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https://github.com/m1guelpf/tinyvector

A tiny embedding database in pure Rust.
https://github.com/m1guelpf/tinyvector

embeddings embeddings-similarity machine-learning rust search-engines similarity-search vector-database vector-search

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A tiny embedding database in pure Rust.

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    tinyvector - a tiny embedding database in pure Rust 



       






## ✨ Features

- **Tiny**: It's in the name. It's literally just an axum server. Extremely easy to customize, around 600 lines of code.

- **Fast**: Tinyvector _should_ have comparable speed to advanced vector databases when it comes on small to medium datasets, and slightly better accuracy.

- **Vertically Scales**: Tinyvector stores all indexes in memory for fast querying. Very easy to scale up to 100 million+ vector dimensions without issue.

- **Open Source**: MIT Licensed, free forever.



### Soon

- **Powerful Queries**: Allow filtering by the provided vector metadata without slowing the search down.

- **Integrated Models**: Soon you won't have to bring your own vectors, just generate them on the server automaticaly. Aiming to support support SBert, Hugging Face models, OpenAI, Cohere, etc.

- **Typescript/Python Libraries**: Should be able to auto-generate pretty good clients using the included OpenAPI schema.



## 🚀 Getting Started



### 🐳 Docker



We provide a lightweight Docker container that you can run anywhere. It only takes one command to get up and running with the latest changes:



```sh

docker run \

  -p 8000:8000 \

  ghcr.io/m1guelpf/tinyvector:edge

```



> **Note**

> When running via Docker Compose or Kubernetes, make sure to bind a volume to `/tinyvector/storage` for persistence. This is handled automatically in the command above.



### 🛠️ Building from scratch



You can build tinyvector from the latest tagged release by running `cargo install tinyvector` (you might need to [install Rust](https://rustup.rs/) first). Then, run `tinyvector` to start up the server.

 

You can also build it from the latest commit by cloning the repo and running `cargo build --release`, and run it with `./target/release/tinyvector`.



## 💡 Why use tinyvector?



Most vector databases are overkill for simple setups. For example:

- Using embeddings to chat with your documents. Most document search is nowhere close to what you'd need to justify accelerating search speed with [HNSW](https://github.com/nmslib/hnswlib) or [FAISS](https://github.com/facebookresearch/faiss).

- Doing search for your website or store. Unless you're selling 1,000,000 items, you don't need Pinecone.



## 🧩 Embeddings?



Embeddings are a way to compare similar things, in the same way humans compare similar things, by converting text into a small list of numbers. Similar pieces of text will have similar numbers, different ones have very different numbers.



Read OpenAI's [explanation](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).



## 🙏 Acknowledgements



- Will Depue's [tinyvector](https://twitter.com/willdepue/status/1675796236304252928) (python+sqlite+numpy) inspired me to build a vector database from scratch (and borrow the name). Will also contributed plenty of ideas to optimize performance.



## 📄 License



This project is open-sourced under the MIT license. See [the License file](LICENSE) for more information.