Composite Indexes and the Faiss Index Factory
In the world of vector search, there are many indexing methods and vector processing techniques that allow us to prioritize between recall, latency, and memory usage.

Using specific methods such as IVF, PQ, or HNSW, we can often return good results. But for best performance we will usually want to use composite indexes.

We can view a composite index as a step-by-step process of vector transformations and one or more indexing methods. Allowing us to place multiple indexes and/or processing steps together to create our ‘ideal’ index.

For example, we can use an inverted file (IVF) index to reduce the scope of our search (increasing search speed), and then add a compression technique such as product quantization (PQ) to keep larger indexes within a reasonable size limit.

Where there is the ability to customize indexes, there is the risk of producing indexes with unnecessarily poor recall, latency, or memory usage.

We must know how composite indexes work if we want to build robust and high-performance vector similarity search applications. It is essential to understand where different indexes or vector transformations can be used — and when they are not needed.

Part 2: https://youtu.be/3Wqh4iUupbM

🌲 Pinecone article:
https://www.pinecone.io/learn/composite-indexes/

🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5

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00:00 Intro
01:54 Composite Indexes
06:43 Faiss Index Factory
11:34 Why we use Index Factory
17:11 Outro