Full-Text Search is Now Generally Available In Pinecone Database

Pinecone Database has made full-text search generally available, combining BM25 keyword ranking and text-match filters with dense and sparse vector search in a single document-based index. The feature is intended to address cases where semantic embeddings can return similar but incorrect results for literal identifiers such as SKUs, part numbers, error codes, order IDs, and quoted phrases, which can be important in retrieval, recommendations, RAG applications, and agent workflows. Users can define text, dense vector, and sparse vector fields in one schema and query them through the Documents API, using capabilities including Lucene syntax, phrase and Boolean queries, fuzzy matching, filters, and tokenization and stemming across 18 languages. Pinecone positions the service as an alternative to operating a separate lexical-search cluster, retaining its serverless, usage-based capacity model while also offering Dedicated Read Nodes and BYOC deployment options.

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