Build local vector retrieval indexes with Faiss

Build local vector retrieval indexes with Faiss

Use Faiss to create and query local vector indexes for agent retrieval and RAG workflows before adding heavier managed vector infrastructure.

Prerequisites

Faiss, embeddings, Python or native runtime bindings

Installation

Requirements and caveats from upstream:

• Some of the methods, like those based on binary vectors and compact quantization codes, solely use a compressed representation of the vectors and do not require to keep the original vectors. This generally comes at th...
• Faiss comes with precompiled libraries for Anaconda in Python, see faiss-cpu, faiss-gpu and [faiss-gpu-cuvs](https://anaconda.org/pyt...

Basic usage or getting-started notes:

• Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains suppo...

• The GPU implementation can accept input from either CPU or GPU memory. On a server with GPUs, the GPU indexes can be used a drop-in replacement for the CPU indexes (e.g., replace IndexFlatL2 with GpuIndexFlatL2) and c...

• Source: https://github.com/facebookresearch/faiss

• Extracted from upstream docs: https://raw.githubusercontent.com/facebookresearch/faiss/HEAD/README.md

Documentation

• https://github.com/facebookresearch/faiss

Source

• Agent Skill Exchange