Fix extraction for companies and religious entities
This commit is contained in:
Georges Haddad
+147
-3
@@ -28,18 +28,19 @@ CRITICAL INSTRUCTIONS:
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- معلومات محولة لغاية (data_valid_until) found near the bottom.
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- أمانة السجل (registry_office) found at the bottom right.
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- عدد العقارات: (declared_property_count) located right under the properties table.
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7. The properties table has 8 columns in order from RIGHT to LEFT as they appear on the page:
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7. If the property owner is a company, religious entity, or organization (e.g., شركة, وقف, مطرانية, جمعية) rather than a natural person, extract its full name into the `first_name` field and leave `father_name`, `mother_name`, `family_name`, etc. as null.
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8. The properties table has 8 columns in order from RIGHT to LEFT as they appear on the page:
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col1(rightmost)=اسم الفريق, col2=رقم العقار, col3=القسم, col4=البلوك,
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col5=المنطقة العقارية, col6=القضاء, col7=عدد الأسهم, col8(leftmost)=نوع الملكية
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Map these to JSON keys: party_name, property_number, section, block, real_estate_district, qaza, num_shares, ownership_type
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8. Return ONLY valid JSON matching the schema. No markdown, no explanation."""
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9. Return ONLY valid JSON matching the schema. No markdown, no explanation."""
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USER_PROMPT = (
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"Extract all data from this Lebanese real estate property card. "
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"Return a single JSON object with these keys: "
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"request_number, request_date, applicant_name_raw, request_purpose, data_valid_until, "
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"registry_office, page_info, search_scope, owns_properties, declared_property_count, "
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"person (object with: first_name, father_name, mother_name, family_name, "
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"person (object with: first_name (or company/entity name), father_name, mother_name, family_name, "
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"family_origin, nationality, birth_date, registry_number, registry_place), "
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"properties (array of objects with: party_name, property_number, section, block, "
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"real_estate_district, qaza, num_shares, ownership_type), "
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@@ -47,6 +48,149 @@ USER_PROMPT = (
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"Use null for missing fields. Include every property row from the table (or empty array if none)."
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)
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CORRELATION_SYSTEM_PROMPT = """You are verifying whether two scanned Lebanese real estate document pages belong to the same multi-page request and the same person.
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Rules:
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1. Compare both page images and the extracted metadata together.
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2. Strong signals: matching request number, matching search scope, matching page numbering in the same sequence, matching applicant/person names, matching footer/header identifiers, and obvious continuation of the same document layout.
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3. Weak OCR differences are common in Arabic letters such as س and ن, ب and ت, or Arabic-Indic digits. Do not reject a match solely because of one likely OCR confusion.
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4. Reject when there are clear contradictions in request number, search scope, person identity, or unrelated page numbering.
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5. Return JSON only.
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"""
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def _get_ai_verification_provider(preferred_provider: str = "") -> str:
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"""Return a provider capable of vision reasoning for verification."""
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providers = [p["id"] for p in get_available_providers() if p["id"] in {"claude", "gemini"}]
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if preferred_provider in providers:
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return preferred_provider
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if DEFAULT_PROVIDER in providers:
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return DEFAULT_PROVIDER
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if providers:
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return providers[0]
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raise ValueError("No AI verification provider configured")
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def _build_correlation_user_prompt(current_context: dict, candidate_context: dict) -> str:
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return (
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"Determine whether PAGE_A and PAGE_B belong to the same multi-page request/document for the same person. "
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"Treat minor OCR mistakes as possible noise. Return one JSON object with keys: "
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"same_document (boolean), confidence ('high'|'medium'|'low'), verdict_ar (short Arabic sentence), "
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"reasons_ar (array of short Arabic bullet strings), mismatch_flags (array of short Arabic strings), "
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"recommended_action ('auto-link'|'manual-review').\n\n"
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f"PAGE_A_CONTEXT={json.dumps(current_context, ensure_ascii=False)}\n"
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f"PAGE_B_CONTEXT={json.dumps(candidate_context, ensure_ascii=False)}"
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)
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async def verify_page_correlation(
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current_image_path: str,
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candidate_image_path: str,
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current_context: dict,
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candidate_context: dict,
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provider: str = "",
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) -> dict:
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"""Ask a vision model if two pages belong to the same multi-page request."""
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provider = _get_ai_verification_provider(provider)
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if provider == "claude":
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import anthropic
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client = anthropic.AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
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current_full_path = _resolve_path(current_image_path)
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candidate_full_path = _resolve_path(candidate_image_path)
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current_img_data, current_media_type = _encode_image(current_full_path)
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candidate_img_data, candidate_media_type = _encode_image(candidate_full_path)
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message = await client.messages.create(
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model=CLAUDE_MODEL,
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max_tokens=1200,
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system=[{"type": "text", "text": CORRELATION_SYSTEM_PROMPT}],
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": _build_correlation_user_prompt(current_context, candidate_context)},
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": current_media_type,
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"data": current_img_data,
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},
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},
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": candidate_media_type,
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"data": candidate_img_data,
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},
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},
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],
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}
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],
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)
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raw_text = _strip_code_fences(message.content[0].text)
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result = json.loads(raw_text)
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elif provider == "gemini":
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import asyncio
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from google import genai
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from google.genai import types
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client = genai.Client(api_key=GEMINI_API_KEY)
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current_full_path = _resolve_path(current_image_path)
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candidate_full_path = _resolve_path(candidate_image_path)
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with open(current_full_path, "rb") as f:
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current_bytes = f.read()
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with open(candidate_full_path, "rb") as f:
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candidate_bytes = f.read()
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mime_map = {
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".jpg": "image/jpeg",
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".jpeg": "image/jpeg",
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".png": "image/png",
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".webp": "image/webp",
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}
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current_mime = mime_map.get(Path(current_full_path).suffix.lower(), "image/jpeg")
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candidate_mime = mime_map.get(Path(candidate_full_path).suffix.lower(), "image/jpeg")
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def _call():
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response = client.models.generate_content(
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model=GEMINI_MODEL,
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contents=[
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types.Content(
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parts=[
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types.Part(text=CORRELATION_SYSTEM_PROMPT + "\n\n" + _build_correlation_user_prompt(current_context, candidate_context)),
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types.Part(inline_data=types.Blob(mime_type=current_mime, data=current_bytes)),
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types.Part(inline_data=types.Blob(mime_type=candidate_mime, data=candidate_bytes)),
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]
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)
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],
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config=types.GenerateContentConfig(
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temperature=0.1,
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max_output_tokens=1600,
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),
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)
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return response.text
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raw_text = await asyncio.get_event_loop().run_in_executor(None, _call)
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raw_text = _strip_code_fences(raw_text)
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result = json.loads(raw_text)
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else:
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raise ValueError(f"Provider {provider} does not support AI correlation verification")
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return {
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"provider": provider,
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"same_document": bool(result.get("same_document")),
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"confidence": result.get("confidence") or "medium",
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"verdict_ar": result.get("verdict_ar") or "تعذر توليد خلاصة واضحة.",
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"reasons_ar": result.get("reasons_ar") or [],
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"mismatch_flags": result.get("mismatch_flags") or [],
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"recommended_action": result.get("recommended_action") or "manual-review",
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}
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def _resolve_path(image_path: str) -> str:
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if Path(image_path).is_absolute():
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return image_path
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