feat: DSI document discovery + completeness check in agent scan workflow

Agent scan now automatically:
1. Discovers all legal documents via consent-tester /dsi-discovery endpoint
2. Classifies each as DSE/AGB/Widerruf/Cookie/Impressum
3. Checks completeness against type-specific checklists:
   - DSE: 9 Art. 13 DSGVO mandatory fields (controller, DPO, purposes,
     legal basis, recipients, third-country, retention, rights, complaint)
   - AGB: §305ff BGB (scope, contract formation, liability, jurisdiction)
   - Widerruf: §355 BGB (right info, 14-day deadline, form, consequences)
4. Adds findings per document to scan results
5. Shows discovered documents with completeness % in email summary
6. Returns discovered_documents list in API response

New files:
- dsi_document_checker.py (229 LOC) — checklists + classifier
- agent_scan_helpers.py (109 LOC) — extracted summary builder + corrections

Refactor: agent_scan_routes.py 537→448 LOC (under 500 budget)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend-compliance/compliance/api/agent_scan_helpers.py

@@ -0,0 +1,109 @@
+"""
+Agent scan helpers — summary builder and correction generator.
+Extracted from agent_scan_routes.py to keep route file under 500 LOC.
+"""
+
+import logging
+import os
+import re
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+async def add_corrections(findings: list, dse_text: str) -> None:
+    """Add correction suggestions for pre-launch mode via LLM."""
+    for finding in findings:
+        if finding.severity in ("HIGH", "MEDIUM") and "MISSING" in finding.code:
+            service_name = finding.code.replace("DSE-MISSING-", "").replace("_", " ").title()
+            try:
+                ollama_url = os.environ.get("OLLAMA_URL", "http://host.docker.internal:11434")
+                ollama_model = os.environ.get("OLLAMA_MODEL", "qwen3.5:35b-a3b")
+                async with httpx.AsyncClient(timeout=120.0) as client:
+                    resp = await client.post(f"{ollama_url}/api/generate", json={
+                        "model": ollama_model,
+                        "prompt": (
+                            f"Erstelle einen einbaufertigen Textbaustein fuer eine deutsche "
+                            f"Datenschutzerklaerung fuer den Dienst '{service_name}'. "
+                            f"Enthalte: Ueberschrift, Anbietername mit Sitz, Zweck der Verarbeitung, "
+                            f"Rechtsgrundlage nach DSGVO, Drittlandtransfer-Hinweis wenn noetig, "
+                            f"Widerspruchsmoeglichkeit. Max 150 Woerter. "
+                            f"Antworte NUR mit dem fertigen Textbaustein."
+                        ),
+                        "stream": False,
+                    })
+                    data = resp.json()
+                    raw = data.get("response", "").strip()
+                    raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
+                    if raw and len(raw) > 50:
+                        finding.correction = raw
+            except Exception as e:
+                logger.warning("Correction generation failed for %s: %s", service_name, e)
+
+
+def build_scan_summary(
+    url: str, scan, comparison: dict, findings: list, is_live: bool,
+    discovered_docs: list | None = None,
+) -> str:
+    """Build German scan summary including DSI document results."""
+    mode = "PRUEFUNG LIVE-WEBSITE" if is_live else "INTERNE PRUEFUNG"
+    n_undoc = len(comparison["undocumented"])
+    n_ok = len(comparison["documented"])
+    n_outdated = len(comparison["outdated"])
+    n_findings = len(findings)
+    high = sum(1 for f in findings if f.severity == "HIGH")
+
+    parts = [
+        f"{mode} — Website-Scan",
+        f"URL: {url}",
+        f"Seiten gescannt: {len(scan.pages_scanned)}",
+    ]
+    for page in scan.pages_scanned:
+        status = scan.missing_pages.get(page, 200)
+        marker = "\u2717" if status >= 400 else "\u2713"
+        parts.append(f"  {marker} {page}" + (f" (HTTP {status})" if status >= 400 else ""))
+    parts.extend([
+        "",
+        "Dienstleister-Abgleich (DSE vs. Website):",
+        f"  Korrekt dokumentiert: {n_ok}",
+        f"  NICHT in DSE (Verstoss): {n_undoc}",
+        f"  Veraltet in DSE: {n_outdated}",
+        "",
+        f"Findings: {n_findings} ({high} mit hoher Prioritaet)",
+    ])
+
+    # DSI Documents section
+    if discovered_docs:
+        parts.extend([
+            "",
+            f"Rechtliche Dokumente gefunden: {len(discovered_docs)}",
+        ])
+        for doc in discovered_docs:
+            pct = doc.completeness_pct if hasattr(doc, 'completeness_pct') else 0
+            fc = doc.findings_count if hasattr(doc, 'findings_count') else 0
+            wc = doc.word_count if hasattr(doc, 'word_count') else 0
+            status = "OK" if pct >= 80 else "LUECKENHAFT" if pct >= 50 else "MANGELHAFT"
+            dt = doc.doc_type if hasattr(doc, 'doc_type') else "unknown"
+            title = doc.title if hasattr(doc, 'title') else "?"
+            parts.append(
+                f"  [{status}] {title} ({dt}, {wc} Woerter, "
+                f"{pct}% vollstaendig, {fc} Maengel)"
+            )
+
+    if findings:
+        parts.append("")
+        for f in findings[:20]:
+            sev = f.severity if hasattr(f, 'severity') else "?"
+            txt = f.text if hasattr(f, 'text') else str(f)
+            marker = "!!" if sev == "HIGH" else "!" if sev == "MEDIUM" else "i"
+            parts.append(f"  [{marker}] {txt}")
+
+    if is_live and high > 0:
+        parts.extend([
+            "",
+            "ACHTUNG: Verstoesse auf einer bereits veroeffentlichten Website. "
+            "Sofortige Korrektur empfohlen.",
+        ])
+
+    return "\n".join(parts)

