feat: chat unifié, GestureCatalog, Copilot, Léa UI, extraction données, vérification replay
Refonte majeure du système Agent Chat et ajout de nombreux modules : - Chat unifié : suppression du dual Workflows/Agent Libre, tout passe par /api/chat avec résolution en 3 niveaux (workflow → geste → "montre-moi") - GestureCatalog : 38 raccourcis clavier universels Windows avec matching sémantique, substitution automatique dans les replays, et endpoint /api/gestures - Mode Copilot : exécution pas-à-pas des workflows avec validation humaine via WebSocket (approve/skip/abort) avant chaque action - Léa UI (agent_v0/lea_ui/) : interface PyQt5 pour Windows avec overlay transparent pour feedback visuel pendant le replay - Data Extraction (core/extraction/) : moteur d'extraction visuelle de données (OCR + VLM → SQLite), avec schémas YAML et export CSV/Excel - ReplayVerifier (agent_v0/server_v1/) : vérification post-action par comparaison de screenshots, avec logique de retry (max 3) - IntentParser durci : meilleur fallback regex, type GREETING, patterns améliorés - Dashboard : nouvelles pages gestures, streaming, extractions - Tests : 63 tests GestureCatalog, 47 tests extraction, corrections tests existants - Dépréciation : /api/agent/plan et /api/agent/execute retournent HTTP 410, suppression du code hardcodé _plan_to_replay_actions Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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core/extraction/schema.py
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core/extraction/schema.py
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"""
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Schema d'extraction de donnees - Definition des champs et navigation
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Permet de definir un schema YAML decrivant les champs a extraire
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depuis des captures d'ecran (DPI, formulaires, listes...).
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"""
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import re
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from dataclasses import dataclass, field
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import yaml
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@dataclass
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class ExtractionField:
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"""Definition d'un champ a extraire depuis un screenshot."""
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name: str # Ex: "nom_patient", "date_naissance"
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description: str # Description pour le VLM
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field_type: str = "text" # "text", "date", "number", "boolean"
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required: bool = True
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validation_regex: Optional[str] = None # Regex de validation optionnelle
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def validate_value(self, value: Optional[str]) -> bool:
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"""
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Valider une valeur extraite pour ce champ.
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Returns:
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True si la valeur est valide
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"""
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# Champ requis mais absent
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if self.required and (value is None or str(value).strip() == ""):
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return False
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# Pas de valeur et pas requis => OK
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if value is None or str(value).strip() == "":
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return True
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value_str = str(value).strip()
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# Validation par type
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if self.field_type == "number":
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try:
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float(value_str.replace(",", ".").replace(" ", ""))
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except ValueError:
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return False
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elif self.field_type == "boolean":
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if value_str.lower() not in (
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"true", "false", "oui", "non", "1", "0", "vrai", "faux"
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):
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return False
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elif self.field_type == "date":
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# Accepter les formats courants FR
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date_patterns = [
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r"\d{2}/\d{2}/\d{4}", # JJ/MM/AAAA
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r"\d{2}-\d{2}-\d{4}", # JJ-MM-AAAA
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r"\d{4}-\d{2}-\d{2}", # AAAA-MM-JJ (ISO)
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r"\d{2}\.\d{2}\.\d{4}", # JJ.MM.AAAA
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]
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if not any(re.fullmatch(p, value_str) for p in date_patterns):
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return False
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# Validation regex custom
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if self.validation_regex:
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if not re.fullmatch(self.validation_regex, value_str):
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return False
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return True
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@dataclass
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class ExtractionSchema:
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"""
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Schema complet d'extraction : liste de champs + regles de navigation.
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Peut etre charge/sauvegarde en YAML pour reutilisation.
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"""
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name: str # Ex: "dossier_patient_DPI"
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description: str
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fields: List[ExtractionField] = field(default_factory=list)
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navigation: Dict[str, Any] = field(default_factory=dict)
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# --- Serialisation YAML ---
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@classmethod
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def from_yaml(cls, path: str) -> "ExtractionSchema":
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"""
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Charger un schema depuis un fichier YAML.
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Args:
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path: Chemin vers le fichier YAML
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Returns:
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Instance ExtractionSchema
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"""
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yaml_path = Path(path)
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if not yaml_path.exists():
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raise FileNotFoundError(f"Schema YAML non trouve : {path}")
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with open(yaml_path, "r", encoding="utf-8") as f:
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data = yaml.safe_load(f)
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if not isinstance(data, dict):
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raise ValueError(f"Le fichier YAML doit contenir un dictionnaire, pas {type(data).__name__}")
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return cls._from_dict(data)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "ExtractionSchema":
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"""Construire un schema depuis un dictionnaire Python."""
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return cls._from_dict(data)
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@classmethod
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def _from_dict(cls, data: Dict[str, Any]) -> "ExtractionSchema":
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"""Construction interne depuis un dict."""
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fields_raw = data.get("fields", [])
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fields = []
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for fd in fields_raw:
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fields.append(ExtractionField(
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name=fd["name"],
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description=fd.get("description", ""),
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field_type=fd.get("type", fd.get("field_type", "text")),
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required=fd.get("required", True),
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validation_regex=fd.get("validation", fd.get("validation_regex")),
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))
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return cls(
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name=data.get("name", "unnamed"),
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description=data.get("description", ""),
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fields=fields,
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navigation=data.get("navigation", {}),
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)
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def to_yaml(self, path: str) -> None:
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"""
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Sauvegarder le schema en fichier YAML.
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Args:
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path: Chemin de sortie
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"""
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yaml_path = Path(path)
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yaml_path.parent.mkdir(parents=True, exist_ok=True)
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data = self.to_dict()
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with open(yaml_path, "w", encoding="utf-8") as f:
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yaml.dump(data, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
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def to_dict(self) -> Dict[str, Any]:
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"""Convertir en dictionnaire serialisable."""
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return {
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"name": self.name,
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"description": self.description,
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"fields": [
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{
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"name": f.name,
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"description": f.description,
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"type": f.field_type,
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"required": f.required,
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**({"validation": f.validation_regex} if f.validation_regex else {}),
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}
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for f in self.fields
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],
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"navigation": self.navigation,
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}
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# --- Utilitaires ---
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@property
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def required_fields(self) -> List[ExtractionField]:
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"""Retourne la liste des champs obligatoires."""
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return [f for f in self.fields if f.required]
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@property
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def field_names(self) -> List[str]:
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"""Retourne la liste des noms de champs."""
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return [f.name for f in self.fields]
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def get_field(self, name: str) -> Optional[ExtractionField]:
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"""Recuperer un champ par son nom."""
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for f in self.fields:
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if f.name == name:
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return f
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return None
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def validate_record(self, record: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Valider un enregistrement complet contre le schema.
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Returns:
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Dict avec 'valid' (bool), 'errors' (list), 'completeness' (float)
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"""
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errors = []
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valid_count = 0
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for fld in self.fields:
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value = record.get(fld.name)
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if fld.validate_value(value):
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if value is not None and str(value).strip():
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valid_count += 1
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else:
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errors.append(f"Champ '{fld.name}' invalide: {value!r}")
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total = len(self.fields) if self.fields else 1
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completeness = valid_count / total
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return {
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"valid": len(errors) == 0,
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"errors": errors,
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"completeness": completeness,
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}
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