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Version: v0.2.23

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​

FieldTypeRequiredDefaultDescription
kvstoreutils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfigNosqlite

Sample Configuration​

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