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참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/pinecone/README.md

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+### Usage Instructions
+
+You will need both a Pinecone vector database and a Neo4j database to use this retriever.
+
+### Writing Test Data
+
+Update  `NEO4J_AUTH`, `NEO4J_URL`, and `PC_API_KEY` variables in the `tests/e2e/pinecone_e2e/populate_dbs.py` script then run this from the project root to write test data to both dbs.
+
+```
+uv run python -m tests/e2e/pinecone_e2e/populate_dbs.py
+```
+
+### Install Pinecone client
+
+You need to install the `pinecone-client` package to use this retriever.
+
+```bash
+pip install pinecone-client
+```
+
+### Search
+Update the `NEO4J_AUTH`, `NEO4J_URL`, and `PC_API_KEY` variables in each file then run one of the following from the project root to test the retriever.
+
+```
+# Search by vector
+uv run python -m examples.customize.retrievers.external.pinecone.vector_search
+
+# Search by text, with embeddings generated locally
+uv run python -m examples.customize.retrievers.external.pinecone.text_search
+```

참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/pinecone/pinecone_text_search.py

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+"""This example demonstrates how to use PineconeNeo4jRetriever, ie vectors are
+stored in the Pinecone database.
+
+See the [README](./README.md) for more
+information about how spin up a Pinecone and Neo4j databases if needed.
+
+In this example, search is performed from a text. Embeddings are computed
+using OpenAI models. See [../../embeddings/](../../embeddings/) for examples
+using other supported embedders.
+"""
+
+from neo4j import GraphDatabase
+from neo4j_graphrag.embeddings import OpenAIEmbeddings
+from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
+from pinecone import Pinecone
+
+NEO4J_AUTH = ("neo4j", "password")
+NEO4J_URL = "neo4j://localhost:7687"
+PC_API_KEY = "API_KEY"
+
+
+def main() -> None:
+    with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
+        pc_client = Pinecone(PC_API_KEY)
+        embedder = OpenAIEmbeddings()
+
+        retriever = PineconeNeo4jRetriever(
+            driver=neo4j_driver,
+            client=pc_client,
+            index_name="jeopardy",
+            id_property_neo4j="id",
+            embedder=embedder,
+        )
+
+        res = retriever.search(query_text="biology", top_k=2)
+        print(res)
+
+
+if __name__ == "__main__":
+    main()

참고/neo4j-graphrag-python-main/examples/customize/retrievers/external/pinecone/pinecone_vector_search.py

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+"""This example demonstrates how to use PineconeNeo4jRetriever, ie vectors are
+stored in the Pinecone database.
+
+See the [README](./README.md) for more
+information about how spin up a Pinecone and Neo4j databases if needed.
+
+In this example, search is performed from an already computed vector.
+"""
+
+from neo4j import GraphDatabase
+from neo4j_graphrag.embeddings.sentence_transformers import (
+    SentenceTransformerEmbeddings,
+)
+from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
+from pinecone import Pinecone
+
+NEO4J_AUTH = ("neo4j", "password")
+NEO4J_URL = "neo4j://localhost:7687"
+PC_API_KEY = "API_KEY"
+
+
+def main() -> None:
+    with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
+        pc_client = Pinecone(PC_API_KEY)
+        embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
+        retriever = PineconeNeo4jRetriever(
+            driver=neo4j_driver,
+            client=pc_client,
+            index_name="jeopardy",
+            id_property_neo4j="id",
+            embedder=embedder,
+        )
+
+        res = retriever.search(query_text="biology", top_k=2)
+        print(res)
+
+
+if __name__ == "__main__":
+    main()