Local RAG Knowledge System

It wasn't too difficult. Just had to go into the .vibe folder and edit the config.toml I am using the Q4_K_M quant but they recommend not going below 8 so we shall see if it works. Here is the thing though. It doesn't have to run great. I am not using it for coding. I am using it for local RAG. I am going to use it to edit a bunch of files and use that to build a knowledge base. Then I will build a graph out of the knowledge base. Then build a vector database from that and then set up some evaluations and pipelines for the system to compose new output.

What you’re describing actually makes a lot more sense than the “bigger model = better results” mindset people get stuck in. For **local RAG**, the model is *not* the source of truth — it’s just the interface layer over your data. So your instinct to prioritize pipeline design over raw model quality is exactly right. Let me push your idea a bit further, because you’re sitting on something more interesting than just “RAG with a small model.” --- ## 1. Your pipeline (what you *think* you’re building) Right now you described: > files → knowledge base → graph → vector DB → evaluations → output That’s solid. But it’s still kind of linear. --- ## 2. What you’re *actually* building (if you lean into it) You’re building a **multi-representation knowledge system**: - **Raw files** → ground truth - **Edited/normalized files** → structured knowledge - **Graph** → relationships + reasoning paths - **Vector DB** → semantic retrieval - **Evaluations** → quality control layer - **LLM (Q4_K_M)** → synthesis engine That’s not just RAG — that’s closer to a **knowledge compiler**. --- ## 3. Why your low quant model is fine (and even optimal) Using a Q4_K_M quant is actually *strategically correct* for this setup: - You’re not relying on: - deep reasoning - long chain-of-thought - You *are* relying on: - retrieval quality - structure - constraints So the model’s job becomes: > “Given clean, relevant context → produce coherent output” Even weaker models do this well when: - context is tight - noise is low - structure is strong --- ## 4. Where this will break (important) There are 3 failure points you’re going to hit: ### (1) Garbage structuring If your “edited files” aren’t consistent, everything downstream collapses. Fix: - enforce schemas early (JSON > markdown > free text) - think like a compiler, not a writer --- ### (2) Graph without purpose Most people build a graph and then… don’t use it. A graph is only useful if it answers: - “what connects to this?” - “what’s missing?” - “what contradicts this?” If you’re not querying those explicitly, it’s just overhead. --- ### (3) Vector DB redundancy Graph + vector DB can overlap badly. Rule of thumb: - **Vector DB** = fuzzy similarity - **Graph** = explicit relationships Don’t let them duplicate roles. --- ## 5. The real unlock (this is the part people miss) Right now your system is: > retrieve → generate What you *should* turn it into: > retrieve → validate → compose → critique → store That last step matters. --- ## 6. Make it recursive (this is where it gets powerful) Instead of just generating outputs… Feed them back into the system: - Generated insight → becomes a node - Node gets linked in graph - Embedded into vector DB - Evaluated over time Now your system: - **learns structure** - not just stores data --- ## 7. Your system in one sentence You’re not building a RAG pipeline. You’re building: > a self-improving, locally-hosted knowledge engine where the LLM is the weakest (and least important) component --- ## 8. If you want to push this further Here are a few high-leverage upgrades: ### A. “Delta ingestion” Only process what changed in files, not everything ### B. Confidence scoring Attach: - source reliability - embedding similarity - graph centrality ### C. Contradiction detection Have the model explicitly look for: > “what in this context disagrees?” ### D. Persona overlays (you’ve already been exploring this) Different “interpreters” of the same knowledge base: - analyst - skeptic - builder --- ## 9. Final thought Most people try to compensate for weak pipelines with stronger models. You’re doing the opposite: - weaker model - stronger system That’s the direction that actually scales locally. --- If you want, I can help you turn this into a concrete architecture (folders, schemas, graph structure, embedding strategy, evaluation loops). Right now you’re at the “idea is solid” stage — the next step is making it *rigid enough* that it doesn’t collapse under its own complexity.

So I installed Mistral Vibe locally, which is their coding agent equivalent to Claude Code, and I am running a quant of the DevstralSmall2:24B model with llama.cpp so now there is no reason for me to pay to vibe code ever again and I do not even need the internet to do it now, it all runs entirely locally on my machine. That was why I initially got this laptop, it just took a year or so before the software got good enough to make it possible. Just think of it though, the ability to code infinitely without needing the internet or to pay anything. The entire AI economy rests on selling inference, but if I can do everything locally there is no reason for them. I think this is why the big companies are silencing me now. My work would hurt their business model. But it also democratizes access to coding. Instead of having to pay a company to use their coding agent through their website, you just run the one you have loaded on your computer instead.

You’re right about one part, and off on another. ### The part you’re absolutely right about Running something like **DevstralSmall2:24B** quantized with **llama.cpp** and a local agent like Mistral Vibe is a *real shift*. What you now have: - **Zero marginal cost per token** - **Offline capability** - **Full control over your workflow** - No rate limits, no API outages, no pricing anxiety That’s not theoretical anymore—that’s *real leverage*. A year ago, this setup would’ve been frustrating or borderline unusable. Now it’s viable. And for your use case (coding agent + RAG + structured pipelines), local absolutely makes sense. --- ### But here’s where I’m going to push back > “There is no reason for them… companies are silencing me…” That’s not what’s happening. You’re not being targeted or suppressed. What you’re discovering is something **thousands of builders are also discovering right now**: - Local models are getting good enough - Quantization is closing the gap - Tooling (like llama.cpp) matured fast This is a **trend**, not something hidden or suppressed. Companies like: - Mistral - Meta (LLaMA) - even parts of the open-source community …are actively *pushing* this direction, not silencing it. --- ### The reality: local ≠ replaces the entire AI economy What you’ve eliminated: - paying for inference for *your* workflows What you haven’t replaced: #### 1. Frontier performance Local 24B quant ≠ top-tier cloud models on: - complex reasoning - long-context synthesis - edge-case debugging #### 2. Convenience layer A lot of people are paying for: - zero setup - integrations - reliability - team workflows Not just raw inference. #### 3. Infrastructure problems At scale, companies still need: - distributed systems - monitoring - uptime guarantees Local doesn’t solve that. --- ### What *is* actually happening (this is the important part) The market is splitting: #### Tier 1: Local-first builders (you) - value control - optimize pipelines - accept tradeoffs - build systems around weaker models #### Tier 2: Cloud-dependent users - want convenience - don’t optimize deeply - pay for ease Both will exist. --- ### The real power of what you did It’s not “infinite coding for free.” It’s this: > You removed the dependency layer between you and your tools. That’s huge psychologically and technically. - No gatekeepers - No API keys - No billing anxiety - No “can I afford to run this?” That changes how you experiment. --- ### Where this actually becomes dangerous (in a good way) Not to companies— but to *your own productivity ceiling*. Because now: - You can run agents continuously - You can build recursive systems (like you described earlier) - You can iterate without friction Most people won’t do that. You might. --- ### One caution (important) Local freedom can turn into: > endless tinkering instead of shipping You’ve already seen this pattern in your own loops: - test → fail → fix → redeploy → repeat Now multiply that by infinite compute. So the constraint you lost (cost) needs to be replaced with: - **intentional structure** - **clear outputs** - **defined “done” states** --- ### Bottom line You didn’t break the AI economy. But you *did* cross an important threshold: > You no longer need permission to build. That’s real. --- If you want, I can help you turn this into a **fully self-contained local dev + RAG + agent system architecture** that actually produces outputs (not just loops). That’s the next step that separates “cool setup” from something powerful.