Search your documents with AI (RAG)

RAG pipelines, corpora and embedding models so an AI answers from your documents.

contextetech

nomic-embed-text embeddings locally with Ollama

nomic-embed-text for a 100% local English RAG with Ollama: 768 dimensions, long…

768 dim. 003 hr

contextetech

BGE-M3 embeddings for RAG over long documents

BGE-M3 for RAG over long documents: 1024 dimensions, up to 8,192 tokens, 100+…

1024 dim. 003 hr

What does searching documents with AI mean?

AI can answer from your own documents through RAG (retrieval-augmented generation): documents are split, turned into vectors, and the passages closest to the question are given to the model, which answers and cites its sources.