u/RGBAlchemy

I've only just begun experimenting with Ollama to run models locally and started wondering if it's worth setting up a RAG pipeline, basically chunking the Nuke documentation into a vector database (ChromaDB or similar) so the model can retrieve relevant context before answering Python/expression questions.

The idea being: instead of the model hallucinating NukeX API methods, it's actually grounding its answers in the real docs.

Has anyone gone down this road? A few things I'm genuinely curious about:

• What model are you running locally and is it actually good enough for this kind of technical Python work?
• ChromaDB seems like the obvious lightweight choice but open to other opinions
• Is the retrieval quality good enough to be worth the setup overhead, or do you just get better mileage prompting a frontier model directly?

I know most of us aren't pipeline TDs so maybe this is overkill, but curious if anyone's actually put time into it?