langchain/docs/extras/integrations/providers/facebook_faiss.mdx

# Facebook Faiss

>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) 
> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that 
> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting 
> code for evaluation and parameter tuning.

[Faiss documentation](https://faiss.ai/).


## Installation and Setup

We need to install `faiss` python package.

```bash
pip install faiss-gpu # For CUDA 7.5+ supported GPU's.
```

OR

```bash
pip install faiss-cpu # For CPU Installation
```


## Vector Store

See a [usage example](/docs/integrations/vectorstores/faiss).

```python
from langchain.vectorstores import FAISS
```