FAISS

Use Facebook FAISS as a high-performance vector store in Mem0, optimized for memory usage and fast similarity search.

FAISS is a library for efficient similarity search and clustering of dense vectors. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data. FAISS is optimized for memory usage and search speed, making it an excellent choice for production environments.

Usage

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "faiss",
        "config": {
            "collection_name": "test",
            "path": "/tmp/faiss_memories",
            "distance_strategy": "euclidean"
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})

Installation

To use FAISS in your mem0 project, you need to install the appropriate FAISS package for your environment:

# For CPU version
pip install faiss-cpu

# For GPU version (requires CUDA)
pip install faiss-gpu

Config

Here are the parameters available for configuring FAISS:

Parameter	Description	Default Value
collection_name	The name of the collection	mem0
path	Path to store FAISS index and metadata	/tmp/faiss/<collection_name>
distance_strategy	Distance metric strategy to use (options: ‘euclidean’, ‘inner_product’, ‘cosine’)	euclidean
normalize_L2	Whether to normalize L2 vectors (only applicable for euclidean distance)	False
embedding_model_dims	Dimensions of the embedding model	1536

Performance Considerations

FAISS offers several advantages for vector search:

1. Efficiency: FAISS is optimized for memory usage and speed, making it suitable for large-scale applications.
2. Offline Support: FAISS works entirely locally, with no need for external servers or API calls.
3. Storage Options: Vectors can be stored in-memory for maximum speed or persisted to disk.
4. Multiple Index Types: FAISS supports different index types optimized for various use cases (though mem0 currently uses the basic flat index).

Distance Strategies

FAISS in mem0 supports three distance strategies:

• euclidean: L2 distance, suitable for most embedding models
• inner_product: Dot product similarity, useful for some specialized embeddings
• cosine: Cosine similarity, best for comparing semantic similarity regardless of vector magnitude

When using cosine or inner_product with normalized vectors, you may want to set normalize_L2=True for better results.