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* SingleStoreDB folder * Readme * Readme with notebook * S2DB notebook * Update README.md * Delete OpenAI_wikipedia semantic_search.ipynb * Add files via upload * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md
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SingleStoreDB has first-class support for vector search through our Vector Functions. Our vector database subsystem, first made available in 2017 and subsequently enhanced, allows extremely fast nearest-neighbor search to find objects that are semantically similar, easily using SQL.
SingleStoreDB supports vectors and vector similarity search using dot_product (for cosine similarity) and euclidean_distance functions. These functions are used by our customers for applications including face recognition, visual product photo search and text-based semantic search. With the explosion of generative AI technology, these capabilities form a firm foundation for text-based AI chatbots.
But remember, SingleStoreDB is a high-performance, scalable, modern SQL DBMS that supports multiple data models including structured data, semi-structured data based on JSON, time-series, full text, spatial, key-value and of course vector data. Start powering your next intelligent application with SingleStoreDB today!
Example
This folder contains examples of using SingleStoreDB and OpenAI together. We will keep adding more scenarios so stay tuned!
| Name | Description |
|---|---|
| OpenAI wikipedia semantic search | Improve ChatGPT accuracy through SingleStoreDB semantic Search in QA |