Fix claims parsing: truncated JSON recovery + chunking + compact output
config.py parse_llm_json: Added truncated JSON recovery. When Claude's output is cut mid-JSON (common with long claim lists), the parser now: - Finds the last complete JSON item (closing "}") - Closes the array/object brackets - Returns partial but valid results instead of None Tested: recovers 2/3 items from truncated array, all cases pass. claims_extractor.py: - Prompt asks for compact output (150 words max per claim, group similar) - Explicitly requests "no markdown, no explanations, JSON only" - Long documents split into chunks at paragraph boundaries - Each chunk processed separately, results merged - max_tokens already at 8192 This fixes the recurring "0 claims" bug for committee responses and permit applicant responses where the JSON was getting truncated. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@@ -72,13 +72,19 @@ AUDIT_ENABLED = os.environ.get("AUDIT_ENABLED", "true").lower() == "true"
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# ── Utility ───────────────────────────────────────────────────────
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def parse_llm_json(raw: str):
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"""Parse JSON from LLM response, stripping markdown code blocks and extra text."""
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"""Parse JSON from LLM response, handling markdown wrapping and truncation.
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Handles:
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1. Markdown ```json ... ``` code blocks
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2. Extra text before/after JSON
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3. Truncated JSON (missing closing brackets) — attempts recovery
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"""
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import json
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import re
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raw = raw.strip()
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# Strip markdown code blocks
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raw = re.sub(r"^```(?:json)?\s*\n?", "", raw)
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raw = re.sub(r"\n?\s*```$", "", raw)
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raw = re.sub(r"\n?\s*```\s*$", "", raw)
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# Try direct parse first
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try:
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return json.loads(raw)
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@@ -92,4 +98,38 @@ def parse_llm_json(raw: str):
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return json.loads(match.group())
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except json.JSONDecodeError:
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continue
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# Attempt truncated JSON recovery:
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# Find the start of JSON, then try closing open brackets
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for opener, closer in [("[", "]"), ("{", "}")]:
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start = raw.find(opener)
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if start < 0:
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continue
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fragment = raw[start:]
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# Try progressively removing trailing partial content and closing
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# Look for the last complete item (ending with }, or ])
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for end_pattern in [r'.*\}(?=\s*,?\s*$)', r'.*\](?=\s*,?\s*$)', r'.*"(?=\s*$)']:
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pass # fallback below
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# Simple approach: find last complete JSON item boundary
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# For arrays: find last "}" and close the array
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if opener == "[":
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last_brace = fragment.rfind("}")
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if last_brace > 0:
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truncated = fragment[:last_brace + 1] + "]"
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try:
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return json.loads(truncated)
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except json.JSONDecodeError:
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pass
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# For objects: find last complete key-value
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if opener == "{":
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last_brace = fragment.rfind("}")
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if last_brace > 0:
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# Check if this closes a nested object — try adding outer close
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truncated = fragment[:last_brace + 1]
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# Count unclosed braces
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open_count = truncated.count("{") - truncated.count("}")
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truncated += "}" * open_count
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try:
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return json.loads(truncated)
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except json.JSONDecodeError:
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pass
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return None
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@@ -44,16 +44,13 @@ EXTRACT_CLAIMS_PROMPT = """אתה מנתח מסמכים משפטיים בתחו
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- permit_applicant — מבקש/ת היתר
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## פלט:
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החזר JSON array בלבד:
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[
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{
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"party_role": "appellant",
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"claim_text": "הטענה בגוף שלישי, בעברית",
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"topic": "נושא הטענה בקצרה (3-5 מילים)"
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}
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]
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החזר JSON array בלבד — ללא markdown, ללא הסברים, רק JSON:
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[{"party_role": "appellant", "claim_text": "הטענה בגוף שלישי", "topic": "נושא"}]
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אם אין טענות — החזר [].
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חשוב:
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- claim_text קצר — עד 150 מילים לכל טענה
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- קבץ טענות דומות לטענה אחת
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- אם אין טענות החזר []
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"""
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@@ -72,42 +69,59 @@ async def extract_claims_with_ai(
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Returns:
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רשימת טענות עם party_role, claim_text, topic
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"""
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# For very long documents, truncate but try to keep complete paragraphs
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max_chars = 25000
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if len(text) > max_chars:
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# Find a paragraph break near the limit
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cutoff = text.rfind("\n\n", 0, max_chars)
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if cutoff < max_chars // 2:
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cutoff = max_chars
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sample = text[:cutoff]
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logger.info("Document truncated from %d to %d chars", len(text), len(sample))
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else:
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sample = text
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context = f"סוג המסמך: {doc_type}"
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if party_hint:
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context += f"\nהצד המגיש: {party_hint}"
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client = _get_anthropic()
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message = client.messages.create(
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model="claude-sonnet-4-20250514",
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max_tokens=8192,
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messages=[
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{
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"role": "user",
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"content": (
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f"{EXTRACT_CLAIMS_PROMPT}\n\n"
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f"{context}\n\n"
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f"--- תחילת מסמך ---\n{sample}\n--- סוף מסמך ---"
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),
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}
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],
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)
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# For very long documents, split into chunks and merge results
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max_chars_per_call = 25000
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chunks = []
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if len(text) > max_chars_per_call:
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# Split at paragraph boundaries
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pos = 0
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while pos < len(text):
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end = min(pos + max_chars_per_call, len(text))
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if end < len(text):
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# Find paragraph break near the limit
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break_pos = text.rfind("\n\n", pos, end)
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if break_pos > pos + max_chars_per_call // 2:
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end = break_pos
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chunks.append(text[pos:end])
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pos = end
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logger.info("Document split into %d chunks (%d chars total)", len(chunks), len(text))
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else:
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chunks = [text]
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raw = message.content[0].text.strip()
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claims = parse_llm_json(raw)
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if claims is None:
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logger.warning("Failed to parse claims response: %s", raw[:200])
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all_claims = []
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client = _get_anthropic()
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for i, chunk in enumerate(chunks):
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chunk_label = f" (חלק {i+1}/{len(chunks)})" if len(chunks) > 1 else ""
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message = client.messages.create(
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model="claude-sonnet-4-20250514",
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max_tokens=8192,
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messages=[
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{
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"role": "user",
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"content": (
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f"{EXTRACT_CLAIMS_PROMPT}\n\n"
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f"{context}{chunk_label}\n\n"
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f"--- תחילת מסמך ---\n{chunk}\n--- סוף מסמך ---"
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),
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}
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],
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)
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raw = message.content[0].text.strip()
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claims = parse_llm_json(raw)
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if claims is None:
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logger.warning("Failed to parse claims for chunk %d: %s", i, raw[:200])
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continue
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if isinstance(claims, list):
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all_claims.extend(claims)
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claims = all_claims
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if not claims:
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return []
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if not isinstance(claims, list):
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