Pinecone 2.0: Take Vector Search from the Lab to Production

What this post added

Pinecone 2.0 introduces metadata filtering for vector similarity search, allowing users to combine vector similarity search with arbitrary metadata filters to provide more relevant results. This provides fine-grained control over vector search results at ultra-low latencies, accepting arbitrary filters on metadata and retrieving exactly the number of nearest-neighbor results that match the filters. The update also includes hybrid storage for up to 10x lower costs by storing a compressed vector index in memory and the original, full-resolution vector index on disk. The new architecture uses Kafka ingestion and Kubernetes orchestration, separating read and write paths and disassociating storage and compute for fault tolerance, data persistence, and high availability. A new REST API based on the OpenAPI spec is available for ease of use, along with a revamped Python client. Pinecone 2.0 is SOC2 Type II audited.