Cohesion: Python inventory + integration plan + Phase A verdict indexing

Three artifacts in one PR: 1. docs/PYTHON_INVENTORY.md — every .py file in the repo classified: Production (sidecar routers + 3 systemd services), Documented (kb_measure, kb_staffer_report), Manual (one-off tools), Dead (sidecar/sidecar/lab_ui.py + pipeline_lab.py are genuinely not imported anywhere). 2. docs/COHESION_INTEGRATION_PLAN.md — the "smarter DB" loop J called out as missing. Six phases A-F. Phase A ships here; B-F are named + sequenced for follow-up PRs. Each phase adds ONE wire of the loop; no single PR does them all. 3. Phase A wire (auditor verdicts → observer + KB): - auditor/audit.ts: after assembleVerdict, fire-and-forget POST to :3800/event with source="auditor" AND append to data/_kb/outcomes.jsonl with kind="audit". Errors log + drop — the verdict is still on disk at _auditor/verdicts/. - mcp-server/observer.ts: extend source union to include "auditor" | "bot" (was "mcp" | "scenario" only, which silently coerced my first auditor POST to source="scenario"). Accept body.ok OR body.success. Accept body.audit_duration_ms as a fallback for duration_ms. Uses body.one_liner as output_summary when set. Live-verified after observer restart: re-audit PR #6 → verdict=request_changes, 4 findings (1 warn) observer: by_source={'auditor': 1} (previously coerced to 'scenario') _kb/outcomes.jsonl tail: kind=audit sig=pr6-7fe47bab pr=6 overall=request_changes The shape of the loop is now visible to downstream consumers. Phase B (auditor's kb_query check reads these audit rows for history) lands in a follow-up PR. Phase C-F similar. NOT in this PR: - Actually deleting lab_ui.py + pipeline_lab.py (operator decision, called out in the inventory doc) - Cleaning up the 5 overlapping Python scripts (same) - Phases B-F of the cohesion plan (separate PRs per wire) - Integration test that asserts "smarter DB" across runs (Phase F) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 17:22:42 -05:00
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+# Cohesion integration plan — the "smarter DB" loop
+
+**Written:** 2026-04-22, after J flagged that the system has good parts but they don't compose into the self-improving loop promised in the control-plane thesis (`project_control_plane_thesis.md` memory: 0→85% via hyperfocus-then-escalate).
+
+## The gap
+
+Each piece works in isolation. What's not wired:
+
+```
+┌─────────────┐     ┌─────────────┐     ┌─────────────┐
+│   observer  │──✓──│  data/_kb/  │──?──│   auditor   │
+│    :3800    │     │ outcomes.   │     │  kb_query   │
+│             │     │   jsonl     │     │   check     │
+└─────────────┘     └─────────────┘     └─────────────┘
+      ▲                                         │
+      │?                                        ▼
+┌─────────────┐     ┌─────────────┐     ┌─────────────┐
+│  auditor    │─────│ data/_audit │     │   cloud     │
+│  verdicts   │     │or/verdicts/ │     │  inference  │
+│             │     │             │     │             │
+└─────────────┘     └─────────────┘     └─────────────┘
+                                                │?
+                                                ▼
+                                         ┌─────────────┐
+                                         │ hybrid_srch │
+                                         │ playbook_mem│
+                                         │  context7   │
+                                         └─────────────┘
+```
+
+- **✓** solid: observer captures scenarios, writes `_kb/outcomes.jsonl`.
+- **?** missing:
+  - auditor verdicts go to `_auditor/verdicts/` — not to observer or KB
+  - auditor kb_query reads `_kb/outcomes.jsonl` but doesn't query hybrid_search, playbook_memory, or context7
+  - auditor inference sends claim+diff to cloud — but WITHOUT KB neighbors, WITHOUT drift context, WITHOUT tool-aware retrieval
+- Result: every audit is stateless-ish. The DB doesn't get smarter across audits.
+
+## The target loop
+
+```
+PR opened
+  ↓
+auditor fetches diff + claims
+  ↓
+auditor enriches with:
+  — KB neighbors: past verdicts on similar claim-hashes
+  — hybrid_search: playbooks for task classes the diff touches
+  — context7 drift status: for any tool named in commit messages
+  — MCP tools: agent-style queries if relevant
+  ↓
+cloud inference sees: diff + claims + enrichment
+  ↓
+verdict posted to Gitea
+  ↓
+verdict ALSO persisted to:
+  — observer :3800/event (source=auditor)
+  — _kb/outcomes.jsonl (kind=audit, sig_hash=pr+sha)
+  ↓
+next audit on similar PR:
+  — KB neighbors find this prior verdict
+  — cloud sees "similar PRs ended in request_changes for reason X"
+  — verdict is more calibrated
+```
+
+## Phases of closure
+
+Building this in order from least-invasive:
+
+### Phase A — Verdict indexing (shipped in this PR)
+
+After every `auditPr()` completes, in addition to persisting to `_auditor/verdicts/`:
+
+1. POST the verdict to observer `:3800/event` with `source: "auditor"`, `ok: verdict === "approve"`, `event_kind: "audit"`, `sig_hash: <stable hash of pr_number + head_sha>`.
