Vectra: a local vector database

Vectra is a local, file-backed, in-memory vector database with an optional gRPC server for cross-language access. Each index is a folder on disk — queries use MongoDB-style metadata filtering and cosine similarity ranking, with sub-millisecond latency for small indexes.

What's New in Vectra 0.14

• Browser & Electron support — vectra/browser entry point with IndexedDBStorage and TransformersEmbeddings
• Local embeddings — LocalEmbeddings and TransformersEmbeddings run HuggingFace models with no API key
• Protocol Buffers — opt-in binary format, 40-50% smaller files
• gRPC server — vectra serve exposes 19 RPCs for cross-language access
• FolderWatcher — auto-sync directories into a document index
• Language bindings — vectra generate scaffolds clients for 6 languages

See the Changelog for breaking changes and migration details.

Install

npm install vectra

Quick Example

import { LocalDocumentIndex, OpenAIEmbeddings } from 'vectra';

const docs = new LocalDocumentIndex({
  folderPath: './my-index',
  embeddings: new OpenAIEmbeddings({
    apiKey: process.env.OPENAI_API_KEY!,
    model: 'text-embedding-3-small',
    maxTokens: 8000,
  }),
});

if (!(await docs.isIndexCreated())) {
  await docs.createIndex({ version: 1 });
}

await docs.upsertDocument('doc://readme', 'Vectra is a local vector database...', 'md');

const results = await docs.queryDocuments('What is Vectra?', { maxDocuments: 5 });
if (results.length > 0) {
  const sections = await results[0].renderSections(2000, 1, true);
  console.log(sections[0].text);
}

Documentation

Full docs at stevenic.github.io/vectra:

| Guide | Description | |-------|-------------| | Getting Started | Install, requirements, quick start with both index types | | Core Concepts | Index types, metadata filtering, on-disk layout | | Embeddings Guide | Choose and configure an embeddings provider | | Document Indexing | Chunking, retrieval, hybrid search, FolderWatcher | | CLI Reference | All CLI commands, flags, and provider config | | API Reference | TypeScript API overview | | Best Practices | Performance tuning, troubleshooting | | Storage | Pluggable backends, browser/IndexedDB, serialization formats | | gRPC Server | Cross-language access and language bindings | | Changelog | Breaking changes and migration guides | | Tutorials | RAG pipeline, browser app, gRPC, custom storage, folder sync | | Samples | Runnable examples: quickstart, RAG, browser, SQLite storage, gRPC, folder watcher |

Agent Ready

Vectra ships an llms.txt file that gives coding agents everything they need to integrate Vectra into your project. Point your agent at it and let it do the work:

Read the llms.txt file at https://raw.githubusercontent.com/Stevenic/vectra/main/llms.txt
and then add Vectra support to this project. Use LocalDocumentIndex for document
storage and retrieval.

The llms.txt file covers all exports, index types, CLI commands, gRPC bindings, and on-disk format — enough for any coding agent to scaffold a working integration without browsing docs.

License

MIT License. See LICENSE.

Contributing

See CONTRIBUTING.md for guidelines. Please review our Code of Conduct.