---
title: "Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres"
description: "Lakebase Search is a beta offering on AWS and Azure that adds hybrid vector and full-text retrieval to Lakebase Postgres. It uses the lakebase_vector and lakebase_text extensions to keep retrieval, memory, operational data, and hybrid search in one backend. lakebase_vector retains pgvector types and operators, applies RaBitQ clustering and compression for 32x smaller indexes, and targets more than 1B vectors, while lakebase_text replaces GIN with object-storage-optimized BM25 ranking. A tiered cache keeps hot data on NVMe and places colder data in object storage; the source reports lower memory needs, faster index builds, and cold-cache startup than standard pgvector HNSW in its LAION-100M benchmark. The extensions also combine vector similarity and keyword relevance with reciprocal rank fusion in a single SQL query, enabling joins and tenant filtering alongside transactional workflows."
---

# Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres

[Databricks](https://yomu.fyi/company/databricks) · Pranav Aurora, Zhou Sun, Jinjing Zhou · Jun 16, 2026

**Type:** Announcement

## Summary

Lakebase Search is a beta offering on AWS and Azure that adds hybrid vector and full-text retrieval to Lakebase Postgres. It uses the lakebase\_vector and lakebase\_text extensions to keep retrieval, memory, operational data, and hybrid search in one backend. lakebase\_vector retains pgvector types and operators, applies RaBitQ clustering and compression for 32x smaller indexes, and targets more than 1B vectors, while lakebase\_text replaces GIN with object-storage-optimized BM25 ranking. A tiered cache keeps hot data on NVMe and places colder data in object storage; the source reports lower memory needs, faster index builds, and cold-cache startup than standard pgvector HNSW in its LAION-100M benchmark. The extensions also combine vector similarity and keyword relevance with reciprocal rank fusion in a single SQL query, enabling joins and tenant filtering alongside transactional workflows.

## Context

Agents use search as part of an operational read/write loop: they retrieve context, write new memories, and need newly generated data to be searchable immediately. The source identifies cold, mostly idle data, vector-index bloat, and memory-bound traditional indexes as constraints for large-scale multi-tenant search.

## Approach / What changed

Lakebase Search adds the lakebase\_vector and lakebase\_text Postgres extensions on a tiered Lakebase architecture. The vector extension uses native pgvector types with RaBitQ compression and clustering, while the text extension provides BM25 through an index designed for sequential object-storage reads. Both support hybrid search through reciprocal rank fusion in one SQL query.

## Takeaways

- lakebase\_vector preserves standard pgvector types and operators while using RaBitQ to reduce index footprint 32x; a 100-million-vector index described in the source fits in under 10 GB instead of 300 GB of RAM.
- On LAION-100M, lakebase\_vector runs on a 192 GB instance with a 1.5-hour index build; standard pgvector HNSW is reported to require a 512 GB instance and about 40 hours to build.
- lakebase\_text replaces RAM-resident GIN indexing with an object-storage-optimized index and native BM25 ranking, while reciprocal rank fusion combines keyword and vector results in one SQL query.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Architecture](https://yomu.fyi/topic/architecture), [Lakebase](https://yomu.fyi/topic/lakebase), [Postgres](https://yomu.fyi/topic/postgres), [Search](https://yomu.fyi/topic/search)

- Source: [Databricks](https://www.databricks.com/blog/announcing-lakebase-search-agent-native-retrieval-built-lakebase-postgres)
- Source URL: https://www.databricks.com/blog/announcing-lakebase-search-agent-native-retrieval-built-lakebase-postgres
- Ingested by Yomu: 2026-08-30T17:02:21.617Z

[Read original post](https://www.databricks.com/blog/announcing-lakebase-search-agent-native-retrieval-built-lakebase-postgres)