Nearest Neighbor Indexes for Similarity Search | Pinecone
Vector similarity search is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles — with incredible accuracy in sub-second timescales for billion+ size datasets.
Nearest Neighbor Indexes for Similarity Search Jump to section Indexes in Search Flat And Accurate Locality Sensitive Hashing Hierarchical Navigable Small World Graphs Inverted File Index Vector similarity search is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles — with incredible accuracy in sub-second timescales for billion+ size datasets. One of the key components to efficient search is flexibility. And for that we have a wide range of search indexes available to us — there is no ‘one-size-fits-all’ in similarity search.
Explore this link on the map →related reading
- Hierarchical Navigable Small Worlds (HNSW) | Pineconepinecone.io
- Faiss: A library for efficient similarity search - Engineering at Metaengineering.fb.com
- Announcing ScaNN: Efficient Vector Similarity Searchai.googleblog.com
- Not All Vector Databases Are Made Equal | Towards Data Sciencetowardsdatascience.com
- GitHub - facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors. · GitHubgithub.com
- Introduction to Facebook AI Similarity Search (Faiss) | Pineconepinecone.io
- Hierarchical Navigable Small Worlds (HNSW) | Pineconepinecone.io
- Welcome to Faiss Documentation - Faiss documentationfaiss.ai
- Semantic Search - Sentence Transformers documentationsbert.net
- Guidelines to choose an index · facebookresearch/faiss Wiki · GitHubgithub.com
- FAISS Python API for fast and efficient similarity search | Learn Pain Lesslearnpainless.com
- Regarding the IndexFlatIP · Issue #1119 · facebookresearch/faiss · GitHubgithub.com