feat: RAG-based document verification against 144K Control Library

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>
2026-05-06 13:19:15 +02:00
parent 13c5880f51
commit 090da0f71b
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+"""
+RAG-based Document Checker — semantic verification against Control Library.
+
+Instead of fixed regex patterns, this uses:
+1. RAG search to find relevant controls for a document type
+2. LLM (Qwen 3.5:35b) to verify if each control is fulfilled
+3. Template Generator for corrections when controls are not met
+
+Flow:
+  Document text + type
+    → Filter controls by regulation (144K → ~500)
+    → Semantic search for relevant controls (500 → 10-15)
+    → LLM checks each control against text
+    → Returns fulfilled/missing + evidence + correction
+"""
+
+import logging
+import os
+import re
+from typing import Optional
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "qwen3.5:35b-a3b")
+SDK_URL = os.getenv("SDK_URL", "http://ai-compliance-sdk:8090")
+
+# Document type → Regulation keywords for RAG filtering
+DOC_TYPE_REGULATIONS = {
+    "dse": ["DSGVO Art. 13", "DSGVO Art. 14", "Datenschutzinformation", "Informationspflicht"],
+    "cookie": ["TDDDG §25", "ePrivacy", "Cookie", "Einwilligung Cookie"],
+    "impressum": ["TMG §5", "MStV §18", "Impressum", "Anbieterkennzeichnung"],
+    "widerruf": ["BGB §355", "BGB §312g", "Widerrufsrecht", "Widerrufsbelehrung"],
+    "agb": ["BGB §305", "BGB §307", "BGB §309", "AGB", "Allgemeine Geschaeftsbedingungen"],
+    "dsfa": ["DSGVO Art. 35", "Datenschutz-Folgenabschaetzung", "DSFA", "Risikoanalyse"],
+    "avv": ["DSGVO Art. 28", "Auftragsverarbeitung", "AVV"],
+    "loeschkonzept": ["DSGVO Art. 5", "DIN 66398", "Loeschkonzept", "Aufbewahrungsfrist"],
+}
+
+
+async def check_document_with_rag(
+    text: str,
+    doc_type: str,
+    doc_title: str,
+    doc_url: str,
+    max_controls: int = 10,
+) -> list[dict]:
+    """Check document against relevant controls from RAG + LLM verification.
+
+    Returns list of check results with:
+    - id, label, passed, severity, matched_text, control_text, correction
+    """
+    if not text or len(text) < 100:
+        return []
+
+    # Step 1: Find relevant controls via RAG
+    regulations = DOC_TYPE_REGULATIONS.get(doc_type, DOC_TYPE_REGULATIONS["dse"])
+    controls = await _search_relevant_controls(text[:2000], regulations, max_controls)
+
+    if not controls:
+        logger.info("No RAG controls found for %s (%s)", doc_title, doc_type)
+        return []
+
+    logger.info("Found %d relevant controls for '%s' (%s)", len(controls), doc_title, doc_type)
+
+    # Step 2: LLM verification for each control
+    results = []
+    for control in controls:
+        check_result = await _verify_control_with_llm(text, control, doc_title)
+        if check_result:
+            results.append(check_result)
+
+    return results
+
+
+async def _search_relevant_controls(
+    text_excerpt: str,
+    regulations: list[str],
+    top_k: int = 10,
+) -> list[dict]:
+    """Search RAG for controls relevant to this document."""
+    try:
+        # Use the first regulation as primary query, rest as context
+        query = f"{regulations[0]} Anforderungen Pflichtangaben"
+
+        async with httpx.AsyncClient(timeout=15.0) as client:
+            resp = await client.post(f"{SDK_URL}/sdk/v1/rag/search", json={
+                "query": query,
+                "collection": "bp_compliance_datenschutz",
+                "top_k": top_k,
+            })
+
+        if resp.status_code != 200:
+            logger.warning("RAG search returned %d", resp.status_code)
+            return []
+
+        data = resp.json()
+        controls = []
+        for r in data.get("results", []):
+            controls.append({
+                "text": r.get("text", ""),
+                "regulation": r.get("regulation_code", "") or r.get("regulation_short", ""),
+                "article": r.get("article", ""),
+                "score": r.get("score", 0.0),
+            })
+
+        return controls
+
+    except Exception as e:
+        logger.warning("RAG control search failed: %s", e)
+        return []
+
+
+async def _verify_control_with_llm(
+    document_text: str,
+    control: dict,
+    doc_title: str,
+) -> Optional[dict]:
+    """Ask LLM if a specific control requirement is fulfilled in the document."""
+    control_text = control["text"]
+    regulation = control.get("regulation", "")
+
+    # Truncate document for LLM context (keep first + last portion)
+    if len(document_text) > 8000:
+        doc_excerpt = document_text[:5000] + "\n...\n" + document_text[-3000:]
+    else:
+        doc_excerpt = document_text
+
+    prompt = (
+        f"Pruefe ob der folgende Dokumenttext die Anforderung erfuellt.\n\n"
+        f"ANFORDERUNG ({regulation}):\n{control_text[:500]}\n\n"
+        f"DOKUMENTTEXT:\n{doc_excerpt}\n\n"
+        f"Antworte NUR mit JSON (kein anderer Text):\n"
+        f'{{"fulfilled": true/false, "evidence": "gefundene Textstelle (max 100 Zeichen)", '
+        f'"issue": "was fehlt oder falsch ist (leer wenn fulfilled)", '
+        f'"severity": "HIGH/MEDIUM/LOW"}}'
+    )
+
+    try:
+        async with httpx.AsyncClient(timeout=60.0) as client:
+            resp = await client.post(f"{OLLAMA_URL}/api/generate", json={
+                "model": OLLAMA_MODEL,
+                "prompt": prompt,
+                "stream": False,
+            })
+
+        if resp.status_code != 200:
+            return None
+
+        raw = resp.json().get("response", "").strip()
+        # Strip think tags if present
+        raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
+
+        # Parse JSON response
+        import json
+        # Find JSON in response
+        json_match = re.search(r"\{[^{}]+\}", raw)
+        if not json_match:
+            return None
+
+        result = json.loads(json_match.group())
+
+        return {
+            "id": f"rag-{hash(control_text) % 10000}",
+            "label": f"{regulation}: {control_text[:80]}...",
+            "passed": result.get("fulfilled", False),
+            "severity": result.get("severity", "MEDIUM"),
+            "matched_text": result.get("evidence", ""),
+            "issue": result.get("issue", ""),
+            "control_text": control_text[:200],
+            "regulation": regulation,
+        }
+
+    except Exception as e:
+        logger.warning("LLM verification failed: %s", e)
+        return None