How to Combine Multiple FAISS Indexes into One for a Single Retriever

Faiss is a powerful library for similarity search and clustering of dense vectors, widely used in various applications such as information retrieval and recommendation systems. In scenarios where we have multiple FAISS indexes and we want to combine them into a single retriever, the following steps can be followed.

Prerequisites

Before we dive into the process, make sure you have the following set up:

• FAISS library installed
• Python environment with necessary packages

Steps to Combine Multiple FAISS Indexes

1. Initialize a new empty FAISS index that will serve as the combined index:

import faiss

combined_index = faiss.Index()

2. Load the individual FAISS indexes that you want to combine:

index_1 = faiss.read_index('index_1.index')
index_2 = faiss.read_index('index_2.index')

3. Retrieve the total number of vectors in each individual index:

n_vectors_1 = index_1.ntotal
n_vectors_2 = index_2.ntotal

4. Resize the combined index to accommodate the total number of vectors:

combined_index.reserve(n_vectors_1 + n_vectors_2)

5. Append the vectors from the first index to the combined index:

combined_index.add(index_1.reconstruct_n(0, n_vectors_1))

6. Append the vectors from the second index to the combined index:

combined_index.add(index_2.reconstruct_n(0, n_vectors_2))

7. Optimize the combined index for better performance:

combined_index.optimize()

8. Save the combined index for future use:

faiss.write_index(combined_index, 'combined_index.index')

By following these steps, you can successfully combine multiple FAISS indexes into one, creating a single retriever that can be used for efficient similarity search.

For more information and advanced usage of FAISS, you can refer to the official documentation: FAISS Documentation.

Additional resources:

• Introduction to FAISS: A Practical Guide
• FAISS: A Library for Efficient Similarity Search

With these resources, you can further explore the capabilities of FAISS and enhance your understanding of how to leverage it for your specific use cases.

Remember to install the necessary dependencies and follow the guidelines provided in the documentation for a smooth integration of FAISS into your projects. Happy coding!