# Build AgentDB Vector Search

Teams need fast semantic retrieval without designing vector storage from scratch. This skill guides AgentDB setup, embedding storage, similarity search, and RAG retrieval workflows.

## Install

```bash
npx skillstore add ruvnet/agentdb-vector-search
```

## Metadata

- Status: approved
- Slug: ruvnet-agentdb-vector-search
- Skillstore revision: r1
- Version status: missing
- Tree hash: 600a88e5a7fd7efce246843d4743c9e491ed9b0a24290784ec0b0c0efcb52fff
- Author: ruvnet
- GitHub username: ruvnet
- License: MIT
- Repository: https://github.com/ruvnet/claude-flow/tree/main/.claude/skills/agentdb-vector-search
- Ref: dbe0e719813583400773166a0621f8a9b8185c72
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network
- Quality score: 75
- Public page: https://skillstore.pages.dev/skills/ruvnet-agentdb-vector-search
- Manifest: https://skillstore.pages.dev/api/skills/ruvnet-agentdb-vector-search/manifest

## Capabilities

- Shows how to initialize AgentDB vector databases with different embedding dimensions.
- Provides CLI examples for similarity queries, thresholds, metrics, import, export, and statistics.
- Demonstrates TypeScript adapter setup for embeddings, metadata, and reasoning retrieval.
- Explains RAG context retrieval patterns with Maximal Marginal Relevance support.
- Covers quantization choices, HNSW indexing, caching, and basic troubleshooting.

## Use Cases

- Prototype RAG Search: Create a small semantic search database for documentation, support articles, or internal notes.
- Tune Retrieval Quality: Compare similarity thresholds, distance metrics, and MMR settings for a retrieval workflow.
- Expose Search Through MCP: Connect AgentDB search tools to Claude Code for interactive project knowledge retrieval.

## Prompt Templates

### Create a Starter Database

```
Set up an AgentDB vector database for my project documents. Use the right embedding dimension and explain the first query I should run.
```

### Design a RAG Flow

```
Design a RAG workflow using AgentDB. Include document storage, embedding creation, retrieval settings, and answer generation with cited context.
```

### Improve Search Relevance

```
Review my AgentDB semantic search configuration. Recommend threshold, metric, metadata filters, and MMR settings for better result quality.
```

### Plan a Production Rollout

```
Create a production plan for AgentDB vector search. Cover indexing, quantization, monitoring, backups, access controls, and benchmark validation.
```

## Limitations

- Requires AgentDB or agentic-flow packages to be available in the project environment.
- Requires an embedding provider, such as OpenAI or a compatible local model.
- Does not provide production access control, backup policy, or data retention guidance.
- Performance claims should be verified with project data and hardware.

## Best Practices

- Pin AgentDB package versions before running CLI or MCP setup commands.
- Keep embedding dimensions consistent between stored vectors and query vectors.
- Benchmark retrieval quality and latency with representative project data.

## Anti Patterns

- Do not store secrets or regulated data in sample vector databases.
- Do not mix embeddings from different models in the same index without migration.
- Do not trust benchmark numbers until they are reproduced in your environment.

## Security Audit

- Audited at: 2026-07-20T03:43:29.344\+00:00
- Summary: The skill is documentation for AgentDB vector search. Most static matches are Markdown fences, TypeScript syntax, local database paths, or reference links and are false positives. Commands using npx agentdb@latest are confirmed medium-risk because they instruct users to download and execute an unpinned package; no prompt injection or data-exfiltration intent was found.

## Stats

- Views: 279
- Downloads: 12
- Favorites: 0
- Popularity score: 0