# inline::faiss ## Description [Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It allows you to store and query vectors directly in memory. That means you'll get fast and efficient vector retrieval. ## Features - Lightweight and easy to use - Fully integrated with Llama Stack - GPU support - **Vector search** - FAISS supports pure vector similarity search using embeddings ## Search Modes **Supported:** - **Vector Search** (`mode="vector"`): Performs vector similarity search using embeddings **Not Supported:** - **Keyword Search** (`mode="keyword"`): Not supported by FAISS - **Hybrid Search** (`mode="hybrid"`): Not supported by FAISS > **Note**: FAISS is designed as a pure vector similarity search library. See the [FAISS GitHub repository](https://github.com/facebookresearch/faiss) for more details about FAISS's core functionality. ## Usage To use Faiss in your Llama Stack project, follow these steps: 1. Install the necessary dependencies. 2. Configure your Llama Stack project to use Faiss. 3. Start storing and querying vectors. ## Installation You can install Faiss using pip: ```bash pip install faiss-cpu ``` ## Documentation See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for more details about Faiss in general. ## Configuration | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | | ## Sample Configuration ```yaml kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db ```