Benjamin Admin 07e392913f feat(knowledge-intake): classify a document + assess its impact before extraction
Phase A1. The real knowledge production is not writing — it is TARGETED UPDATING: when 20 documents
arrive, which 5 change our knowledge and which 15 are ignorable? Before the parser, Knowledge Intake
classifies a new document (no content extraction) and intersects its signals with an index of the
existing knowledge to emit a Knowledge Package (an impact analysis).

- compliance/knowledge_intake/: build_knowledge_index(patterns, playbooks, reference_scenarios,
  obligation_index) + assess_document_impact(descriptor, index) -> KnowledgePackage. Deterministic,
  NO content extraction, NO LLM. Surfaces affected capabilities / playbooks / transition patterns /
  reference scenarios / (injected) obligations, whether it is a new domain, and a triage level
  (HIGH / LOW / NONE / NEW_DOMAIN) with a recommendation.
- ADR-006: Knowledge Intake = classify + impact before extraction; full factory Intake -> Package ->
  Parser -> Draft -> Review -> Published; phase order A1 Intake / A2 Draft / A3 Review.
- reference suite: "Knowledge Intake" section triages 3 example documents (CRA SBOM-FAQ -> high,
  14C/2PB/3RTS/2Obl; environmental guidance -> new_domain; marketing blog -> ignorable). Section
  lives in _helpers.py to keep generate.py under the 500-LOC budget.
- Honest known refinement surfaced by intake: regulation-ID normalization (CRA vs Cyber Resilience Act).

10 intake tests (60 with the adjacent modules), mypy --strict clean (16 files), check-loc 0.
Product code with no app caller + ADR/reference = non-runtime -> no deploy (ADR-001). Freeze-safe.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-27 13:58:59 +02:00

breakpilot-compliance

DSGVO/AI-Act compliance platform — 10 services, Go · Python · TypeScript

CI Go Python Node.js TypeScript FastAPI DSGVO AI Act LOC guard Services


Overview

breakpilot-compliance is a multi-tenant DSGVO/EU AI Act compliance platform that provides an SDK for consent management, data subject requests (DSR), audit logging, iACE impact assessments, and document archival. It ships as 10 containerised services covering an admin dashboard, a developer portal, a Python/FastAPI backend, a Go AI compliance engine, TTS, and a decentralised document store on IPFS. Every service is deployed automatically via Gitea Actions → Orca on every push to main.


Architecture

Service Tech Port Container
admin-compliance Next.js 15 3007 bp-compliance-admin
backend-compliance Python / FastAPI 0.123 8002 bp-compliance-backend
ai-compliance-sdk Go 1.24 / Gin 8093 bp-compliance-ai-sdk
developer-portal Next.js 15 3006 bp-compliance-developer-portal
breakpilot-compliance-sdk TypeScript SDK (React/Vue/Angular/vanilla)
consent-sdk JS/TS Consent SDK
compliance-tts-service Python / Piper TTS 8095 bp-compliance-tts
document-crawler Python / FastAPI 8098 bp-compliance-document-crawler
dsms-gateway Python / FastAPI / IPFS 8082 bp-compliance-dsms-gateway
dsms-node IPFS Kubo v0.24.0 bp-compliance-dsms-node

All containers share the external breakpilot-network Docker network and depend on breakpilot-core (Valkey, Vault, RAG service, Nginx reverse proxy).


Quick Start

Prerequisites: Docker, Go 1.24+, Python 3.12+, Node.js 20+, Infisical CLI

git clone ssh://git@gitea.meghsakha.com:22222/Benjamin_Boenisch/breakpilot-compliance.git
cd breakpilot-compliance

# One-time per machine: log in to the self-hosted Infisical instance
infisical login --domain https://secrets.meghsakha.com

# Start the full stack with secrets injected from Infisical (env=dev)
make dev

Secrets are pulled from Infisical (secrets.meghsakha.com) at runtime; .env files are not used. See INFISICAL_SETUP.md for full onboarding, and make help for the rest of the targets (dev-build, dev-down, secrets, secrets-set).

For the Orca/Hetzner production target (x86_64), use the override:

make dev ENV=prod  # or:
infisical run --env=prod -- docker compose -f docker-compose.yml -f docker-compose.hetzner.yml up -d

Development Workflow

Use feature branches off main. Supported prefixes: feat/, feature/, hotfix/.

git checkout main && git pull origin main
git checkout -b feat/my-change
# ... make changes ...
git push origin feat/my-change
# Open a PR → squash merge to main

Push to main triggers:

  1. Gitea Actions — lint → test → validate (see CI Pipeline below)
  2. Orca — automatic build + deploy (~3 min total)

Monitor status: https://gitea.meghsakha.com/Benjamin_Boenisch/breakpilot-compliance/actions


CI Pipeline

Defined in .gitea/workflows/ci.yaml.

Job What it checks
loc-budget All source files ≤ 500 LOC; soft target 300
guardrail-integrity Commits touching guardrail files carry [guardrail-change]
go-lint golangci-lint on ai-compliance-sdk/
python-lint ruff + mypy on Python services
nodejs-lint tsc --noEmit + ESLint on Next.js services
test-go-ai-compliance go test ./... in ai-compliance-sdk/
test-python-backend-compliance pytest in backend-compliance/
test-python-document-crawler pytest in document-crawler/
test-python-dsms-gateway pytest test_main.py in dsms-gateway/
sbom-scan License + vulnerability scan via syft + grype
validate-canonical-controls OpenAPI contract baseline diff

File Budget

Limit Value How to check
Soft target 300 LOC bash scripts/check-loc.sh
Hard cap 500 LOC Same; also enforced by PreToolUse hook + git pre-commit + CI
Exceptions .claude/rules/loc-exceptions.txt Require written rationale + [guardrail-change] commit marker

The .claude/settings.json PreToolUse hook blocks Claude Code from writing or editing files that would exceed the hard cap. The git pre-commit hook re-checks. CI is the final gate.


URL
Admin dashboard https://admin-dev.breakpilot.ai
Developer portal https://developers-dev.breakpilot.ai
Backend API https://api-dev.breakpilot.ai
AI SDK API https://sdk-dev.breakpilot.ai
Gitea repo https://gitea.meghsakha.com/Benjamin_Boenisch/breakpilot-compliance
Gitea Actions https://gitea.meghsakha.com/Benjamin_Boenisch/breakpilot-compliance/actions
S
Description
No description provided
Readme 40 MiB
Languages
TypeScript 39.8%
Python 35.3%
Go 22.4%
Shell 1.1%
PLpgSQL 0.7%
Other 0.4%