Vectra
A local, file-backed vector database with cross-language gRPC access.
Vectra works like a local Pinecone or Qdrant: each index is a folder on disk, loaded into memory for fast queries. Filter by metadata using MongoDB-style operators, rank by cosine similarity, and get results in under a millisecond for small indexes.
Key capabilities
| Zero infrastructure | Everything lives in a local folder — no servers or managed services |
| Fast lookups | Sub-millisecond to low-millisecond latency |
| Pinecone-style filtering | MongoDB query operators for metadata filtering |
| Multiple embeddings | OpenAI, Azure, OSS endpoints, or local HuggingFace models (no API key) |
| Pluggable storage | Filesystem, IndexedDB (browsers), in-memory, or custom backends |
| Browser & Electron | Dedicated vectra/browser entry point with local embeddings and IndexedDB |
| CLI included | Manage indexes, serve gRPC, watch folders from the terminal |
| Cross-language | Built-in gRPC server with bindings for Python, C#, Rust, Go, Java, TypeScript |
| Flexible format | JSON (human-readable) or Protocol Buffers (40-50% smaller) |
Choose your path
| I want to… | Start here |
|---|---|
| Store items with vectors I generate myself | Getting Started — LocalIndex |
| Ingest raw text and let Vectra chunk + embed | Getting Started — LocalDocumentIndex |
| Run in a browser or Electron | Storage — Running in the Browser |
| Access Vectra from Python, C#, Rust, etc. | gRPC Server |
| Use the CLI without writing code | CLI Reference |
What’s New in v0.14.x
- Browser & Electron support —
vectra/browserentry point withIndexedDBStorageandTransformersEmbeddings - Local embeddings —
LocalEmbeddingsandTransformersEmbeddingsrun HuggingFace models with no API key - Protocol Buffers — opt-in binary format, 40-50% smaller files
- gRPC server —
vectra serveexposes 19 RPCs for cross-language access - FolderWatcher — auto-sync directories into a document index
- Language bindings —
vectra generatescaffolds clients for 6 languages
See the Changelog for breaking changes and migration details.
Documentation
| Guide | Description |
|---|---|
| Getting Started | Install, requirements, quick start with both index types |
| Core Concepts | Index types, metadata filtering, on-disk layout, storage backends |
| Embeddings Guide | Choose and configure an embeddings provider |
| Document Indexing | Chunking, retrieval, hybrid search, FolderWatcher |
| CLI Reference | All CLI commands, flags, and embeddings provider config |
| API Reference | TypeScript API overview with links to typedoc |
| Best Practices | Performance tuning, operational tips, troubleshooting |
| Storage | Pluggable backends, custom storage, browser/IndexedDB, formats |
| gRPC Server | Cross-language access, service API, language bindings |
| Changelog | Breaking changes, migration guides, version compatibility |
| Tutorials | End-to-end walkthroughs: RAG pipeline, browser app, gRPC, custom storage, folder sync |