to properly use LlamaIndex with Pinecone:
1.) initialize Pinecone and create an index:
python
from pinecone.grpc import PineconeGRPC as Pineconefrom pinecone import ServerlessSpec
pc = Pinecone(api_key="YOUR_API_KEY")
pc.create_index( name="example-index", dimension=1536, metric="cosine", spec=ServerlessSpec( cloud="aws", region="us-east-1" ))
2.) To query data using LlamaIndex and Pinecone, you need to:
python
from llama_index.core import VectorStoreIndexfrom llama_index.core.retrievers import VectorIndexRetriever
# Create VectorStoreIndex from your vector_storevector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
# Configure retriever with number of resultsretriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)
# Query vector DBanswer = retriever.retrieve('your query here')
- For querying, you can create a query engine using the RetrieverQueryEngine:
python
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = RetrieverQueryEngine(retriever=retriever)response = query_engine.query('your query here')