103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
"""
|
|
Pipeline d'extraction d'entités
|
|
"""
|
|
|
|
import os
|
|
import logging
|
|
import re
|
|
from typing import Dict, Any, List
|
|
from services.worker.utils.llm_client import WorkerLLMClient
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
def run(doc_id: str, ctx: Dict[str, Any]) -> None:
|
|
"""Pipeline d'extraction d'entités"""
|
|
logger.info(f"🔍 Extraction d'entités pour le document {doc_id}")
|
|
|
|
try:
|
|
ocr_text = ctx.get("ocr_text", "")
|
|
document_type = ctx.get("document_type", "autre")
|
|
|
|
# Extraction basique
|
|
entities = _extract_basic_entities(ocr_text, document_type)
|
|
|
|
# Extraction avancée via LLM (merge non destructif)
|
|
llm = WorkerLLMClient()
|
|
prompt = _build_extraction_prompt(ocr_text[:3000] if ocr_text else "", document_type)
|
|
llm_response = llm.generate(prompt)
|
|
llm_json = WorkerLLMClient.extract_first_json(llm_response) or {}
|
|
entities = _merge_entities_basic_with_llm(entities, llm_json)
|
|
|
|
ctx.update({
|
|
"extracted_entities": entities,
|
|
"entities_count": len(entities)
|
|
})
|
|
logger.info(f"✅ Extraction terminée pour {doc_id}: {len(entities)} entités")
|
|
except Exception as e:
|
|
logger.error(f"❌ Erreur extraction {doc_id}: {e}")
|
|
ctx["extraction_error"] = str(e)
|
|
|
|
def _extract_basic_entities(text: str, doc_type: str) -> List[Dict[str, Any]]:
|
|
"""Extraction basique d'entités"""
|
|
entities = []
|
|
|
|
# Emails
|
|
emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)
|
|
for email in emails:
|
|
entities.append({
|
|
"type": "contact",
|
|
"subtype": "email",
|
|
"value": email,
|
|
"confidence": 0.95
|
|
})
|
|
|
|
# Téléphones
|
|
phones = re.findall(r'\b0[1-9](?:[.\-\s]?\d{2}){4}\b', text)
|
|
for phone in phones:
|
|
entities.append({
|
|
"type": "contact",
|
|
"subtype": "phone",
|
|
"value": phone,
|
|
"confidence": 0.9
|
|
})
|
|
|
|
# Dates
|
|
dates = re.findall(r'\b\d{1,2}[\/\-\.]\d{1,2}[\/\-\.]\d{4}\b', text)
|
|
for date in dates:
|
|
entities.append({
|
|
"type": "date",
|
|
"subtype": "generic",
|
|
"value": date,
|
|
"confidence": 0.8
|
|
})
|
|
|
|
return entities
|
|
|
|
|
|
def _build_extraction_prompt(text: str, doc_type: str) -> str:
|
|
return f"""
|
|
Tu es un extracteur d'entités pour documents notariaux.
|
|
Type de document: {doc_type}
|
|
Extrait en JSON strict les objets: identites, adresses, biens, entreprises, montants, dates.
|
|
Réponds UNIQUEMENT par un JSON.
|
|
|
|
TEXTE:
|
|
{text}
|
|
"""
|
|
|
|
|
|
def _merge_entities_basic_with_llm(basic: List[Dict[str, Any]], advanced: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
merged = list(basic)
|
|
if not isinstance(advanced, dict):
|
|
return merged
|
|
# Aplatit les entités LLM en liste simple type/value pour compatibilité minimale
|
|
for key in ["identites", "adresses", "biens", "entreprises", "montants", "dates"]:
|
|
items = advanced.get(key, []) or []
|
|
for item in items:
|
|
try:
|
|
value = item.get("adresse_complete") or item.get("date") or item.get("montant") or item.get("nom") or item.get("description") or str(item)
|
|
if value:
|
|
merged.append({"type": key, "value": value, "confidence": item.get("confidence", 0.8)})
|
|
except Exception:
|
|
continue
|
|
return merged |