Each check now has a "hint" field explaining what is missing and
what the customer should do to fix it. Hints are shown in the
frontend below failed checks in red text.
Examples:
- "Bei Verarbeitung auf Basis von Art. 6(1)(f) muss dokumentiert
werden, warum Ihr berechtigtes Interesse die Rechte der
Betroffenen ueberwiegt."
- "Die ladungsfaehige Anschrift fehlt. Erforderlich: Strasse,
Hausnummer, PLZ und Ort."
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Neue Dateien: packaging, medical_pressure, specific_machines2
Split: food_pkg aufgeteilt in food_processing + packaging
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Patterns ran on text.lower() but searched [A-Z] — changed to [a-z].
Also accept D-12345 prefix (common German format).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
L2 checks were hidden behind a second click on L1 items.
Now they render inline below their L1 parent, always visible
when the document card is expanded.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Local origin is 20+ commits ahead of remote gitea. All conflicts
resolved by keeping HEAD (our version) which includes the full
56→138 check expansion and doc_checks package split.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
'Datenschutzfolgeabschätzung...Social Media' was matching as social_media
(Art. 26) instead of dsfa (Art. 35) because the social_media pattern
'datenschutz.*social media' matched first.
Fixed: DSFA patterns checked before social_media patterns.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
8 false positives from generic canonical_controls. Regex checks (9+5)
are accurate. Re-enable when ~80 specific doc-check controls exist.
See INSTRUCTION-master-controls-for-doc-check.md
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Only use controls whose test_procedure mentions document-type-specific terms:
- DSI: test_procedure must contain 'datenschutzerkl' or 'art. 13/14'
- Cookie: must contain 'cookie', 'einwilligung', 'consent'
- Impressum: must contain 'impressum'
This filters out internal governance controls (Datenmodelle, Infrastruktur)
that are irrelevant for public document checks.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Complete rewrite of rag_document_checker.py:
- Queries canonical_controls table (294K controls, 10K data_protection)
- Filters by category + title keywords per document type
- Uses test_procedure field as actual check instructions
- Regex pre-check extracts key terms from procedure → fast match
- LLM fallback only for regex misses (saves tokens)
- /no_think prefix for direct JSON output
SQL approach advantages:
- Structured data with test_procedure, pass_criteria, fail_criteria
- Category filtering (data_protection, compliance, governance)
- No Qdrant API key issues
- Controls are actual check criteria, not general legal texts
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Current 144K controls are general legal texts, not specific check criteria.
RAG integration code stays (rag_document_checker.py), just disabled in
the doc-check endpoint. Re-enable when G1-G4 block is complete and
25K Master Controls with Decision Trace are available.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
LLM returns {fulfilled: true} instead of {"fulfilled": true}.
Now fixes unquoted keys, True→true, and falls back to text-based
boolean extraction when JSON parsing fails entirely.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Qwen 3.5 uses all tokens for thinking, leaving response empty.
Using /no_think prefix to get direct JSON output.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Qwen 3.5 with latest Ollama returns structured thinking in separate
'thinking' field, leaving 'response' empty. Now checks both fields.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replaces scroll+filter approach with proper semantic search:
1. Embed query via bp-core-embedding-service (bge-m3, 1024 dim)
2. Vector search in Qdrant (bp_compliance_datenschutz + bp_compliance_gesetze)
3. Sort by cosine similarity score
4. No API key needed — local Qdrant on Mac Mini
Falls back gracefully: SDK first, then semantic Qdrant, then empty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Go SDK points to external Qdrant (qdrant-dev.breakpilot.ai) with expired API key.
Fallback: search directly in local Qdrant (bp-core-qdrant:6333) which has
all collections: bp_compliance_datenschutz, bp_compliance_gesetze, atomic_controls_dedup.
Search strategy:
1. Try Go SDK RAG endpoint (preferred, has embedding-based search)
2. Fallback: Qdrant scroll with text-based regulation filter
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New module: rag_document_checker.py
- Searches RAG (Qdrant) for controls relevant to document type
- Filters by regulation (DSGVO Art.13, TDDDG §25, BGB §355 etc.)
