# Faiss-rs
[](https://crates.io/crates/faiss)
[](https://travis-ci.org/Enet4/faiss-rs)
[](https://deps.rs/repo/github/Enet4/faiss-rs)
This project provides Rust bindings to [Faiss](https://github.com/facebookresearch/faiss),
the state-of-the-art vector search and clustering library.
## Installing as a dependency
Currently, this crate does not build Faiss automatically for you. The dynamic library needs to be installed manually to your system.
1. Follow the instructions [here](https://github.com/Enet4/faiss/tree/c_api_head/INSTALL.md) to build Faiss. The latest `master` branch should suffice, but in the event that it doesn't build properly, consider building Faiss from [this fork, `c_api_head` branch](https://github.com/Enet4/faiss/tree/c_api_head), which will contain the latest bindings to the C interface.
2. Afterwards, follow the instructions on [building the C API of Faiss](https://github.com/Enet4/faiss/tree/c_api_head/c_api/INSTALL.md). This will result in the dynamic library `faiss_c`, which needs to be installed in a place where your system will pick up (in Linux, try somewhere in the `LD_LIBRARY_PATH` environment variable, such as "/usr/lib", or try adding a new path to this variable). For GPU support, don't forget to build and install `gpufaiss_c` instead.
3. You are now ready to include this crate as a dependency:
```toml
[dependencies]
"faiss" = "0.6.0"
```
If you have built Faiss with GPU support, you can include the "gpu" feature in the bindings:
```toml
[dependencies]
"faiss" = {version = "0.6.0", features = ["gpu"]}
```
## Using
A basic example is seen below. Please check out the [documentation](https://docs.rs/faiss) for more.
```rust
use faiss::{Index, index_factory, MetricType};
let mut index = index_factory(64, "Flat", MetricType::L2)?;
index.add(&my_data)?;
let result = index.search(&my_query, 5)?;
for (i, (l, d)) in result.labels.iter()
.zip(result.distances.iter())
.enumerate()
{
println!("#{}: {} (D={})", i + 1, *l, *d);
}
```
## License and attribution notice
Licensed under either of
* Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or <http://www.apache.org/licenses/LICENSE-2.0>)
* MIT license ([LICENSE-MIT](LICENSE-MIT) or <http://opensource.org/licenses/MIT>)
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any
additional terms or conditions.
This work is not affiliated with Facebook AI Research or the main Faiss software.