langfuse: Go-side client + Phase 1c instrumentation
The Rust side has Langfuse tracing already (gateway/v1/langfuse_trace.rs); this commit lands Go-side parity so the multi-coord stress harness can emit traces visible at http://localhost:3001. internal/langfuse/client.go: - Minimal Trace + Span + Flush API mirroring what the Rust emitter uses. Auth: Basic over public_key:secret_key. - Best-effort posture: errors are slog.Warn'd, never block calling paths. Same fail-open as observerd's persistor (ADR-005 Decision 5.1) — observability is a witness, not a gate. - Events buffered until 50, then auto-flushed; explicit Flush() at process exit. - Each Trace/Span returns its id so callers can build hierarchies. multi_coord_stress driver wiring: - New --langfuse-env flag (default /etc/lakehouse/langfuse.env). Empty / missing / unparseable file → skip tracing with a logged warning; run still proceeds. - Phase 1c (inbox burst) now emits one parent trace + 4 spans per inbox event: 1. observerd.inbox.record (post to /v1/observer/inbox) 2. llm.parse_demand (qwen2.5 → structured fields) 3. matrix.search (parsed query → top-K) 4. llm.judge_top1 (rate top-1 vs original body) Each span carries input/output JSON + start/end times so the Langfuse UI shows a full waterfall per event. Run #009 result: Trace landed: "multi_coord_stress phase 1c inbox burst" Observations attached: 24 (= 6 events × 4 spans) Tags: stress, phase-1c, inbox Browseable at http://localhost:3001 by tag query. Other harness metrics: diversity 0.016, determinism 1.000, verbatim handover 4/4, paraphrase handover 4/4 — all unchanged by the tracing addition (best-effort post in parallel). Phase 1c is the proof-of-concept; future commits can wrap other phases (baseline / merge / handover / split) in traces too. Once that's done, the entire stress run becomes scrubbable in Langfuse without grepping the events JSON. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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# Multi-Coordinator Stress Test — Run 009
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**Generated:** 2026-04-30T21:23:59.011167722Z
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**Coordinators:** alice / bob / carol (each with own playbook namespace: `playbook_alice` / `playbook_bob` / `playbook_carol`)
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**Contracts:** alpha_milwaukee_distribution / beta_indianapolis_manufacturing / gamma_chicago_construction
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**Corpora:** `workers,ethereal_workers`
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**K per query:** 8
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**Total events captured:** 67
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**Evidence:** `reports/reality-tests/multi_coord_stress_009.json`
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---
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## Diversity — is the system locking into scenarios or cycling?
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| Metric | Mean Jaccard | n pairs | Interpretation |
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|---|---:|---:|---|
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| Same role across different contracts | 0.015873015873015872 | 9 | Lower = more diverse (different region/cert mix → different workers) |
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| Different roles within same contract | 0.015343915343915345 | 18 | Should be near-zero (different roles = different worker pools) |
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**Healthy ranges:**
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- Same role across contracts: < 0.30 means the system is genuinely picking different workers per region/contract.
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- Different roles same contract: < 0.10 means role-specific retrieval is working.
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- If either is > 0.50, the system is "cycling" the same handful of workers regardless of query intent.
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---
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## Determinism — same query reissued, top-K stability
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| Metric | Value |
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|---|---:|
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| Mean Jaccard on retrieval-only reissue | 1 |
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| Number of reissue pairs | 12 |
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**Interpretation:**
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- ≥ 0.95: HNSW retrieval is highly deterministic; reissues land on near-identical top-K. Good — system locks into a stable view of "best workers for this query."
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- 0.80 – 0.95: Some HNSW or embed variance, acceptable.
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- < 0.80: Retrieval is unstable — reissues see substantially different results, suggesting either embed nondeterminism (Ollama returning slightly different vectors) or vectord nondeterminism (HNSW insertion order affecting recall).
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---
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## Learning — handover hit rate
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Bob takes Alice's contract using Alice's playbook namespace. Did Alice's recorded answers surface in Bob's results?
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| Metric | Value |
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|---|---:|
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| Verbatim handover queries run | 4 |
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| Alice's recorded answer at Bob's top-1 (verbatim) | 4 |
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| Alice's recorded answer in Bob's top-K (verbatim) | 4 |
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| **Verbatim handover hit rate (top-1)** | **1** |
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| Paraphrase handover queries run | 4 |
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| Alice's recorded answer at Bob's top-1 (paraphrase) | 4 |
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| Alice's recorded answer in Bob's top-K (paraphrase) | 4 |
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| **Paraphrase handover hit rate (top-1)** | **1** |
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**Interpretation:**
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- Verbatim hit rate ≈ 1.0: trivial case — Bob runs identical queries; should always hit.
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- Paraphrase hit rate ≥ 0.5: institutional memory survives wording change — the harder learning property.
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- Paraphrase hit rate ≈ 0.0: Bob's paraphrases drift past the inject threshold, so Alice's recordings don't activate. Same caveat as the playbook_lift paraphrase pass.
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---
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## Per-event capture
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All matrix.search responses live in the JSON — top-K with worker IDs, distances, and per-corpus counts. Search by phase:
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```bash
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jq '.events[] | select(.phase == "merge")' reports/reality-tests/multi_coord_stress_009.json
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jq '.events[] | select(.coordinator == "alice" and .phase == "baseline")' reports/reality-tests/multi_coord_stress_009.json
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jq '.events[] | select(.role == "warehouse worker") | {phase, contract, top_k_ids: [.top_k[].id]}' reports/reality-tests/multi_coord_stress_009.json
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```
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---
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## What's NOT in this run (Phase 1 deliberately defers)
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- **48-hour clock.** Events fire as discrete steps, not on a timeline.
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- **Email / SMS ingest.** No endpoints exist on the Go side yet.
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- **New-resume injection mid-run.** The corpus is fixed at the start.
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- **Langfuse traces.** Need Go-side wiring.
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These are Phase 2/3. The Phase 1 substrate is what the time-based runner will mount on top of.
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