PSBigBig

got it they likely skimmed and never verified whether **llama backend actually got built/linked**. keep it surgical, give them two clear paths and 3 checks. --- **Short diagnosis** This is...

It looks like the core issue is that Milvus is being force-loaded even when not needed (see your snippet where it’s marked optional but still imported without condition). This means...

Glad it’s merged. Just to confirm does this implementation fully skip importing Milvus in environments without it installed? I’ve seen similar lazy-load patches still instantiate hidden dependencies unless fully isolated.

you’re likely hitting **No.11 – Symbolic Collapse**: the config says “parallel,” but hidden constraints are serializing the work path. quick checks to isolate why the “parallel” variant is \~30× slower:...

You might find this useful — here’s the Problem Map covering common real-world AI system failure modes, along with direct links to detailed fixes: 🔗 https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md This specific approach works...

the behavior you’re seeing (two nearly identical deep links handled differently because of subtle parameter differences like `apn`) maps directly to ProblemMap No.11 — semantic drift in routing. what happens...

Both of you are bumping into the same root cause: this is **Problem Map No.11 – Symbolic Drift/Collapse in routing**. When the router sees two nearly identical deep links, subtle...

Hey @Olflach, Oh yes, I felt this one. You’re doing everything right — local Ollama, clean LangChain pipeline, no sign of OpenAI anywhere — and then BAM, the ghost of...

Ouch — 40+ seconds on knowledge retrieval is brutal, especially when your doc count isn't massive yet We ran into a similar bottleneck before, and turns out the issue wasn’t...

ran into the same issue with faiss_cpu_flat on some Xeon SKUs. likely hitting an AVX-related trap — either unsupported instructions or memory alignment blowups under load. we traced this back...