Lakebase pgvector

Enable vector similarity search in Lakebase using the pgvector extension. Covers extension setup, vector table design, insert and cosine retrieval helpers, and IVFFlat/HNSW index options.

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When done, you will have:

The pgvector extension enabled on your Lakebase instance
A vector embedding table with configurable dimensions
Server-side functions for inserting documents and performing similarity search
An IVFFlat or HNSW index for efficient nearest-neighbor queries

Prerequisites

Verify these Databricks workspace features are enabled before starting. If any check fails, ask your workspace admin to enable the feature.

Lakebase Postgres available. Run databricks postgres list-projects --profile <PROFILE> and confirm the command succeeds. A not enabled error means Lakebase is not available to this identity.
A provisioned Lakebase project. Complete the Create a Lakebase Instance template first. You will enable the vector extension against its primary endpoint.
databricks psql available in your CLI. Run databricks psql --help and confirm the subcommand exists. If it does not, upgrade the Databricks CLI (see Set Up Your Local Dev Environment).

Lakebase pgvector

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Prerequisites

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Create a Lakebase Instance. [Provision a managed Lakebase Postgres project on Databricks and collect the connection values needed by downstream templates.]

Lakebase Data Persistence. [Add a managed Postgres database to your Databricks app using the Lakebase plugin. Covers schema setup, table creation, and full CRUD REST API routes.]

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