Lakebase Search: the retrieval primitive for agents on Neon

Lakebase Search, now generally available on Neon’s Lakebase Postgres platform, provides vector, BM25 full-text, and hybrid search capabilities intended for AI agents without requiring data to move to a separate search system. Its lakebase_vector extension uses hierarchical IVF and RaBitQ quantization with durable indexes stored in object storage, allowing compute to scale independently, suspend when idle, and remain compatible with pgvector queries and data types. The lakebase_text extension adds BM25 ranking and top-K query support while preserving standard Postgres full-text types and operators, enabling combined semantic and exact-keyword retrieval. Neon positions Lakebase Search as part of an integrated retrieval stack that includes Object Storage for source files, Neon Functions for TypeScript-based ingestion and search endpoints, and AI Gateway for embedding generation. Documents can be automatically parsed, chunked, embedded, and indexed after upload, while agents can access hybrid retrieval through a single tool and iteratively refine searches based on returned results.

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