Vector embeddings with ChromaDB. Basically you pre compute the word embeddings of every row / table / whatever granularity you want and then stick that into a vector DB. Then you do an embedding computation of your query and compare similarity. You can either return the table / row / whatever you want that’s most similar (“semantic search”) or you use that as context for an LLM (“RAG”)