- LLM (Qwen 3.5:35b) verifies each control against document text
- Returns fulfilled/missing with evidence text + severity
- Supports: DSI, Cookie, Impressum, Widerruf, AGB, DSFA, AVV, Loeschkonzept
Integration in doc-check endpoint:
- Regex checklist runs first (fast, deterministic)
- RAG checks run after (semantic, catches what regex misses)
- Both results combined in single response
LLM prompt returns JSON: {fulfilled, evidence, issue, severity}
Think-tags stripped, JSON extracted from response.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Only Cookie and Widerruf sections are checked as separate documents.
Social Media, DSFA, Betroffenenrechte, Dienste von Drittanbietern are
part of the parent DSI and no longer generate false findings.
Added PLAN-rag-document-check.md for Phase 2:
- RAG-based checks with document-type-specific Controls
- DSFA checklist (Art. 35 + Landes-Listen)
- AVV checklist (Art. 28)
- Reference detection (sub-doc → parent doc)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When a single URL contains multiple document sections (e.g. IHK DSI page
with Cookies, Social Media, Dienste von Drittanbietern), the system now:
1. Extracts full page text (main document check as before)
2. Splits text at heading boundaries (short uppercase lines)
3. Classifies each section: Cookie→cookie checklist, Social Media→DSI etc.
4. Runs type-specific checklist per section
5. Returns all results: main doc + sub-sections
Section type detection via SECTION_TYPE_MAP patterns:
- 'Cookie*' → §25 TDDDG checklist
- 'Dienste von Drittanbietern' → DSI checklist
- 'Social Media' → DSI checklist (Art. 26 joint controllership)
- 'Widerrufsrecht' → §355 BGB checklist
- 'Impressum' → §5 TMG checklist
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New "Dokumenten-Pruefung" tab in Compliance Agent:
- User adds multiple URLs with document type (DSI, AGB, Impressum, Cookie, Widerruf)
- Each document loaded via Playwright, accordions expanded, text extracted
- Checked against type-specific legal checklist
- Optional: Cookie banner check via checkbox
Checklisten-UX (solves "100% looks like nothing was checked"):
- All checks shown per document: green checkmark + matched text excerpt
- Red X for missing fields with legal reference
- Builds user trust: "9 Punkte geprueft, alle bestanden"
- Expandable per document with completeness bar
New checklists:
- Impressum: §5 TMG (6 fields: name, address, contact, register, VAT, representative)
- Cookie-Richtlinie: §25 TDDDG (5 fields: types, purposes, retention, third-party, opt-out)
Backend:
- POST /agent/doc-check — async with polling (same pattern as /scan)
- DocCheckResult includes checks[] with passed/failed + matched_text
- dsi_document_checker returns all_checks in SCORE finding
- Email report shows per-document checklist
Files: agent_doc_check_routes.py (280 LOC), DocCheckTab.tsx (248 LOC),
ChecklistView.tsx (130 LOC), dsi_document_checker.py (+70 LOC)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Each scan is a separate entry so users can track changes over time.
Increased max entries from 20 to 50.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Service detection:
- Only search script tags + src/href attributes for service patterns
- Prevents false positives from DSE text mentioning services
(e.g. IHK DSE describes etracker, 'google analytics' in text)
- Technical patterns (with regex chars) still checked in full HTML
Short documents:
- Documents with < 200 words flagged as 'Kurzhinweis' instead of
'MANGELHAFT' — too short for Art. 13 completeness check
- Prevents 96-word navigation pages from showing 8 missing fields
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two fixes:
1. consent-tester: full_text truncation raised from 10,000 to 50,000 chars
(IHK Internetangebot has ~50K chars, Beschwerderecht was after 10K cutoff)
2. Backend: dse_text now combines Playwright HTML + ALL DSI discovery texts
for mandatory content checking. Previously only used first 8K chars from
one source, missing Verantwortlicher/DSB that were in DSI documents.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: When all 9 Art. 13 checks passed (100%), no SCORE finding
was created (line: 'if pct < 100'). The backend then defaulted to
completeness=0 because it looked for the SCORE finding to extract the %.
Fix: Always generate SCORE finding, even at 100%. Added 'OK' severity
for fully compliant documents.
This was the cause of 8 documents showing '0% MANGELHAFT' despite
containing all required information.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Benjamin Admin