pinecone

To set up a managed vector database for production RAG, initialize the client with an API key, create a serverless index with the correct dimension and metric, upsert vectors with metadata, and run similarity queries using top_k and optional filters.

Hybrid search combines semantic dense embeddings with keyword-based sparse vectors like BM25 to improve retrieval recall. You query by passing hybrid parameters to retrieve relevant context using both semantic similarity and exact keyword matching.

You can isolate data per tenant in a multi-tenant retrieval system by using namespaces. Namespaces partition your vector index so queries and upserts only interact with the specific tenant's data scope.

You filter vector search results by specific attributes using metadata filtering. Attach metadata fields during vector upsert, then apply metadata filters within your query request to restrict retrieval to matching records.

Pinecone integrates with LangChain and LlamaIndex for RAG applications. You can use established integration patterns to connect your vector database directly within these frameworks for storing embeddings and retrieving context.

Using a managed vector database is the best way to scale semantic search without managing infrastructure. It handles provisioning, scaling, and high availability so your application maintains low-latency retrieval performance reliably.