𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗙𝗔𝗜𝗦𝗦: 𝟰𝟯𝟬𝘅 𝗦𝗽𝗲𝗲𝗱𝘂𝗽 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗱

FAISS

When working with image-based recommendation systems, managing a large number of image embeddings can quickly become computationally intensive. During inference, calculating distances between a query vector and every other vector in the database leads to high latency — especially at scale.

To address this, I implemented 𝗙𝗔𝗜𝗦𝗦 (𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸 𝗔𝗜 𝗦𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗦𝗲𝗮𝗿𝗰𝗵) in a recent project at Vizuara. FAISS significantly reduces latency with only a minimal drop in accuracy, making it a powerful solution for high-dimensional similarity search.

FAISS operates on two key indexing strategies:

𝗜𝗻𝗱𝗲𝘅𝗙𝗹𝗮𝘁𝗟𝟮: Performs exact L2 distance matching, much faster than brute-force methods.

𝗜𝗻𝗱𝗲𝘅𝗜𝗩𝗙 (𝗜𝗻𝘃𝗲𝗿𝘁𝗲𝗱 𝗙𝗶𝗹𝗲 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴): Groups similar features into clusters, allowing searches within only the most relevant subsets — massively improving efficiency.

In our implementation, we achieved a 𝟰𝟯𝟬𝘅 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 with only a 𝟮% 𝗱𝗲𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆. This clearly demonstrates the value of trading off a small amount of precision for substantial performance gains.

To help others understand how FAISS works, I created a simple, visual animation and made the source code publicly available: https://github.com/pritkudale/Code_for_LinkedIn/blob/main/FAISS_Animation.ipynb

For more AI and machine learning insights, check out 𝗩𝗶𝘇𝘂𝗮𝗿𝗮’𝘀 𝗔𝗜 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://www.vizuaranewsletter.com/?r=502twn