Using FAISS for Local Vector Search

1. Introduction

In this lesson, we will explore how to use Facebook's FAISS (Facebook AI Similarity Search) library for local vector search. FAISS is designed to efficiently search for similar vectors in large datasets, making it an essential tool in the realm of vector databases.

2. What is FAISS?

FAISS is a library developed by Facebook AI Research that enables efficient similarity search and clustering of dense vectors. It provides functionalities for:

• Indexing large datasets of vectors.
• Performing fast nearest neighbor search.
• Supporting various distance metrics.

3. Installation

To install FAISS, you can use pip. Depending on your system and whether you want GPU support, use one of the following commands:

pip install faiss-cpu  # For CPU version
pip install faiss-gpu  # For GPU version
        

4. Indexing

Indexing is a crucial step in using FAISS, allowing you to prepare your vector data for efficient searching. Below is a step-by-step guide on how to create an index and add vectors.

4.1 Creating an Index

FAISS offers several types of indices. For simplicity, we will use the IndexFlatL2 for L2 distance. Here’s how to create an index:

import numpy as np
import faiss

# Dimension of the vector space
d = 128

# Create a random dataset of vectors
n = 1000  # Number of vectors
xb = np.random.random((n, d)).astype('float32')

# Create the index
index = faiss.IndexFlatL2(d)  # L2 distance
index.add(xb)  # Add the vectors to the index
        

In the code above, we created a dataset of 1000 random vectors in a 128-dimensional space and added them to the index.

4.2 Saving and Loading Index

You can save your index to disk and load it later:

faiss.write_index(index, 'index_file.index')  # Save index
loaded_index = faiss.read_index('index_file.index')  # Load index
        

5. Searching

After indexing, you can perform searches to find the nearest neighbors of a query vector.

5.1 Performing Search

Here’s how to search for the top 5 nearest neighbors:

# Create a random query vector
xq = np.random.random((1, d)).astype('float32')

# Perform the search
k = 5  # Number of nearest neighbors
D, I = index.search(xq, k)  # D: distances, I: indices of nearest neighbors
print(I)  # Print indices of nearest neighbors
        

6. Best Practices

Here are some best practices to optimize your use of FAISS:

• Choose the right index type based on your dataset and query requirements.
• Use batch processing for adding or searching multiple vectors.
• Experiment with different distance metrics to find the best fit for your application.

7. FAQ