+2. Append a simplified outcome to `data/_kb/outcomes.jsonl` with a special `kind: "audit"` row so the KB surface treats audits alongside scenarios.
+
+Minimal surgery. Doesn't change the verdict itself. Just makes it visible to downstream consumers.
+
+**Test:** after this PR lands, re-run the auditor once; observer stats should show `by_source.auditor > 0`; `_kb/outcomes.jsonl` should have one new row per audited SHA.
+
+### Phase B — KB query sees auditor history
+
+Extend `auditor/checks/kb_query.ts` to:
+
+1. Read `_kb/outcomes.jsonl`, filter `kind === "audit"`, find prior audits with matching `sig_hash` (same PR across SHAs) or similar claim hashes.
+2. Emit finding: "prior N audits on this PR ended in [approve, block, request_changes], last reason was X."
+
+Gives the auditor memory across re-audits of the same PR.
+
+### Phase C — Hybrid search in kb_query
+
+When a PR touches `crates/vectord/src/playbook_memory.rs` (task-class matching), auditor calls `POST /vectors/hybrid` to find playbooks semantically related to the diff. Surfaces as "N playbooks in production rely on this code path, consider backward-compat."
+
+Requires:
+- Task-class extractor from diff paths
+- Cloud-free hybrid_search call (we have local Ollama for embeddings)
+
+### Phase D — Context7 drift awareness in auditor
+
+If the diff / commit message names a tool that's in any playbook's `doc_refs`, auditor calls the context7 bridge to check current drift status. Surfaces as "tool X has drifted since N playbooks referenced it; this PR may need to update those."
+
+### Phase E — Inference sees enrichment
+
+The cloud inference check currently sends `diff + claims`. Extend to send `diff + claims + kb_neighbors + drift_context + hybrid_search_matches`. The prompt becomes context-rich — exactly what makes the cloud model competent (per the control-plane thesis).
+
+### Phase F — Full integration test
+
+An auditor self-test that:
+
+1. Creates a synthetic PR with known-good and known-bad claims
+2. Runs the enriched auditor
+3. Asserts the verdict found the planted issues
+4. Runs a second identical-claim PR
+5. Asserts the SECOND verdict references the FIRST audit (via KB neighbor retrieval)
+6. "Smarter DB" proof: two runs, measurable context gain.
+
+## Sequence
+
+Phase A lands in this PR alongside the inventory. Phases B-F are follow-up PRs, each with their own auditor gate. Order matters: A → B (reads A's output) → C+D (in parallel) → E (consumes B/C/D) → F (asserts E's behavior).
+
+## Not in this plan (deliberate)
+
+- **Rewriting the auditor to use a different architecture.** Current 4-check model stays; we just enrich the checks.
+- **Tuning cloud inference precision.** The false-positive rate is a prompt-engineering concern; this plan is about context enrichment, which is separate.
+- **Branch protection enforcement.** Stays off until Phase F passes.
+
+The overall bet: this is the "putting it all together coherently" J said was a real problem. Six phases over however many PRs it takes. Each one ships one wire of the loop; no single PR tries to do them all.
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+# Python inventory — what's wired vs dead
+
+Generated 2026-04-22. Enumerates every `.py` file in the repo and classifies each by actual integration status. Goal: surface the "ambiguous names that don't hook in" J flagged.
+
+## Classification
+
+- **🟢 Production** — running as a systemd service, or imported by code that does
+- **🟡 Documented** — referenced in `docs/PRD.md` / `docs/PHASES.md` / `docs/CONTROL_PLANE_PRD.md` or called from a shell/TS orchestrator
+- **🟠 Manual** — runnable as a one-off utility but not in any automated pipeline
+- **🔴 Dead** — not imported, not referenced, not called anywhere
+
+## Results
+
+### Sidecar (`sidecar/sidecar/`)
+
+The Python FastAPI sidecar on `:3200`. Runs as `lakehouse-sidecar.service`.
+
+| File | Status | Evidence |
+|---|---|---|
+| `main.py` | 🟢 Production | systemd ExecStart: `uvicorn sidecar.sidecar.main:app` |
+| `admin.py` | 🟢 Production | imported by `main.py` as `admin_router` |
+| `embed.py` | 🟢 Production | imported by `main.py` as `embed_router` |
+| `generate.py` | 🟢 Production | imported by `main.py` as `generate_router` |
+| `rerank.py` | 🟢 Production | imported by `main.py` as `rerank_router` |
+| `ollama.py` | 🟢 Production | imported transitively by embed/generate/rerank |
+| `__init__.py` | 🟢 Production | package marker |
+| `lab_ui.py` | 🔴 **Dead** | **not imported by any file in `sidecar/`. `scripts/serve_lab.py` only uses stdlib — doesn't import this.** Mtime 2026-04-16 (pre-session). Safe to delete. |
+| `pipeline_lab.py` | 🔴 **Dead** | **same — not imported anywhere. Mtime 2026-04-16.** Docstring says "iterative embedding/LLM pipeline experimentation" — looks like an abandoned prototype. Safe to delete. |
+
+### Scripts (`scripts/`)
+
+17 files. Only **2** are production (behind systemd), **3** are documented in phase/PRD docs, **12** are unreferenced.
