Using FAISS for Retrieval

FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. It plays a crucial role in Retrieval-Augmented Generation (RAG) systems by enabling fast retrieval of relevant information.

FAISS allows for:

• Efficient similarity search in high-dimensional spaces.
• Support for multiple indexing methods.
• Scalability to billions of vectors.

It is particularly useful when working with embeddings from models such as BERT or other neural networks.

To install FAISS, use the following command:

pip install faiss-cpu

pip install faiss-gpu

4.1 Creating an Index

To create an index, start by importing FAISS and preparing your data:

import faiss
import numpy as np

# Sample data
data = np.random.random((1000, 128)).astype('float32')

# Create a FAISS index
index = faiss.IndexFlatL2(128)  # L2 distance
index.add(data)  # Add vectors to the index

After indexing, you can search for similar vectors:

# Query vector
query_vector = np.random.random((1, 128)).astype('float32')

# Search for 5 nearest neighbors
D, I = index.search(query_vector, 5)  # D = distances, I = indices
print(I)  # Output indices of nearest neighbors

When using FAISS, consider the following best practices:

• Choose the appropriate index type based on your data size and dimensionality.
• Normalize your vectors if using cosine similarity.
• Experiment with different search parameters for optimal performance.