What is FAISS and it’s uses?

FAISS (Facebook AI Similarity Search) is an open-source, vector database library that enables efficient storage, retrieval, and similarity search of dense vector embeddings. It overcomes traditional query search engine limitations, allowing for:

• Fast and accurate similarity searches
• Scalable and effective matching of complex data
• Compact and optimized indexing for efficient retrieval of relevant data chunks

Ideal for applications requiring fast and accurate matching, FAISS is a powerful tool for searching and retrieving similar multimedia documents, such as images, videos, and text.

What is Similarity Search?

Similarity search is a process that finds the most similar vectors to a given query vector in a dataset. Faiss is a library that enables efficient similarity search by building a data structure (index) in RAM, allowing for fast and accurate searches.

Key Features of Faiss:

1. Nearest Neighbor Search: Finds the closest vector to a query vector using Euclidean distance (L2).
2. K-Nearest Neighbors: Returns the top k nearest neighbors to a query vector.
3. Batch Processing: Searches multiple vectors at once for faster performance.
4. Trade-off between Precision and Speed: Allows for faster searches with reduced precision.
5. Maximum Inner Product Search: Finds the vector with the highest inner product with a query vector.
6. Range Search: Returns all vectors within a specified radius of a query vector.
7. Disk Storage: Stores the index on disk for larger datasets.
8. Binary Vector Support: Indexes binary vectors for efficient search.
9. Predicate-based Filtering: Ignores specific vectors based on a custom predicate.

In summary, Faiss is a powerful library for similarity search that offers various features for efficient and accurate searches, including nearest neighbor search, batch processing, and trade-offs between precision and speed.

Choosing Between CPU and GPU Versions of Faiss

When installing Faiss, you have two options: CPU and GPU versions. The main difference between them is:

• CPU Version: Uses the processor for computations, suitable for systems without a dedicated GPU.
• GPU Version: Leverages the graphics card for parallel processing, ideal for large-scale datasets and intensive numerical calculations.

How to Decide:

• If you have a system with a GPU and work with large datasets, choose the GPU Version for faster performance.
• If you don’t have a GPU or work with smaller datasets, the CPU Version is a reliable choice.