+
+| File | Status | Evidence |
+|---|---|---|
+| `serve_ui.py` | 🟢 Production | `lakehouse-ui.service` ExecStart |
+| `serve_imagegen.py` | 🟢 Production | `imagegen.service` ExecStart |
+| `serve_lab.py` | 🟢 Production | `pipeline-lab.service` ExecStart |
+| `kb_measure.py` | 🟡 Documented | referenced in `docs/PHASES.md` (Phase 22 aggregator) |
+| `kb_staffer_report.py` | 🟡 Documented | referenced in `docs/PHASES.md` + `docs/PRD.md` (Phase 23), also called by `scripts/run_staffer_demo.sh` |
+| `autonomous_agent.py` | 🟠 Manual | "Autonomous stress-test agent" — no docs refs, no callers. Mtime 2026-04-17. |
+| `copilot.py` | 🟠 Manual | "Staffing Co-Pilot — the anticipation layer" — no docs refs. Writes briefing to `/tmp/copilot_briefing.json` on run. |
+| `generate_demo.py` | 🟠 Manual | "realistic demo datasets" — mtime 2026-03-27, never touched this session |
+| `generate_workers.py` | 🟠 Manual | "worker profiles at scale" — usage example in docstring: `python3 generate_workers.py 100000 > /tmp/workers_100k.csv`. Still useful, just not auto-run. |
+| `lance_tune.py` | 🟠 Manual | "IVF_PQ parameter sweep" — one-shot experiment tool. |
+| `qwen3_plan.py` | 🟠 Manual | "Qwen 3 agent plan — structured test with playbook building" |
+| `quality_eval.py` | 🟠 Manual | "Quality evaluation pipeline — tests whether the system gives CORRECT" |
+| `scale_test.py` | 🟠 Manual | called by `scripts/scale_10m_test.sh` (not in systemd). 2.5M row test. |
+| `staffing_day.py` | 🟠 Manual | "Real-world staffing agency day simulation" — no docs refs. Overlaps conceptually with `tests/multi-agent/scenario.ts`. |
+| `staffing_demo.py` | 🟠 Manual | "Realistic staffing company data generator" — demo fixture. |
+| `staffing_simulation.py` | 🟠 Manual | "multi-agent stress test" — overlaps with `tests/multi-agent/scenario.ts`. |
+| `stress_test.py` | 🟠 Manual | "prove this architecture is sound or find where it breaks" — overlaps with `tests/multi-agent/run_stress.ts`. |
+
+## Overlap with TypeScript / Rust equivalents
+
+Several Python scripts overlap conceptually with newer TS/Rust code. This is the "ambiguous names that don't hook in" pattern — the Python originals were superseded but not removed.
+
+| Python script | Newer equivalent | Integration drift |
+|---|---|---|
+| `scripts/staffing_simulation.py` | `tests/multi-agent/scenario.ts` | scenario.ts ships + observer-integrated (Phase 24); Python version is unwired. |
+| `scripts/staffing_day.py` | `tests/multi-agent/scenario.ts` | same. |
+| `scripts/stress_test.py` | `tests/multi-agent/run_stress.ts` | TS version is the one the auditor fixture exercises. |
+| `scripts/autonomous_agent.py` | `bot/` + `auditor/` Bun sub-agents | new Bun pattern supersedes. Python version predates the control-plane pivot. |
+| `scripts/qwen3_plan.py` | `tests/multi-agent/agent.ts` | TS agent is the one with Phase 21 continuation + cloud routing. |
+
+**Recommended cleanup actions (not in this PR — separate decision):**
+
+1. **Delete the two confirmed dead sidecar files:** `sidecar/sidecar/lab_ui.py`, `sidecar/sidecar/pipeline_lab.py`. Nothing calls them; removal is safe.
+2. **Archive the 5 overlapping scripts** (staffing_simulation, staffing_day, stress_test, autonomous_agent, qwen3_plan) — either move to `scripts/legacy/` with a README explaining they predate the TS versions, or delete if J confirms no future use.
+3. **Keep the one-off utilities** (generate_demo, generate_workers, lance_tune, quality_eval, copilot, scale_test, staffing_demo) — useful as manual tools. Maybe add a `scripts/README.md` documenting what each does and when to run.
+4. **Promote `kb_measure.py` and `kb_staffer_report.py`** to be actually callable from the KB flow — they're documented but only run manually after scenario batches.
+
+## Honest next step
+
+The cleanup above isn't this session's work — it's a subsequent PR. This doc lands first so the state is visible and operator-auditable.