matsui528/faiss_tips: Some useful tips for faiss
github.com · 2,651 words · saved by 1 readers
Some useful tips for faiss. Contribute to matsui528/faiss_tips development by creating an account on GitHub.
Faiss tips Some useful tips for faiss We suppose faiss is installed via conda: conda install -c pytorch faiss-cpu conda install -c pytorch -c nvidia faiss-gpu If you want to build faiss from source, see: instruction script example of github actions: [ code ][ result ] If you want to add your class to faiss, see this Nearest neighbor search (CPU) The most basic nearest neighbor search by L2 distance. This is much faster than scipy. You should first try this, especially when the database is relatively small (N<10^6). The search is automatically paralellized. import faiss import numpy as np D = 1
related reading
- Faiss: A library for efficient similarity search - Engineering at Metaengineering.fb.com
- GitHub - facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.github.com
- Getting started · facebookresearch/faiss Wiki · GitHubgithub.com
- Welcome to Faiss Documentation - Faiss documentationfaiss.ai
- Indexing 1T vectors · facebookresearch/faiss Wiki · GitHubgithub.com
- Lower memory footprint · facebookresearch/faiss Wiki · GitHubgithub.com
- Faiss indexes · facebookresearch/faiss Wiki · GitHubgithub.com
- plippe/faiss-web-service - Docker Imagehub.docker.com
- Guidelines to choose an index · facebookresearch/faiss Wiki · GitHubgithub.com
- Faster search · facebookresearch/faiss Wiki · GitHubgithub.com
- The vector database to build knowledgeable AI | Pineconepinecone.io
- Hierarchical Navigable Small Worlds (HNSW) | Pineconepinecone.io