[amscotti]

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.

[someguyiguess]

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.

[threecheese]

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!

[vaylian]

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]

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