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- LLM verify now sends ALL failed checks in one batched call instead of one Ollama call per check (80+ calls → 1 per document) - Increase frontend poll timeout from 6 min to 15 min Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
"""
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LLM verification for regex check results.
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When a regex check FAILs, the LLM re-checks the original text
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to confirm or overturn the finding. This eliminates false positives
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caused by regex limitations (unusual formatting, synonyms, etc.).
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Uses the self-hosted Ollama endpoint (Qwen) for fast local inference.
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"""
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import logging
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import os
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import httpx
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logger = logging.getLogger(__name__)
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OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
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OLLAMA_MODEL = os.getenv("OLLAMA_VERIFY_MODEL", "qwen3.5:35b-a3b")
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TIMEOUT = 30.0
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async def verify_failed_checks(
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text: str,
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failed_checks: list[dict],
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doc_title: str,
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) -> dict[str, dict]:
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"""Verify regex FAIL results using LLM — single batched call.
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Sends ALL failed checks in one LLM prompt instead of one call per check.
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Returns a dict mapping check_id -> {"overturned": bool, "evidence": str}.
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"""
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results: dict[str, dict] = {}
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checks_with_hints = [c for c in failed_checks if c.get("hint")]
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if not checks_with_hints:
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return results
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# Truncate text to fit context window
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text_excerpt = text[:8000]
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try:
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batch_results = await _ask_llm_batch(
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text_excerpt, checks_with_hints, doc_title,
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)
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for check_id, answer in batch_results.items():
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overturned = answer.get("found", False)
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results[check_id] = {
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"overturned": overturned,
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"evidence": answer.get("evidence", ""),
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}
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if overturned:
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logger.info(
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"LLM overturned regex FAIL for '%s' in '%s': %s",
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check_id, doc_title, answer.get("evidence", "")[:80],
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)
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except Exception as e:
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logger.warning("LLM batch verify failed for '%s': %s", doc_title, e)
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return results
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async def _ask_llm_batch(
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text: str, checks: list[dict], doc_title: str,
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) -> dict[str, dict]:
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"""Ask the LLM to verify ALL failed checks in a single call."""
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checklist_lines = []
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for i, c in enumerate(checks, 1):
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checklist_lines.append(
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f'{i}. ID="{c["id"]}" | {c["label"]} | {c.get("hint", "")[:120]}'
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)
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checklist_str = "\n".join(checklist_lines)
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prompt = f"""/no_think
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Pruefe ob der Dokumenttext die folgenden Anforderungen erfuellt.
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DOKUMENT: "{doc_title}"
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ANFORDERUNGEN:
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{checklist_str}
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TEXT:
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{text}
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Antworte NUR mit einem JSON-Array (keine Erklaerung). Fuer jede Anforderung:
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[{{"id": "check-id", "found": true/false, "evidence": "Kurzes Zitat (max 80 Zeichen) oder leer"}}]
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"""
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async with httpx.AsyncClient(timeout=90.0) as client:
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resp = await client.post(
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f"{OLLAMA_URL}/api/generate",
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json={
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"model": OLLAMA_MODEL,
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": 0.0, "num_predict": 2000},
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},
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)
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resp.raise_for_status()
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raw = resp.json().get("response", "")
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return _parse_batch_response(raw, checks)
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def _parse_batch_response(raw: str, checks: list[dict]) -> dict[str, dict]:
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"""Parse batch LLM JSON array response."""
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import json
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import re
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results: dict[str, dict] = {}
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raw = raw.strip()
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# Extract JSON array from markdown code blocks
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m = re.search(r"```(?:json)?\s*(\[.*?\])\s*```", raw, re.DOTALL)
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if m:
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raw = m.group(1)
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else:
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m = re.search(r"\[.*\]", raw, re.DOTALL)
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if m:
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raw = m.group(0)
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try:
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items = json.loads(raw)
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if isinstance(items, list):
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for item in items:
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cid = item.get("id", "")
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if cid:
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results[cid] = {
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"found": bool(item.get("found", False)),
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"evidence": str(item.get("evidence", ""))[:150],
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}
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except (json.JSONDecodeError, ValueError):
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# Fallback: extract individual JSON objects
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for m in re.finditer(r'\{[^}]*"id"\s*:\s*"([^"]+)"[^}]*"found"\s*:\s*(true|false)[^}]*\}', raw, re.DOTALL):
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cid = m.group(1)
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found = m.group(2) == "true"
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results[cid] = {"found": found, "evidence": ""}
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logger.info("LLM batch: %d/%d checks parsed", len(results), len(checks))
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return results
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