참고소스 수정본

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

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+"""This example uses an example Movie database where movies' plots are embedded
+using OpenAI embeddings. OPENAI_API_KEY needs to be set in the environment for
+this example to run.
+
+Also requires minimal Cypher knowledge to write the retrieval query.
+
+It shows how to use a hybrid retriever to find context
+similar to a query **text** using vector+text similarity
+and graph traversal.
+"""
+
+import neo4j
+from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
+from neo4j_graphrag.retrievers import HybridCypherRetriever
+
+# Define database credentials
+URI = "neo4j+s://demo.neo4jlabs.com"
+AUTH = ("recommendations", "recommendations")
+DATABASE = "recommendations"
+INDEX_NAME = "moviePlotsEmbedding"
+FULLTEXT_INDEX_NAME = "movieFulltext"
+
+# for each Movie node matched by the vector search, retrieve more context:
+# the name of all actors starring in that movie
+RETRIEVAL_QUERY = """
+RETURN  node.title as movieTitle,
+        node.plot as moviePlot,
+        collect { MATCH (actor:Actor)-[:ACTED_IN]->(node) RETURN actor.name } AS actors,
+        score as similarityScore
+"""
+
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
+    # Initialize the retriever
+    retriever = HybridCypherRetriever(
+        driver=driver,
+        vector_index_name=INDEX_NAME,
+        fulltext_index_name=FULLTEXT_INDEX_NAME,
+        # note: embedder is optional if you only use query_vector
+        embedder=OpenAIEmbeddings(),
+        retrieval_query=RETRIEVAL_QUERY,
+        # optionally, configure how to format the results
+        # (see corresponding example in 'customize' directory)
+        # result_formatter=None,
+        # optionally, set neo4j database
+        neo4j_database=DATABASE,
+    )
+
+    # Perform the similarity search for a text query
+    # (retrieve the top 5 most similar nodes)
+    query_text = "Who were the actors in Avatar?"
+    print(retriever.search(query_text=query_text, top_k=5))
+
+    # note: it is also possible to query from a query_vector directly:
+    # query_vector: list[float] = [...]
+    # retriever.search(query_vector=query_vector, top_k=5)