False,
+)
+print(result.answer)
+
+driver.close()