Benjamin Admin
7df15010ff
Per the user's decision (2026-06-28): observations are CALIBRATION data for the knowledge base, NOT
business data and NOT product-DB data. So they live with the other versioned knowledge artifacts as an
append-only JSONL log under knowledge/observations/ — NO migration, NO DB. (A real persistence layer is
only warranted once thousands of onboardings exist; not before.)
- ObservationRecord = Observation + log metadata (observation_id, timestamp [caller-stamped, no hidden
clock], customer_archetype [anonymised — NEVER a real name], evidence, provenance, knowledge_version).
- append_observation() writes one JSON line; append-only, lines are never rewritten. A later review is a
NEW line with the same observation_id; load_observations(reconcile=True) keeps the latest per id.
- load_observations() reads a single .jsonl or a directory of monthly .jsonl files.
- aggregate_by_hypothesis() (59c) -> per-hypothesis distribution + confidence, COMPUTED from the log
(computed-not-stored); the review gate (reviewed-only) is enforced in empirical_distribution/confidence.
- review_queue() -> the unreviewed worklist. Observation -> Review -> Accepted -> recompute, never
Observation -> confidence++. Nothing is ever written back to a hypothesis.
You can `rm` the log and recompute, `git diff` it over months, or rebuild confidence under a new policy —
fully consistent with computed-not-stored and the product/knowledge data separation.
Non-runtime (module + tests only, no endpoint) -> origin/main, NO dev deploy. 5 new tests (append-only,
review supersession, review-gate statistics, queue, monthly-file load); 27 onboarding tests pass, mypy
--strict clean (9 modules), check-loc 0. 59d (surface computed confidence at runtime) stays a later step.
backend-compliance
Python/FastAPI service implementing the DSGVO compliance API: DSR, DSFA, consent, controls, risks, evidence, audit, vendor management, ISMS, change requests, document generation.
Port: 8002 (container: bp-compliance-backend)
Stack: Python 3.12, FastAPI, SQLAlchemy 2.x, Alembic, Keycloak auth.
Architecture
compliance/
├── api/ # Routers (thin, ≤30 LOC per handler)
├── services/ # Business logic
├── repositories/ # DB access
├── domain/ # Value objects, domain errors
├── schemas/ # Pydantic models, split per domain
└── db/models/ # SQLAlchemy ORM, one module per aggregate
The service follows this layered target structure but not all files are fully refactored yet. Phase 1 backlog is tracked in .claude/rules/loc-exceptions.txt (27 backend-compliance files currently excepted).
See ../AGENTS.python.md for the full convention and ../.claude/rules/architecture.md for the non-negotiable rules.
Run locally
cd backend-compliance
pip install -r requirements.txt
export COMPLIANCE_DATABASE_URL=... # Postgres (Hetzner or local)
uvicorn main:app --reload --port 8002
Tests
pytest compliance/tests/ -v
pytest --cov=compliance --cov-report=term-missing
Layout: tests/unit/, tests/integration/, tests/contracts/. Contract tests diff /openapi.json against tests/contracts/openapi.baseline.json.
Public API surface
404+ endpoints across /api/v1/*. Grouped by domain: ai, audit, consent, dsfa, dsr, gdpr, vendor, evidence, change-requests, generation, projects, company-profile, isms. Every path is a contract — see the "Public endpoints" rule in the root CLAUDE.md.
Environment
| Var | Purpose |
|---|---|
COMPLIANCE_DATABASE_URL |
Postgres DSN, sslmode=require |
KEYCLOAK_* |
Auth verification |
QDRANT_URL, QDRANT_API_KEY |
Vector search |
CORE_VALKEY_URL |
Session cache |
Don't touch
Database schema, __tablename__, column names, existing migrations under migrations/. See root CLAUDE.md rule 3.