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Status](https://coveralls.io/repos/github/Stevenic/vectra/badge.svg?branch=main)](https://coveralls.io/github/Stevenic/vectra?branch=main)\r\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\r\n[![Agent Ready](https://img.shields.io/badge/Agent-Ready-blue.svg)](#agent-ready)\r\n\r\nVectra 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.\r\n\r\n## What's New in Vectra 0.14+\r\n\r\n- **Browser & Electron support** — `vectra/browser` entry point with `IndexedDBStorage` and `TransformersEmbeddings`\r\n- **Local embeddings** — `LocalEmbeddings` and `TransformersEmbeddings` run HuggingFace models with no API key\r\n- **Protocol Buffers** — opt-in binary format, 40-50% smaller files\r\n- **gRPC server** — `vectra serve` exposes 19 RPCs for cross-language access\r\n- **FolderWatcher** — auto-sync directories into a document index\r\n- **Language bindings** — `vectra generate` scaffolds clients for 6 languages\r\n- **0.15: Performance improvements** — skip-if-unchanged document upserts, shallow-clone transactional snapshots, heap-based top-K query ranking, parallel metadata loads, and O(N) batch chunk deletion\r\n\r\nSee the [Changelog](https://stevenic.github.io/vectra/changelog) for breaking changes and migration details.\r\n\r\n## Install\r\n\r\n```sh\r\nnpm install vectra\r\n```\r\n\r\n## Quick Example\r\n\r\n```ts\r\nimport { LocalDocumentIndex, OpenAIEmbeddings } from 'vectra';\r\n\r\nconst docs = new LocalDocumentIndex({\r\n  folderPath: './my-index',\r\n  embeddings: new OpenAIEmbeddings({\r\n    apiKey: process.env.OPENAI_API_KEY!,\r\n    model: 'text-embedding-3-small',\r\n    maxTokens: 8000,\r\n  }),\r\n});\r\n\r\nif (!(await docs.isIndexCreated())) {\r\n  await docs.createIndex({ version: 1 });\r\n}\r\n\r\nawait docs.upsertDocument('doc://readme', 'Vectra is a local vector database...', 'md');\r\n\r\nconst results = await docs.queryDocuments('What is Vectra?', { maxDocuments: 5 });\r\nif (results.length > 0) {\r\n  const sections = await results[0].renderSections(2000, 1, true);\r\n  console.log(sections[0].text);\r\n}\r\n```\r\n\r\n## Documentation\r\n\r\nFull docs at **[stevenic.github.io/vectra](https://stevenic.github.io/vectra/)**:\r\n\r\n| Guide | Description |\r\n|-------|-------------|\r\n| [Getting Started](https://stevenic.github.io/vectra/getting-started) | Install, requirements, quick start with both index types |\r\n| [Core Concepts](https://stevenic.github.io/vectra/core-concepts) | Index types, metadata filtering, on-disk layout |\r\n| [Embeddings Guide](https://stevenic.github.io/vectra/embeddings) | Choose and configure an embeddings provider |\r\n| [Document Indexing](https://stevenic.github.io/vectra/documents) | Chunking, retrieval, hybrid search, FolderWatcher |\r\n| [CLI Reference](https://stevenic.github.io/vectra/cli) | All CLI commands, flags, and provider config |\r\n| [API Reference](https://stevenic.github.io/vectra/api-reference) | TypeScript API overview |\r\n| [Best Practices](https://stevenic.github.io/vectra/best-practices) | Performance tuning, troubleshooting |\r\n| [Storage](https://stevenic.github.io/vectra/storage) | Pluggable backends, browser/IndexedDB, serialization formats |\r\n| [gRPC Server](https://stevenic.github.io/vectra/grpc) | Cross-language access and language bindings |\r\n| [Changelog](https://stevenic.github.io/vectra/changelog) | Breaking changes and migration guides |\r\n| [Tutorials](https://stevenic.github.io/vectra/tutorials/) | RAG pipeline, browser app, gRPC, custom storage, folder sync |\r\n| [Samples](./samples/) | Runnable examples: quickstart, RAG, browser, SQLite storage, gRPC, folder watcher |\r\n\r\n## Agent Ready\r\n\r\nVectra ships an [`llms.txt`](https://github.com/Stevenic/vectra/blob/main/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:\r\n\r\n```\r\nRead the llms.txt file at https://raw.githubusercontent.com/Stevenic/vectra/main/llms.txt\r\nand then add Vectra support to this project. Use LocalDocumentIndex for document\r\nstorage and retrieval.\r\n```\r\n\r\nThe `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.\r\n\r\n## License\r\n\r\nMIT License. See [LICENSE](LICENSE).\r\n\r\n## Contributing\r\n\r\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. Please review our [Code of Conduct](CODE_OF_CONDUCT.md).\r\n","readmeFilename":"README.md"}