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
- AI-enhanced development makes me more ambitious with my projects - 27th March 2023
- I built a ChatGPT plugin to answer questions about data hosted in Datasette - 24th March 2023
- Weeknotes: AI won't slow down, a new newsletter and a huge Datasette refactor - 22nd March 2023
- Don't trust AI to talk accurately about itself: Bard wasn't trained on Gmail - 22nd March 2023
- A conversation about prompt engineering with CBC Day 6 - 18th March 2023
- Could you train a ChatGPT-beating model for $85,000 and run it in a browser? - 17th March 2023