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README.md

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An extremely small, "fast enough" vector search SQLite extension that runs
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anywhere! A successor to [sqlite-vss](https://github.com/asg017/sqlite-vss)
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> [!IMPORTANT] > _`sqlite-vec` is a work-in-progress and not ready for general usage! I plan to launch a "beta" version in the next month or so. Watch this repo for updates, and read [this blog post](https://alexgarcia.xyz/blog/2024/building-new-vector-search-sqlite/index.html) for more info._
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> [!IMPORTANT]
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> _`sqlite-vec` is a work-in-progress and not ready for general usage! I plan to launch a "beta" version in the next month or so. Watch this repo for updates, and read [this blog post](https://alexgarcia.xyz/blog/2024/building-new-vector-search-sqlite/index.html) for more info._
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- Store and query float, int8, and binary vectors in `vec0` virtual tables
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- Pre-filter vectors with `rowid IN (...)` subqueries
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- Written in pure C, no dependencies,
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runs anywhere SQLite runs (Linux/MacOS/Windows, in the browser with WASM,
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Raspberry Pis, etc.)
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- Written in pure C, no dependencies, runs anywhere SQLite runs
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(Linux/MacOS/Windows, in the browser with WASM, Raspberry Pis, etc.)
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<p align="center">
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<img src="./.github/logos/mozilla.svg" width=400 />
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## Roadmap
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Not currently implemented, but planned in the future (after initial `v0.1.0` version):
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Not currently implemented, but planned in the future (after initial `v0.1.0`
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version):
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- Approximate nearest neighbors search (DiskANN, IVF, maybe HNSW?)
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- Metadata filtering + custom internal partitioning
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- More vector types (float16, int16, sparse, etc.) and distance functions
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Additionally, there will be pre-compiled and pre-packaged packages of `sqlite-vec` for the following platforms:
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Additionally, there will be pre-compiled and pre-packaged packages of
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`sqlite-vec` for the following platforms:
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- `pip` for Python
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- `npm` for Node.js / Deno / Bun
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## Sponors
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Development of `sqlite-vec` is supported by multiple generous sponsors! Mozilla is the main sponsor through the new Builders project.
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Development of `sqlite-vec` is supported by multiple generous sponsors! Mozilla
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is the main sponsor through the new Builders project.
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<p align="center">
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<img src="./.github/logos/mozilla.svg" width=400 />
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</p>
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`sqlite-vec` is also sponsored by the following companies:
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<a href="https://fly.io/"><img src="./.github/logos/flyio.svg" width=33%/></a> <a href="https://turso.tech/"><img src="./.github/logos/turso.svg" width=33%/> </a> <a href="https://sqlitecloud.io/"><img src="./.github/logos/sqlitecloud.svg" width=33%/></a>
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<a href="https://fly.io/"><img src="./.github/logos/flyio.svg" width=33%/></a>
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<a href="https://turso.tech/"><img src="./.github/logos/turso.svg" width=33%/>
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</a>
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<a href="https://sqlitecloud.io/"><img src="./.github/logos/sqlitecloud.svg" width=33%/></a>
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As well as multiple individual supporters on [Github sponsors](https://github.com/sponsors/asg017/)!
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As well as multiple individual supporters on
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[Github sponsors](https://github.com/sponsors/asg017/)!
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If your company interested in sponsoring `sqlite-vec` development, send me an email to get more info: https://alexgarcia.xyz
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If your company interested in sponsoring `sqlite-vec` development, send me an
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email to get more info: https://alexgarcia.xyz
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## See Also
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- [**`sqlite-ecosystem`**](https://github.com/asg017/sqlite-ecosystem), Maybe more 3rd party SQLite extensions I've developed
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- [**`sqlite-rembed`**](https://github.com/asg017/sqlite-rembed), Generate text embeddings from remote APIs like OpenAI/Nomic/Ollama, meant for testing and SQL scripts
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- [**`sqlite-lembed`**](https://github.com/asg017/sqlite-lembed), Generate text embeddings locally from embedding models in the `.gguf` format
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- [**`sqlite-ecosystem`**](https://github.com/asg017/sqlite-ecosystem), Maybe
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more 3rd party SQLite extensions I've developed
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- [**`sqlite-rembed`**](https://github.com/asg017/sqlite-rembed), Generate text
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embeddings from remote APIs like OpenAI/Nomic/Ollama, meant for testing and
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SQL scripts
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- [**`sqlite-lembed`**](https://github.com/asg017/sqlite-lembed), Generate text
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embeddings locally from embedding models in the `.gguf` format

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