feat(extraction): handler extract_dossier + pont worker→DB VWB mutualisé (brique 3)
vwb_db.py : couplage worker→DB VWB lazy (app Flask sur instance/workflows.db) mutualisé (R1 + extraction), + persist_extracted_dossier (grille → Job/Table/Field). replay_engine.py : handler _handle_extract_dossier_action — lit le screenshot, extrait une grille structurée, gate qualité conservatrice (complete|needs_review), persiste avec preuve (screenshot_ref/bbox/confidence). N'échoue JAMAIS le replay. Données patient EN CLAIR (canal extraction, non anonymisé). Réserve : dispatch runtime (api_stream.py) non encore branché — étape suivante, à coordonner. Brique 3/4 de la verticale extraction dossier patient. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Dom
@@ -40,6 +40,7 @@ _ALLOWED_ACTION_TYPES = {
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"pause_for_human", # Pause supervisée explicite (interceptée par /replay/next)
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"extract_text", # OCR serveur sur dernier heartbeat → variable workflow
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"extract_table", # OCR serveur + filtre regex → liste structurée (boucle)
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"extract_dossier", # OCR grille structurée → dossier patient persisté (brique 3)
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"extract_text_scroll", # Marker côté graphe — expansé en sous-actions par _edge_to_normalized_actions
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"_concat_text_vars", # Action serveur interne (générée par expansion extract_text_scroll)
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"t2a_decision", # Analyse LLM facturation T2A → variable workflow
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@@ -53,6 +54,7 @@ _ALLOWED_ACTION_TYPES = {
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_SERVER_SIDE_ACTION_TYPES = {
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"extract_text",
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"extract_table",
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"extract_dossier",
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"t2a_decision",
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"llm_generate",
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"_concat_text_vars",
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@@ -2216,6 +2218,146 @@ def _handle_extract_table_action(
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return bool(rows)
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def _resolve_screenshot_path(replay_state: Dict[str, Any]) -> Optional[str]:
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"""Résout le chemin du dernier screenshot (path disque ou base64 → temp).
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Calque la source utilisée par extract_text/extract_table : priorité au
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``last_screenshot`` (path ou data-URI base64). Retourne None si absent.
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"""
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raw_screenshot = replay_state.get("last_screenshot") or ""
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if not raw_screenshot:
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return None
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if raw_screenshot.startswith("data:"):
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try:
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import base64 as _b64, tempfile
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header, b64data = raw_screenshot.split(",", 1)
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suffix = ".jpg" if "jpeg" in header else ".png"
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tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
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tmp.write(_b64.b64decode(b64data))
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tmp.close()
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return tmp.name
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except Exception as e:
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logger.warning("extract_dossier: décodage base64 screenshot échoué: %s", e)
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return None
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if os.path.isfile(raw_screenshot):
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return raw_screenshot
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return None
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def _gate_dossier_quality(
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grid: List[List[Dict[str, Any]]],
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*,
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min_confidence: float,
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expected_cols: Optional[int],
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) -> str:
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"""Gate qualité simple → 'complete' ou 'needs_review'.
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'complete' SSI : grille non vide ET confiance médiane ≥ seuil ET (si
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expected_cols fourni) au moins une ligne avec ce nombre de colonnes.
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Sinon 'needs_review'. Volontairement conservatrice (default-review).
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"""
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confs = [
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cell.get("confidence")
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for row in grid for cell in row
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if isinstance(cell.get("confidence"), (int, float))
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]
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if not confs:
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return "needs_review"
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confs.sort()
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median = confs[len(confs) // 2]
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if median < min_confidence:
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return "needs_review"
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if expected_cols is not None:
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if not any(len(row) == expected_cols for row in grid):
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return "needs_review"
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return "complete"
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def _handle_extract_dossier_action(
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action: Dict[str, Any],
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replay_state: Dict[str, Any],
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session_id: str,
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) -> bool:
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"""Traite une action extract_dossier côté serveur (brique 3).
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Lit le dernier screenshot, extrait une grille structurée via
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``extract_grid_from_image``, applique une gate qualité, puis PERSISTE un
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« dossier patient extrait » (Job/Table/Field) dans la DB VWB avec preuve
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(screenshot_ref + screen_bbox + confidences). Le job_id est stocké dans
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``replay_state["variables"][output_var]``.
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Paramètres reconnus (action.parameters) :
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output_var : nom de variable runtime (default "extracted_dossier")
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patient_ref : référence patient EN CLAIR (volontaire) — non tokenisée
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region : (x, y, w, h) px pour cropper avant OCR (None = plein)
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min_confidence : seuil de confiance médiane pour 'complete' (default 0.6)
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expected_cols : nb de colonnes attendu (optionnel) pour la gate
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N'ÉCHOUE JAMAIS le replay : toute erreur → log + needs_review.
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Retourne True SSI le dossier est persisté avec statut 'complete'.
