pinecone

Build a vector database for RAG workloads by upserting dense embeddings with metadata filters and namespaces into a managed index for low-latency semantic retrieval. Pinecone removes the operational burden of self-hosting infrastructure.

Hybrid search combines dense embeddings with sparse keyword signals to improve recall and relevance in real-world search workflows. You need it when pure semantic retrieval misses critical exact-match terminology in your queries.

Isolate data for multi-tenant search applications using namespaces within your vector database index. This allows a single managed index to securely partition tenant data while maintaining fast, scalable query performance.

Filter vector search results by applying metadata filtering to your queries. This restricts retrieval to specific document attributes like product or region, ensuring the most relevant passages are returned in milliseconds.

You do not need to manage scaling for a production vector search index because the system is fully managed and auto-scaling. This lets you focus on retrieval quality instead of database tuning and maintenance.

Upload document embeddings at scale using batch upserts into your vector database. This approach efficiently populates the index with dense vectors and metadata while preparing the system for low-latency querying.