# Build AgentDB Vector Search

Teams need fast semantic retrieval for documents, RAG systems, and knowledge bases. This skill gives AgentDB setup, search patterns, and tuning guidance.

## Install

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

## Metadata

- Status: approved
- Slug: dnyoussef-agentdb-vector-search
- Skillstore revision: r1
- Version status: missing
- Tree hash: 600a88e5a7fd7efce246843d4743c9e491ed9b0a24290784ec0b0c0efcb52fff
- Author: DNYoussef
- GitHub username: DNYoussef
- License: MIT
- Repository: https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/agentdb-vector-search
- Ref: c579efd02184f1128d6741d16e6c8d951d7d4208
- 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: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/dnyoussef-agentdb-vector-search
- Manifest: https://skillstore.pages.dev/api/skills/dnyoussef-agentdb-vector-search/manifest

## Capabilities

- Shows AgentDB CLI commands for initialization, querying, import, export, and statistics.
- Provides TypeScript examples for storing embedded documents and retrieving similar content.
- Explains hybrid search with metadata filters and similarity thresholds.
- Covers RAG retrieval patterns using contextual document results.
- Lists quantization, HNSW indexing, caching, and distance metric options.

## Use Cases

- Build a RAG Knowledge Base: Use AgentDB to store document embeddings, retrieve relevant context, and support answer generation.
- Add Semantic Product Search: Combine vector similarity with metadata filters for product, content, or support search experiences.
- Tune Vector Retrieval Settings: Compare distance metrics, thresholds, quantization choices, and batch operations for retrieval workloads.

## Prompt Templates

### Start a Local Vector Database

```
Use agentdb-vector-search to set up a local AgentDB vector database for [embedding model] with [dimension] dimensions. Show the safest CLI steps and explain each setting.
```

### Add Similarity Search

```
Use agentdb-vector-search to design a similarity search flow for [document type]. Include storage fields, query options, score thresholds, and result handling.
```

### Build a RAG Retrieval Flow

```
Use agentdb-vector-search to create a RAG retrieval plan for [knowledge base]. Include embedding, storage, retrieval, diversity, and context assembly steps.
```

### Tune Hybrid Search at Scale

```
Use agentdb-vector-search to tune an AgentDB index for [scale] vectors. Recommend metric choices, quantization, metadata filters, cache settings, and measurement steps.
```

## Limitations

- Requires Node.js 18\+, AgentDB, and an embedding provider or custom embedding model.
- Does not include runnable project files or automated tests.
- Examples must be adapted to each schema, dimension setting, and privacy requirement.
- Performance results depend on hardware, data size, index settings, and workload.

## Best Practices

- Pin package versions and review third-party CLI packages before running install or MCP commands.
- Match vector dimensions to the embedding model before importing or querying data.
- Use metadata filters and score thresholds to reduce irrelevant retrieval results.

## Anti Patterns

- Running npx commands from untrusted sources without package review.
- Mixing embeddings from different models inside one vector index.
- Treating benchmark figures as guaranteed production performance.

## Security Audit

- Audited at: 2026-07-05T12:35:23.158\+00:00
- Summary: The reviewed SKILL.md is documentation for AgentDB vector search and did not show prompt injection, data exfiltration, or malicious intent. Static findings were false positives from Markdown code fences, local database examples, and documentation links.

## Stats

- Views: 248
- Downloads: 9
- Favorites: 0
- Popularity score: 0