More of a proof of concept to test out ideas, but here's my approach for local RAG, https://github.com/amscotti/local-LLM-with-RAG

Using Ollama for the embeddings with “nomic-embed-text”, with LanceDB for the vector database. Recently updated it to use “agentic” RAG, but probably not fully needed for a small project.

▲

vaylian 2 hours ago | parent | next [-]

Thank you for being the kind of person who explains what the abbreviation RAG stands for. I have been very confused reading this thread.

	▲	someguyiguess 38 minutes ago \| parent [-]
		I feel this pain! It feels like in the world of LLMs there is a new acronym to learn every day! For the curious RAG = Retrieval Augmented Generation. From wikipedia: RAG enables large language models (LLMs) to retrieve and incorporate new information from external data sources

▲

someguyiguess 3 hours ago | parent | prev [-]

Woah. I am doing something very similar also using lancedb https://github.com/nicholaspsmith/lance-context

Mine is much more basic than yours and I just started it a couple of weeks ago.