[paraph1n]

Could someone point me towards a good resource for learning how to build a RAG app without llangchain or llamaindex? It's hard to find good information.

[turnsout]

At a fundamental level, all you need to know is:

- Read in the user's input

- Use that to retrieve data that could be useful to an LLM (typically by doing a pretty basic vector search)

- Stuff that data into the prompt (literally insert it at the beginning of the prompt)

- Add a few lines to the prompt that state "hey, there's some data above. Use it if you can."

[kolinko]

You can start by reading up about how embeddings work, then check out specific rag techniques that people discovered. Not much else is needed really.

[krawczstef]

Here's a blog post that I just pushed that doesn't use them at all - https://blog.dagworks.io/p/building-a-conversational-graphdb (we have more on our blog - search for RAG).

[disclaimer I created Hamilton & Burr - both whitebox frameworks] See https://www.reddit.com/r/LocalLLaMA/comments/1d4p1t6/comment... for comment about Burr.

[verdverm]

My strategy has been to implement in / follow along with llamaindex, dig into the details, and then implement that in a less abstracted, easily understandable codebase / workflow.

Was driven to do so because it was not as easy as I'd like to override a prompt. You can see how they construct various prompts for the agents, it's pretty basic text/template kind of stuff

[d13]

This is fun and interesting:

https://developers.cloudflare.com/workers-ai/tutorials/build...

[sveinek]

Data centric on YouTube has some great videos . https://youtube.com/@data-centric?si=EOdFjXQ4uv02J774

[fsndz]

check this: https://www.lycee.ai/blog/rag-fastapi-postgresql-pgvector

[bestcoder69]

openai cookbook! Instructor is a decent library that can help with the annoying parts without abstracting the whole api call - see it’s docs for RAG examples.