WFGY/ProblemMap/observability-runbook.md

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🔍 Observability Runbook

Turn every opaque token stream into a traceable, testable, measurable system.

Outcome you care about
See each pipeline hop (OCR → chunk → embed → retrieve → prompt → reasoning)
Measure semantic drift (ΔS), logic trend (λ), residual error (E_resonance)
Alert before hallucination, loop, or tool mis-fire happens
Replay exact failure path in one command: wfgy trace-replay <id>


1 · Core Telemetry Trio

Signal Formula Why It Matters Target Band
ΔS (semantic stress) 1 cos(I, G) Spot where meaning rips < 0.50
λ_observe (logic vector) { → ← <> × } Track convergent vs. divergent flow stay →
E_resonance mean‖B‖ (BBMC) Detect creeping entropy flat / ↓

ΔS tells you where; λ tells you direction; E tells you when a slow leak becomes collapse.


2 · Minimal Instrumentation Patch

2.1 Python snippet

from wfgy import monitor

with monitor.pipeline("rag-query") as span:
    span.tag("query", question)
    ctx  = rag_retrieve(question)
    span.metric("ΔS_q_ctx", monitor.deltaS(question, ctx))
    answer = llm_reason(ctx, question)
    span.metric("λ_reason", monitor.lambda_state(answer))
    span.metric("E_res", monitor.e_resonance())

No external SaaS; dumps to newline-JSON for Grafana / Prometheus scrape.

2.2 Console tracer

> wfgy trace-replay 2025-08-06T10:22:15Z
step  ΔS   λ    span
  1   0.08 →
  2   0.41 →   retrieval
  3   0.44 ←   reasoning  ❗ divergent

3 · Alert Rules (Prometheus example)

groups:
- name: wfgy-alerts
  rules:
  - alert: SemanticDriftHigh
    expr: deltaS_q_ctx > 0.60
    for: 2m
    labels: {severity: "warning"}
    annotations:
      summary: "Semantic drift above 0.60 — check chunking/retriever"

Recommended baseline:

Alert Expression Severity
DriftHigh ΔS > 0.60 warn
LogicOsc λ flips 3× in 5 min warn
EntropyClimb E_res 15 min slope > 0.02 crit
ToolTimeout call_dur > p95 × 2 warn

4 · End-to-End Health Dashboard

Tile Metric Healthy
Retrieval ΔS median last 5 min ≤ 0.45
Reason λ % divergent < 5 %
Answer ΔS question↔answer ≤ 0.50
E_res Trend 30 min slope - / flat
Tool Latency p95 ms within SLO

Grafana JSON panel export: dashboards/wfgy-observability.json.


5 · Common Anti-Patterns & Fixes

Anti-Pattern Symptom Fix
Log Everything Raw 20 GiB/day, no insight Only log ΔS/λ/E & key BLobs
Stateless Trace IDs tough replay Use wfgy span ctx_id auto-prop
Per-call embeddings cost explodes cache via BBMC node ID
No sampling GPU choke 1 % sampling on low ΔS calls

6 · Kitchen-Sink CLI Cheats

Command Purpose
wfgy span-tail live ΔS/λ feed
wfgy span-grep λ=divergent list divergent traces
wfgy span-diff A B compare two answers λ/ΔS
wfgy span-export --csv dump traces to CSV

7 · Integration Recipes

7.1 LangChain

from wfgy.langchain import WFGYTracer
lc = initialize_agent(..., callbacks=[WFGYTracer()])

7.2 OpenAI Function-Calling

Add to request:

"logit_bias": {"50256": -100},  // block special token
"wfgy_trace": true

Server-side middleware writes trace JSON.


🔗 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

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🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
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