Google AI Blog: Announcing ScaNN: Efficient Vector Similarity Search

Posted by Philip Sun, Software Engineer, Google Research Suppose one wants to search through a large dataset of literary works using que...

Announcing ScaNN: Efficient Vector Similarity Search Skip to main content Announcing ScaNN: Efficient Vector Similarity Search July 28, 2020 Posted by Philip Sun, Software Engineer, Google Research Quick links Share Copy link × Suppose one wants to search through a large dataset of literary works using queries that require an exact match of title, author, or other easily machine-indexable criteria. Such a task would be well suited for a relational database using a language such as SQL. However, if one wants to support more abstract queries, such as “Civil War poem,” it is no longer possible to

saved by

• Liam Hinzman
• Jonathan Xu
• Amelia Wattenberger

related reading

• Faiss: A library for efficient similarity search - Engineering at Metaengineering.fb.com
• Building a web search engine from scratch in two months with 3 billion neural embeddingsblog.wilsonl.in
• TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
• Not All Vector Databases Are Made Equal | Towards Data Sciencetowardsdatascience.com
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnettransformer-circuits.pub
• HNSWlib vs ScaNN on Vector Search - Zilliz blogzilliz.com
• TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
• 1301.3781arxiv.org
• Hierarchical Navigable Small Worlds (HNSW) | Pineconepinecone.io
• On the Theoretical Limitations of Embedding-Based Retrievalarxiv.org
• [2508.21038] On the Theoretical Limitations of Embedding-Based Retrievalarxiv.org
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnettransformer-circuits.pub