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”)
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”)