Benjamin Admin
2d2cb2a244
The bottleneck is knowledge, not the endpoint. This builds the knowledge the Onboarding Advisor needs, restructured per the user's key insight: NOT "ISO27001 -> 30 capabilities" but each hypothesis as its own object "capability -> supported_by: [certs]". A capability is written ONCE with all supporting certs, so the shared management-system core (document control, incident, supplier, audit, access, asset, monitoring, training, crypto, release, risk) covers most certifications with ~18 hypotheses instead of ~300 — and multi-certification merges AUTOMATICALLY (a company's inferred caps = every hypothesis whose supported_by intersects its certs). Welt-1 throughout: "IF cert present, EXPECT capability (verification required)", never "erfüllt". Capabilities NO cert suggests (SBOM, signed updates, CVD, support period) have no hypothesis -> they stay in the delta and get asked. confidence is EMPIRICAL: computed from real-onboarding observations (confirmed/(confirmed+refuted)), None until calibrated — never an LLM/expert score (record_observation + empirical_confidence). The long-term moat: knowledge that learns from reality, not from a norm. compliance/onboarding/hypotheses.py (resolve_for_certifications / inferred_hypotheses / empirical_ confidence / record_observation) feeds the existing advisor_start unchanged; the demo now runs on the curated library. Pure, mypy --strict clean, library is DATA (no norm text, no real names). Non-runtime -> no deploy. 12 tests pass, check-loc 0.
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.