backend-compliance/compliance/api/agent_scan_routes.py

@@ -22,6 +22,7 @@ from compliance.services.mandatory_content_checker import (
     check_mandatory_documents, check_dse_mandatory_content, MandatoryFinding,
 )
 from compliance.services.legal_basis_validator import validate_legal_bases
+from compliance.api.agent_scan_helpers import add_corrections, build_scan_summary
 
 logger = logging.getLogger(__name__)
 
@@ -78,12 +79,23 @@ class ScanFinding(BaseModel):
     text_reference: TextReferenceModel | None = None
 
 
+class DiscoveredDocument(BaseModel):
+    title: str
+    url: str
+    doc_type: str
+    language: str = ""
+    word_count: int = 0
+    completeness_pct: int = 0
+    findings_count: int = 0
+
+
 class ScanResponse(BaseModel):
     url: str
     pages_scanned: int
     pages_list: list[str] = []
     services: list[ServiceInfo]
     findings: list[ScanFinding]
+    discovered_documents: list[DiscoveredDocument] = []
     ai_detected: bool
     chatbot_detected: bool
     chatbot_provider: str
@@ -140,6 +152,52 @@ async def scan_website_endpoint(req: ScanRequest):
 
     logger.info("Scanned %d pages, found %d services", len(scan.pages_scanned), len(scan.detected_services))
 
+    # Step 1b: DSI Discovery — find all legal documents on the website
+    discovered_docs: list[DiscoveredDocument] = []
+    dsi_findings: list[ScanFinding] = []
+    try:
+        async with httpx.AsyncClient(timeout=180.0) as dsi_client:
+            dsi_resp = await dsi_client.post(
+                "http://bp-compliance-consent-tester:8094/dsi-discovery",
+                json={"url": req.url, "max_documents": 20},
+            )
+            if dsi_resp.status_code == 200:
+                dsi_data = dsi_resp.json()
+                logger.info("DSI discovery: %d documents found", dsi_data.get("total_found", 0))
+
+                # Check each document against its legal requirements
+                from compliance.services.dsi_document_checker import (
+                    check_document_completeness, classify_document_type,
+                )
+                for doc in dsi_data.get("documents", []):
+                    doc_type = classify_document_type(doc["title"], doc["url"])
+                    doc_findings = check_document_completeness(
+                        doc.get("text_preview", ""), doc_type, doc["title"], doc["url"],
+                    )
+                    # Count completeness
+                    score_finding = next((f for f in doc_findings if "SCORE" in f.get("code", "")), None)
+                    completeness = 0
+                    if score_finding:
+                        import re as _re2
+                        pct_match = _re2.search(r"(\d+)%", score_finding.get("text", ""))
+                        if pct_match:
+                            completeness = int(pct_match.group(1))
+
+                    discovered_docs.append(DiscoveredDocument(
+                        title=doc["title"], url=doc["url"],
+                        doc_type=doc_type, language=doc.get("language", ""),
+                        word_count=doc.get("word_count", 0),
+                        completeness_pct=completeness,
+                        findings_count=len([f for f in doc_findings if "SCORE" not in f.get("code", "")]),
+                    ))
+                    for df in doc_findings:
+                        if "SCORE" not in df.get("code", ""):
+                            dsi_findings.append(ScanFinding(
+                                code=df["code"], severity=df["severity"], text=df["text"],
+                            ))
+    except Exception as e:
+        logger.warning("DSI discovery failed: %s", e)
+
     # Step 2: Fetch privacy policy text (from Playwright HTMLs or httpx)
     dse_text = ""
     for page_url, html in playwright_htmls.items():
@@ -215,12 +273,15 @@ async def scan_website_endpoint(req: ScanRequest):
                 ) if lf.original_text else None,
             ))
 
