# 🔍 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 ` --- ## 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 ```python 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 ```bash > 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) ```yaml 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 ```python from wfgy.langchain import WFGYTracer lc = initialize_agent(..., callbacks=[WFGYTracer()]) ``` ### 7.2 OpenAI Function-Calling Add to request: ```json "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](https://zenodo.org/records/15630969) | 1️⃣ Download · 2️⃣ Upload to LLM · 3️⃣ Ask “answer using WFGY + \” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://zenodo.org/records/15788557) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly | --- ### 🧭 Explore More | Module | Description | Link | |-----------------------|----------------------------------------------------------|----------| | WFGY Core | Standalone semantic reasoning engine for any LLM | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) | | Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) | | Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | | Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | | Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) | | Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) | --- > 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** — > Engineers, hackers, and open source builders who supported WFGY from day one. >

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