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"""
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params = action.get("parameters") or {}
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output_var = (params.get("output_var") or params.get("variable_name") or "extracted_dossier").strip()
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patient_ref = params.get("patient_ref")
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region = params.get("region") or None
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try:
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min_confidence = float(params.get("min_confidence", 0.6))
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except (TypeError, ValueError):
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min_confidence = 0.6
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expected_cols = params.get("expected_cols")
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if isinstance(expected_cols, str):
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try:
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expected_cols = int(expected_cols)
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except ValueError:
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expected_cols = None
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job_id = ""
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status = "needs_review"
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try:
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path = _resolve_screenshot_path(replay_state)
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grid: List[List[Dict[str, Any]]] = []
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if path:
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from core.llm import extract_grid_from_image
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grid = extract_grid_from_image(
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path, region=tuple(region) if region else None
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)
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else:
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logger.warning(
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"extract_dossier : pas de screenshot pour session %s — needs_review",
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session_id,
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)
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status = _gate_dossier_quality(
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grid, min_confidence=min_confidence, expected_cols=expected_cols
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)
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from . import vwb_db
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with vwb_db.vwb_app_context():
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job_id = vwb_db.persist_extracted_dossier(
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grid,
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patient_ref=patient_ref,
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source_session_id=session_id,
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screenshot_ref=path,
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screen_bbox=({"x": region[0], "y": region[1], "width": region[2], "height": region[3]}
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if region and len(region) == 4 else None),
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status=status,
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)
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except Exception as e:
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# Ne JAMAIS échouer le replay : on log, on marque needs_review.
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logger.warning(
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"extract_dossier : échec persistance (%s) — needs_review, replay %s",
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e, replay_state.get("replay_id", "?"),
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)
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status = "needs_review"
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replay_state.setdefault("variables", {})[output_var] = job_id
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logger.info(
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"extract_dossier → variable '%s' job=%s statut=%s replay %s",
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output_var, job_id or "?", status, replay_state.get("replay_id", "?"),
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)
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return status == "complete"
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def _handle_t2a_decision_action(
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action: Dict[str, Any],
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replay_state: Dict[str, Any],
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106
agent_v0/server_v1/vwb_db.py
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106
agent_v0/server_v1/vwb_db.py
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@@ -0,0 +1,106 @@
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"""Couplage worker → DB VWB (mutualisé) + persistance « dossier patient extrait ».
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Le worker/serveur streaming est un process distinct du backend VWB : il n'a
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pas d'app Flask en mémoire. Ce module fournit :
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- ``vwb_app_context()`` : un app-context Flask lazy (singleton module) lié au
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fichier SQLite VWB ``visual_workflow_builder/backend/instance/workflows.db``,
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avec ``db.init_app`` (db de ``db.models``). Réutilisable par tout module
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serveur qui doit écrire dans la DB VWB (R1, extraction métier, …).
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- ``persist_extracted_dossier(...)`` : depuis une grille OCR
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(``List[List[cell]]``), crée ExtractionJob → ExtractedTable → ExtractedField
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et commit. Suppose un app-context actif (comme le pont R1 existant).
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⚠️ CANAL EXTRACTION = données patient EN CLAIR (volontaire) : aucune
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tokenisation/assainissement PII ici (cf. note dans db/models.py).
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"""
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import sys
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import uuid
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from contextlib import contextmanager
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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# Ajout du backend VWB au sys.path à l'import → rend ``db.models`` importable
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# (couplage worker→DB VWB mutualisé ; identique au pattern stream_processor).
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_VWB_BACKEND = Path(__file__).resolve().parents[2] / "visual_workflow_builder" / "backend"
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if str(_VWB_BACKEND) not in sys.path:
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sys.path.insert(0, str(_VWB_BACKEND))
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# App Flask lazy (singleton module) — un seul db.init_app pour tout le process.
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_vwb_app = None
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@contextmanager
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def vwb_app_context():
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"""App-context Flask VWB (lazy singleton) sur instance/workflows.db.
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À utiliser via ``with vwb_app_context(): ...`` autour des appels qui
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nécessitent ``db.session`` (ex. persist_extracted_dossier).
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"""
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global _vwb_app
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if _vwb_app is None:
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from flask import Flask
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from db.models import db
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db_path = _VWB_BACKEND / "instance" / "workflows.db"
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app = Flask("worker_vwb")
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app.config["SQLALCHEMY_DATABASE_URI"] = f"sqlite:///{db_path}"
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app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
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db.init_app(app)
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_vwb_app = app
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with _vwb_app.app_context():
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yield
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def persist_extracted_dossier(
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grid: List[List[Dict[str, Any]]],
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*,
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patient_ref: Optional[str],
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source_session_id: Optional[str],
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screenshot_ref: Optional[str],
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screen_bbox: Optional[Dict[str, Any]],
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status: str,
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) -> str:
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"""Persiste un « dossier patient extrait » et retourne le job_id.
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Crée 1 ExtractionJob → 1 ExtractedTable → N ExtractedField (une par
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cellule de la grille), puis commit. Suppose un app-context VWB actif
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(fourni par ``vwb_app_context()`` ou par l'appelant, comme le pont R1).
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⚠️ ``patient_ref`` et ``cell["text"]`` sont stockés EN CLAIR (volontaire) :
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le but est de constituer le dossier, pas d'anonymiser.
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"""
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from db.models import db, ExtractionJob, ExtractedTable, ExtractedField
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job = ExtractionJob(
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id=uuid.uuid4().hex,
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patient_ref=patient_ref,
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source_session_id=source_session_id,
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status=status,
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)
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db.session.add(job)
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table = ExtractedTable(
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id=uuid.uuid4().hex,
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job_id=job.id,
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screen_bbox=screen_bbox,
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screenshot_ref=screenshot_ref,
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)
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db.session.add(table)
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for row in grid or []:
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for cell in row or []:
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db.session.add(ExtractedField(
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id=uuid.uuid4().hex,
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table_id=table.id,
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row=cell.get("row"),
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col=cell.get("col"),
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value=cell.get("text"),
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bbox=cell.get("bbox"),
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confidence=cell.get("confidence"),
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))
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db.session.commit()
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return job.id
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