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참고/neo4j-graphrag-python-main/examples/question_answering/graphrag.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
+- Logging configuration
+"""
+
+import logging
+
+import neo4j
+from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
+from neo4j_graphrag.generation import GraphRAG
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.retrievers import VectorCypherRetriever
+from neo4j_graphrag.types import RetrieverResultItem
+
+# Define database credentials
+URI = "neo4j+s://demo.neo4jlabs.com"
+AUTH = ("recommendations", "recommendations")
+DATABASE = "recommendations"
+INDEX = "moviePlotsEmbedding"
+
+
+# setup logger config
+logger = logging.getLogger("neo4j_graphrag")
+logging.basicConfig(format="%(asctime)s - %(message)s")
+logger.setLevel(logging.DEBUG)
+
+
+def formatter(record: neo4j.Record) -> RetrieverResultItem:
+    return RetrieverResultItem(content=f"{record.get('title')}: {record.get('plot')}")
+
+
+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",
+    result_formatter=formatter,
+    embedder=embedder,
+    neo4j_database=DATABASE,
+)
+
+llm = OpenAILLM(model_name="gpt-5", model_params={"temperature": 0})
+
+rag = GraphRAG(retriever=retriever, llm=llm)
+
+result = rag.search(
+    "Tell me more about Avatar movies",
+    return_context=True,
+    # optional
+    response_fallback="I can't answer this question without context",
+)
+print(result.answer)
+# print(result.retriever_result)
+
+driver.close()

참고/neo4j-graphrag-python-main/examples/question_answering/graphrag_with_message_history.py

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+"""End to end example of building a RAG pipeline backed by a Neo4j database,
+simulating a chat with message history feature.
+
+Requires OPENAI_API_KEY to be in the env var.
+"""
+
+import neo4j
+from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
+from neo4j_graphrag.generation import GraphRAG
+from neo4j_graphrag.llm import OpenAILLM
+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()
+
+retriever = VectorCypherRetriever(
+    driver,
+    index_name=INDEX,
+    retrieval_query="""
+        WITH node as movie, score
+        CALL(movie) {
+            MATCH (movie)<-[:ACTED_IN]-(p:Person)
+            RETURN collect(p.name) as actors
+        }
+        CALL(movie) {
+            MATCH (movie)<-[:DIRECTED]-(p:Person)
+            RETURN collect(p.name) as directors
+        }
+        RETURN movie.title as title, movie.plot as plot, movie.year as year, actors, directors
+    """,
+    embedder=embedder,
+    neo4j_database=DATABASE,
+)
+
+llm = OpenAILLM(model_name="gpt-5", model_params={"temperature": 0})
+
+rag = GraphRAG(
+    retriever=retriever,
+    llm=llm,
+)
+
+questions = [
+    "Who starred in the Apollo 13 movies?",
+    "Who was its director?",
+    "In which year was this movie released?",
+]
+
+history: list[dict[str, str]] = []
+for question in questions:
+    result = rag.search(
+        question,
+        return_context=False,
+        message_history=history,  # type: ignore
+    )
+
+    answer = result.answer
+    print("#" * 50, question)
+    print(answer)
+    print("#" * 50)
+
+    history.append(
+        {
+            "role": "user",
+            "content": question,
+        }
+    )
+    history.append(
+        {
+            "role": "assistant",
+            "content": answer,
+        }
+    )
+
+driver.close()

참고/neo4j-graphrag-python-main/examples/question_answering/graphrag_with_neo4j_message_history.py

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+"""End to end example of building a RAG pipeline backed by a Neo4j database,
+simulating a chat with message history which is also stored in Neo4j.
+
+Requires OPENAI_API_KEY to be in the env var.
+"""
+
+import neo4j
+from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
+from neo4j_graphrag.generation import GraphRAG
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.message_history import Neo4jMessageHistory
+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()
+
+retriever = VectorCypherRetriever(
+    driver,
+    index_name=INDEX,
+    retrieval_query="""
+        WITH node as movie, score
+        CALL(movie) {
+            MATCH (movie)<-[:ACTED_IN]-(p:Person)
+            RETURN collect(p.name) as actors
+        }
+        CALL(movie) {
+            MATCH (movie)<-[:DIRECTED]-(p:Person)
+            RETURN collect(p.name) as directors
+        }
+        RETURN movie.title as title, movie.plot as plot, movie.year as year, actors, directors
+    """,
+    embedder=embedder,
+    neo4j_database=DATABASE,
+)
+
+llm = OpenAILLM(model_name="gpt-5", model_params={"temperature": 0})
+
+rag = GraphRAG(
+    retriever=retriever,
+    llm=llm,
+)
+
+history = Neo4jMessageHistory(session_id="123", driver=driver, window=10)
+
+questions = [
+    "Who starred in the Apollo 13 movies?",
+    "Who was its director?",
+    "In which year was this movie released?",
+]
+
+for question in questions:
+    result = rag.search(
+        question,
+        return_context=False,
+        message_history=history,
+    )
+
+    answer = result.answer
+    print("#" * 50, question)
+    print(answer)
+    print("#" * 50)
+
+    history.add_message(
+        {
+            "role": "user",
+            "content": question,
+        }
+    )
+    history.add_message(
+        {
+            "role": "assistant",
+            "content": answer,
+        }
+    )
+
+driver.close()