TinkerBird is a browser-native vector database for efficient storage and retrieval of high-dimensional vectors, targeting developers building client-side AI applications. It enables fast, local vector search without server-side roundtrips, enhancing privacy and reducing costs by keeping sensitive data within the user's browser.

How It Works

TinkerBird utilizes HNSW (Hierarchical Navigable Small World) indexes for fast approximate nearest neighbor search. Its storage layer is built on IndexedDB, leveraging its stability and familiarity for offline-first workflows. This architecture co-locates data and embeddings, eliminating network latency and server dependencies for vector search operations.

Quick Start & Requirements

• Installation: Typically via npm or yarn.
• Prerequisites: Browser environment supporting IndexedDB.
• Resources: Browser memory and storage capacity are the primary constraints.
• Links: Sample App, Source

Highlighted Details

• Browser-native vector database.
• Uses HNSW for efficient vector retrieval.
• Leverages IndexedDB for storage.
• Focuses on client-side, offline-first vector search.

Maintenance & Community

Contributions are welcomed via suggestions and bug reports.

Licensing & Compatibility

Distributed under the MIT License. Permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

Performance and storage capacity are limited by the user's browser environment. The project is provided "as is" with no guarantees.