+    # Step 8c: Add DSI document findings
+    findings.extend(dsi_findings)
+
     # Step 9: Generate corrections for pre-launch mode
     if not is_live and findings:
-        await _add_corrections(findings, dse_text)
+        await add_corrections(findings, dse_text)
 
     # Step 7: Build summary
-    summary = _build_scan_summary(req.url, scan, comparison, findings, is_live)
+    summary = build_scan_summary(req.url, scan, comparison, findings, is_live, discovered_docs)
 
     # Step 8: Send notification
     mode_label = "INTERNE PRUEFUNG" if not is_live else "LIVE-WEBSITE"
@@ -236,6 +297,7 @@ async def scan_website_endpoint(req: ScanRequest):
         pages_list=scan.pages_scanned,
         services=services_info,
         findings=findings,
+        discovered_documents=discovered_docs,
         ai_detected=len(scan.ai_mentions) > 0,
         chatbot_detected=scan.chatbot_detected,
         chatbot_provider=scan.chatbot_provider,
@@ -384,79 +446,3 @@ def _build_findings(
     return services, findings
 
 
-async def _add_corrections(findings: list[ScanFinding], dse_text: str) -> None:
-    """Add correction suggestions for pre-launch mode via LLM."""
-    for finding in findings:
-        if finding.severity in ("HIGH", "MEDIUM") and "MISSING" in finding.code:
-            service_name = finding.code.replace("DSE-MISSING-", "").replace("_", " ").title()
-            try:
-                # Call Ollama directly (bypasses SDK RBAC + Think-mode issues)
-                ollama_url = os.environ.get("OLLAMA_URL", "http://host.docker.internal:11434")
-                ollama_model = os.environ.get("OLLAMA_MODEL", "qwen3.5:35b-a3b")
-                async with httpx.AsyncClient(timeout=120.0) as client:
-                    resp = await client.post(f"{ollama_url}/api/generate", json={
-                        "model": ollama_model,
-                        "prompt": (
-                            f"Erstelle einen einbaufertigen Textbaustein fuer eine deutsche "
-                            f"Datenschutzerklaerung fuer den Dienst '{service_name}'. "
-                            f"Enthalte: Ueberschrift, Anbietername mit Sitz, Zweck der Verarbeitung, "
-                            f"Rechtsgrundlage nach DSGVO, Drittlandtransfer-Hinweis wenn noetig, "
-                            f"Widerspruchsmoeglichkeit. Max 150 Woerter. "
-                            f"Antworte NUR mit dem fertigen Textbaustein."
-                        ),
-                        "stream": False,
-                    })
-                    data = resp.json()
-                    import re
-                    raw = data.get("response", "").strip()
-                    raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
-                    if raw and len(raw) > 50:
-                        finding.correction = raw
-            except Exception as e:
-                logger.warning("Correction generation failed for %s: %s", service_name, e)
-
-
-def _build_scan_summary(
-    url: str, scan, comparison: dict, findings: list[ScanFinding], is_live: bool,
-) -> str:
-    """Build German scan summary."""
-    mode = "PRUEFUNG LIVE-WEBSITE" if is_live else "INTERNE PRUEFUNG"
-    n_undoc = len(comparison["undocumented"])
-    n_ok = len(comparison["documented"])
-    n_outdated = len(comparison["outdated"])
-    n_findings = len(findings)
-    high = sum(1 for f in findings if f.severity == "HIGH")
-
-    parts = [
-        f"{mode} — Website-Scan",
-        f"URL: {url}",
-        f"Seiten gescannt: {len(scan.pages_scanned)}",
-    ]
-    for page in scan.pages_scanned:
-        status = scan.missing_pages.get(page, 200)
-        marker = "✗" if status >= 400 else "✓"
-        parts.append(f"  {marker} {page}" + (f" (HTTP {status})" if status >= 400 else ""))
-    parts.extend([
-        "",
-        f"Dienstleister-Abgleich (DSE vs. Website):",
-        f"  Korrekt dokumentiert: {n_ok}",
-        f"  NICHT in DSE (Verstoss): {n_undoc}",
-        f"  Veraltet in DSE: {n_outdated}",
-        "",
-        f"Findings: {n_findings} ({high} mit hoher Prioritaet)",
-    ])
-
-    if findings:
-        parts.append("")
-        for f in findings[:10]:
-            marker = "!!" if f.severity == "HIGH" else "!" if f.severity == "MEDIUM" else "i"
-            parts.append(f"  [{marker}] {f.text}")
-
-    if is_live and high > 0:
-        parts.extend([
-            "",
-            "ACHTUNG: Verstoesse auf einer bereits veroeffentlichten Website. "
-            "Sofortige Korrektur empfohlen.",
-        ])
-
-    return "\n".join(parts)