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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.

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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.

There are so many of us doing the same, just had a similar conversation at $work. It’s pretty exciting. I feel like I’m having to shove another 20 years of development experience into my brain with all these new concepts and abstractions, but the dots have been connecting!

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

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

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