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https://github.com/wizenheimer/tinkerbird

Chrome Native Vector Database
https://github.com/wizenheimer/tinkerbird

chrome hnsw indexeddb typescript vector-database

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Chrome Native Vector Database

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# 
TinkerBird



TinkerBird is a browser native vector database designed for efficient storage and

retrieval of high-dimensional vectors (embeddings). It's query engine, written in

TypeScript, leverages HNSW (Hierarchical Navigable Small World) indexes for fast

vector retrieval. The storage layer utilizes IndexedDB, which could be extended

with an lru-cache.



By co-locating data and embeddings, Tikerbird eliminates the roundtrip and reduces 

reliance on server-side interactions for vector search workloads. With Tinkerbird, 

sensitive data remains local, thus benefiting from vector search, without the associated cost,

compliance and security risks.



TinkerBird uses IndexedDB as it's storage layer, which in turn builds upon Blobs

and LevelDB storage systems. By using Indexeddb, it benefits from IndexedDB's

adoption, stability and familiarity as a native choice for offline first

workflows.



## Examples



Here's a sample app built using TinkerBird. Check out [Tinkerboard](https://tinkerboard.vercel.app/) and [Source](https://github.com/wizenheimer/tinkerboard).



## Contributing



Feel free to contribute to TinkerBird by sending us your suggestions, bug

reports, or cat videos. Contributions are what make the open source community

such an amazing place to be learn, inspire, and create. Any contributions you

make are **greatly appreciated**.



## License



Distributed under the MIT License. See [LICENSE](LICENSE.md) for more information.

TinkerBird is provided "as is" and comes with absolutely no guarantees. We take

no responsibility for irrelevant searches, confused users, or existential crises

induced by unpredictable results. If it breaks, well, that's your problem now! jk.



## References



-   [ANN-Benchmarks](https://github.com/erikbern/ann-benchmarks)

-   [Skip Lists: A Probabilistic Alternative to Balanced Trees](https://15721.courses.cs.cmu.edu/spring2018/papers/08-oltpindexes1/pugh-skiplists-cacm1990.pdf)

-   [Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small Worlds](https://arxiv.org/abs/1603.09320)

-   [Scalable Distributed Algorithm for Approximate Nearest Neighbor Search Problem in High-Dimensional General Metric Spaces](https://www.iiis.org/CDs2011/CD2011IDI/ICTA_2011/PapersPdf/CT175ON.pdf)

-   [A Comparative Study on Hierarchical Navigable Small World Graphs](https://deepai.org/publication/a-comparative-study-on-hierarchical-navigable-small-world-graphs)

-   [HNSW: Hierarchical Navigable Small World graphs](https://proceedings.mlr.press/v119/prokhorenkova20a/prokhorenkova20a.pdf)

-   [HNSW Graphs](https://github.com/deepfates/hnsw/)

-   [Hierarchical Navigable Small World graphs in FAISS](https://github.com/facebookresearch/faiss/blob/main/faiss/impl/HNSW.cpp)

-   [A Comparative Study on Hierarchical Navigable Small World Graphs](https://escholarship.org/content/qt1rp889r9/qt1rp889r9_noSplash_7071690a1d8a4ee71eb95432887d3a8e.pdf)

-   [Hierarchical Navigable Small World (HNSW) for ApproximateNearest Neighbor Search](https://towardsdatascience.com/similarity-search-part-4-hierarchical-navigable-small-world-hnsw-2aad4fe87d37)

-   [Hierarchical Navigable Small Worlds](https://srivatssan.medium.com/hierarchical-navigable-small-worlds-d44d39d91f4b)