Semantic text search using embeddings. Example Python notebook from OpenAI demonstrating how to build a search engine using embeddings rather than straight up token matching. This is a fascinating way of implementing search, providing results that match the intent of the search (“delicious beans” for example) even if none of the keywords are actually present in the text.
Recent articles
- Weeknotes: Embeddings, more embeddings and Datasette Cloud - 17th September 2023
- Build an image search engine with llm-clip, chat with models with llm chat - 12th September 2023
- LLM now provides tools for working with embeddings - 4th September 2023
- Datasette 1.0a4 and 1.0a5, plus weeknotes - 30th August 2023
- Making Large Language Models work for you - 27th August 2023
- Datasette Cloud, Datasette 1.0a3, llm-mlc and more - 16th August 2023