참고소스 수정본

참고/neo4j-graphrag-python-main/examples/customize/retrievers/hybrid_retrievers/hybrid_cypher_search.py

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+from __future__ import annotations
+
+from random import random
+
+from neo4j import GraphDatabase
+from neo4j_graphrag.embeddings.base import Embedder
+from neo4j_graphrag.indexes import create_fulltext_index, create_vector_index
+from neo4j_graphrag.retrievers import HybridCypherRetriever
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+FULLTEXT_INDEX_NAME = "fulltext-index-name"
+DIMENSION = 1536
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+
+# Create Embedder object
+class CustomEmbedder(Embedder):
+    def embed_query(self, text: str) -> list[float]:
+        return [random() for _ in range(DIMENSION)]
+
+
+embedder = CustomEmbedder()
+
+# Creating the index
+create_vector_index(
+    driver,
+    INDEX_NAME,
+    label="Document",
+    embedding_property="vectorProperty",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+create_fulltext_index(
+    driver, FULLTEXT_INDEX_NAME, label="Document", node_properties=["vectorProperty"]
+)
+
+# Initialize the retriever
+retrieval_query = "MATCH (node)-[:AUTHORED_BY]->(author:Author)RETURN author.name"
+retriever = HybridCypherRetriever(
+    driver, INDEX_NAME, FULLTEXT_INDEX_NAME, retrieval_query, embedder
+)
+
+# Upsert the query
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document {id: $id})"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'vectorProperty', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "id": 0,
+    "vector": vector,
+}
+driver.execute_query(insert_query, parameters)
+
+# Perform the similarity search for a text query
+query_text = "Find me a book about Fremen"
+print(retriever.search(query_text=query_text, top_k=5))

참고/neo4j-graphrag-python-main/examples/customize/retrievers/hybrid_retrievers/hybrid_search.py

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+from __future__ import annotations
+
+from random import random
+
+from neo4j import GraphDatabase
+from neo4j_graphrag.embeddings.base import Embedder
+from neo4j_graphrag.indexes import create_fulltext_index, create_vector_index
+from neo4j_graphrag.retrievers import HybridRetriever
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+FULLTEXT_INDEX_NAME = "fulltext-index-name"
+DIMENSION = 1536
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+
+# Create Embedder object
+class CustomEmbedder(Embedder):
+    def embed_query(self, text: str) -> list[float]:
+        return [random() for _ in range(DIMENSION)]
+
+
+embedder = CustomEmbedder()
+
+# Creating the index
+create_vector_index(
+    driver,
+    INDEX_NAME,
+    label="Document",
+    embedding_property="vectorProperty",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+create_fulltext_index(
+    driver, FULLTEXT_INDEX_NAME, label="Document", node_properties=["vectorProperty"]
+)
+
+# Initialize the retriever
+retriever = HybridRetriever(driver, INDEX_NAME, FULLTEXT_INDEX_NAME, embedder)
+
+# Upsert the query
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document {id: $id})"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'vectorProperty', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "id": 0,
+    "vector": vector,
+}
+driver.execute_query(insert_query, parameters)
+
+# Perform the similarity search for a text query
+query_text = "Find me a book about Fremen"
+print(retriever.search(query_text=query_text, top_k=5))