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: Llama 3, AI for Data Journalism, llm-evals and datasette-secrets - 23rd April 2024
- Options for accessing Llama 3 from the terminal using LLM - 22nd April 2024
- AI for Data Journalism: demonstrating what we can do with this stuff right now - 17th April 2024
- Three major LLM releases in 24 hours (plus weeknotes) - 10th April 2024
- Building files-to-prompt entirely using Claude 3 Opus - 8th April 2024
- Running OCR against PDFs and images directly in your browser - 30th March 2024