> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentfront.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Welcome to VectoriaDB

> Lightweight in-memory vector database for semantic search with offline embeddings

VectoriaDB is a production-ready in-memory vector database built on transformers.js. Use it to surface the right tool, prompt, or document snippet from natural-language queries without shipping data to an external service.

<Info>
  VectoriaDB runs entirely offline - your data never leaves the server, and you avoid API quotas or rate limits.
</Info>

## Features

<CardGroup cols={3}>
  <Card title="Offline Embeddings" icon="microchip">
    Embeddings run locally via transformers.js, so your data never leaves the server and you avoid API quotas.
  </Card>

  <Card title="Type-safe Metadata" icon="shield-check">
    Strong generics ensure every document you index keeps the same shape as your metadata interface.
  </Card>

  <Card title="Operational Guardrails" icon="gauge">
    Built-in rate limits, batch validation, HNSW indexing, and storage adapters keep the index production ready.
  </Card>
</CardGroup>

## When to Use VectoriaDB

* **Tool discovery** - Surface the right tool from natural-language queries
* **Document search** - Semantic search over documents, prompts, or code snippets
* **Recommendation systems** - Find similar items based on text embeddings
* **Offline-first applications** - No external API dependencies

<Note>
  The default Xenova `all-MiniLM-L6-v2` model is \~22 MB. The first initialization downloads and caches it under `cacheDir`; subsequent boots reuse the local copy.
</Note>

## Core Concepts

### Documents

Each document has:

* **id** - Unique identifier
* **text** - Natural language text to embed
* **metadata** - Type-safe custom data

### Embeddings

VectoriaDB generates embeddings locally using transformers.js. The default model is `all-MiniLM-L6-v2` which provides good quality with fast inference.

### Similarity Search

Search returns documents ranked by cosine similarity to your query. You can filter results by metadata and set minimum similarity thresholds.

## Next Steps

<CardGroup cols={2}>
  <Card title="Installation" icon="download" href="/vectoriadb/get-started/installation">
    Install VectoriaDB in your project
  </Card>

  <Card title="Quickstart" icon="rocket" href="/vectoriadb/get-started/quickstart">
    Build your first semantic search
  </Card>

  <Card title="Tool Discovery" icon="wand-magic-sparkles" href="/vectoriadb/guides/use-cases/tool-discovery">
    Complete tool discovery guide
  </Card>

  <Card title="API Reference" icon="code" href="/vectoriadb/api-reference/overview">
    Explore the full API
  </Card>
</CardGroup>