# databricks-vector-search

Skill for creating Databricks vector indexes and querying embeddings in Delta tables. Useful for RAG applications and semantic search.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 380 installs · ★ 368
- Author: databricks
- License: LicenseRef-Unknown · policy=link-only · description only; the file stays with the author
- Source: https://github.com/databricks/databricks-agent-skills/tree/HEAD/skills/databricks-vector-search/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/databricks-vector-search

## What it does

The skill teaches the agent to work with Databricks Vector Search: create endpoints (`STANDARD` or `STORAGE_OPTIMIZED`), build three index types (Delta Sync with automatic embedding generation, Delta Sync with precomputed vectors, Direct Access for manual control), and query results by text or vector. The agent can configure hybrid search (ANN + BM25) — helpful when queries contain exact terms like SKUs or error codes. Filters for Standard endpoints use dictionary format; Storage-Optimized endpoints require SQL-like syntax via the `databricks-vectorsearch` package.

Delta Sync indexes auto-sync with the source Delta table. `TRIGGERED` mode runs sync on demand, `CONTINUOUS` tracks updates. Direct Access indexes let you add, update, and delete vectors via `upsert_data_vector_index` and `delete_data_vector_index`. Storage-Optimized endpoints handle over a billion vectors but latency is higher — 300–500ms versus 20–50ms for Standard.

## Requirements
- Works with: Databricks

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add databricks/databricks-agent-skills --skill databricks-vector-search -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/databricks-vector-search/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/databricks-vector-search/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add databricks/databricks-agent-skills --skill databricks-vector-search -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/databricks-vector-search/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add databricks/databricks-agent-skills --skill databricks-vector-search -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/databricks-vector-search/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install databricks/databricks-agent-skills databricks-vector-search
```

2. **Manually: put the files in place**
- File: `.github/skills/databricks-vector-search/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add databricks/databricks-agent-skills --skill databricks-vector-search -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/databricks/databricks-agent-skills --path skills/databricks-vector-search
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/databricks-vector-search/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add databricks/databricks-agent-skills --skill databricks-vector-search -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/databricks/databricks-agent-skills/tree/HEAD/skills/databricks-vector-search/SKILL.md

## When to use it
- create a vector index from a Delta table with automatic embedding generation
- run semantic search by text or vector
- configure hybrid search with BM25 for exact terms
- sync an index with a Delta table in real time
- choose between Standard and Storage-Optimized for billions of vectors

## Example prompts
- Create a vector index `catalog.schema.docs_index` on endpoint `my-vs-endpoint` from table `catalog.schema.documents`, embed column `content` via `databricks-gte-large-en`
- Query index `catalog.schema.my_index` for 10 nearest results by text "What is machine learning?", return `id`, `content`, and `metadata`
- Upsert two records with `id`, `text`, `embedding`, and `metadata` into Direct Access index `catalog.schema.direct_index` using `upsert_data_vector_index`

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From the same set: [databricks/databricks-agent-skills](https://affpapa.org/en/nevibecode/by/databricks/databricks-agent-skills.md) — 12 cards

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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md