llama-stack-mirror/docs/source/providers/vector_io/inline_faiss.md

inline::faiss

Description

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 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:

pip install faiss-cpu

Documentation

See Faiss' documentation or the 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

kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db