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

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Features

Offline Embeddings

Embeddings run locally via transformers.js, so your data never leaves the server and you avoid API quotas.

Type-safe Metadata

Strong generics ensure every document you index keeps the same shape as your metadata interface.

Operational Guardrails

Built-in rate limits, batch validation, HNSW indexing, and storage adapters keep the index production ready.

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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
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.

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Core Concepts

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Documents

Each document has:
  • id - Unique identifier
  • text - Natural language text to embed
  • metadata - Type-safe custom data

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Embeddings

VectoriaDB generates embeddings locally using transformers.js. The default model is all-MiniLM-L6-v2 which provides good quality with fast inference. Search returns documents ranked by cosine similarity to your query. You can filter results by metadata and set minimum similarity thresholds.

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Next Steps

Installation

Install VectoriaDB in your project

Quickstart

Build your first semantic search

Tool Discovery

Complete tool discovery guide

API Reference

Explore the full API