---
title: "FAISS"
description: "Use Facebook FAISS as a high-performance vector store in Mem0, optimized for memory usage and fast similarity search."
---
[FAISS](https://github.com/facebookresearch/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

```python
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:

```bash
# 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.
