RAG for Local LLM?

[afsabresfan]

Is there any way to make this information available to a locally-hosted LLM with RAG? Storing all this information locally and letting a local LLM scrape it when needed would be fantastic!

[onestardao]

That’s a great question — many people want to keep things fully local and still benefit from RAG.

Technically, nothing stops you from running a lightweight vector DB (e.g. SQLite + FAISS, or even DuckDB extensions) alongside your local LLM. The pipeline would look like:

1. Collect your documents locally.
2. Index with a simple embedding backend (local-only).
3. Query + rerank inside the same environment, no cloud calls.

The challenge you’ll hit is usually not “can it run locally,” but rather semantic drift when the retriever and the model don’t share the same embedding/logic space. That mismatch is a known failure mode (we usually tag it as Problem NO 5: Semantic ≠ Embedding).

If you’re curious, I can point you to a mapped checklist of such failure cases with reproducible fixes — it helps avoid silent mismatches like this.

Otherwise, the simplest testbed is: run your local LLM, attach a text-OS style operator file (TXTOS or similar), and literally ask it “use this to patch retrieval drift.” Most modern LLMs understand that instruction without infra changes.

[jimternet]

Ive started work on a MCP server here : https://github.com/jimternet/monicahq-mcp