WFGY/ProblemMap/ops/debug_playbook.md

5.8 KiB
Raw Blame History

Debug playbook — incident triage for RAG pipelines

Purpose: step-by-step incident response guide emphasizing reproducible diagnostics and minimal-impact mitigations.


1) Immediate triage (first 120s)

A — Gather context

  • Who reported it? (pager/Slack/ticket)
  • When did it start (wall time)?
  • Scope: single user / single shard / whole cluster?

B — Quick readouts

  • Health: curl -fsS http://$SERVICE/healthz
  • Pods: kubectl -n $NS get pods -o wide
  • Recent errors (last 10m):
    kubectl -n $NS logs -l app=rag --since=10m | tail -n 200
    
    
    
  • Prometheus: check E2E p95 and error rate for last 10m.

C — Decide action mode

  • If P0 (site down / data corruption) → Mitigate (circuit-break / rollback / redirect).
  • If P1 (functional degradation, e.g., CHR drop) → Isolate & debug.

2) Deterministic checks (no LLM calls)

Run these before calling LLMs — theyre cheap and often reveal root causes:

  1. Check retrieval consistency for sample qids:

    curl -X POST http://$SERVICE/debug/retrieve -d '{"qid":"A123","q":"sample question"}' | jq
    

    Validate retrieved_ids and their hashes.

  2. Check mem_rev/mem_hash: verify read vs bound value for turn:

    • Compare retrieved_snapshot.mem_rev vs generation.mem_rev.
  3. Vectorstore health:

    • ping vectorstore API; check index shard status.
  4. Index size & recent writes:

    • kubectl exec -n $NS <vector-pod> -- ls -lh /data/index

3) Common root causes & mitigations

A. Retrieval empty / irrelevant

  • Root cause: indexing job failed or namespace mismatch.

  • Mitigation:

    • Restart indexer pod: kubectl -n $NS rollout restart deploy/indexer
    • Run reindex on a small sample and validate.

B. CHR drop but retrieval OK

  • Root cause: generator hallucinating or prompt/template drift.

  • Mitigation:

    • Turn on guard/refusal stricter mode (feature flag).
    • Re-run golden queries with ?dbg=full to capture prompt+context.

C. Bootstrap / readiness flapping

  • Root cause: bootstrap order or missing dependency.

  • Mitigation:

    • Ensure controller/migrations complete before retriever/generator start; kubectl apply ordering or Helm hooks.

D. LLM provider errors / rate limits

  • Root cause: key expired or provider quota.
  • Mitigation: switch to backup key or provider; throttle traffic until resolved.

4) Live mitigation patterns (minimize impact)

  1. Circuit-breaker (fast): return cached answer for known queries.
  2. Throttle LLM: queue requests, lower concurrency.
  3. Rollback: to last known-good release if config causes issue.
  4. Read-only mode: stop writes to vectorstore if index corruption suspected.

5) Postmortem checklist

  • Timestamped timeline created.
  • Root cause identified (primary + contributing).
  • Actions taken documented.
  • Follow-up tasks created (reindex, fix probe, add tests).
  • Update runbook if new failure mode discovered.

6) Useful debug commands (reference)

  • Pod logs since N minutes:

    kubectl -n $NS logs -l app=rag --since=5m
    
  • Exec into retriever pod:

    kubectl -n $NS exec -it deploy/retriever -- /bin/sh
    
  • Check helm history:

    helm -n $NS history rag
    


🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1 Download · 2 Upload to your LLM · 3 Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1 Download · 2 Paste into any LLM chat · 3 Type “hello world” — OS boots instantly

Explore More

Layer Page What its for
Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
Engine WFGY 1.0 Original PDF based tension engine
Engine WFGY 2.0 Production tension kernel and math engine for RAG and agents
Engine WFGY 3.0 TXT based Singularity tension engine, 131 S class set
Map Problem Map 1.0 Flagship 16 problem RAG failure checklist and fix map
Map Problem Map 2.0 RAG focused recovery pipeline
Map Problem Map 3.0 Global Debug Card, image as a debug protocol layer
Map Semantic Clinic Symptom to family to exact fix
Map Grandmas Clinic Plain language stories mapped to Problem Map 1.0
Onboarding Starter Village Guided tour for newcomers
App TXT OS TXT semantic OS, fast boot
App Blah Blah Blah Abstract and paradox Q and A built on TXT OS
App Blur Blur Blur Text to image with semantic control
App Blow Blow Blow Reasoning game engine and memory demo

If this repository helped, starring it improves discovery so more builders can find the docs and tools. GitHub Repo stars