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tests/conftest.py: mutate the settings singleton (instead of rebinding) so DB + backup dir are isolated per test -> pytest-xdist safe. Real backup tests (snapshot/prune/admin API) and OAuth mock tests (Gitea/GitHub/link) replace the previous skips. init_db() now also creates webhook_subscriptions (full schema without the FastAPI lifespan). ruff check is clean; .eslintrc.json migrated to eslint.config.mjs (flat config). CI: lint job (ruff + eslint), parallel tests (-n auto), run on every branch push. VERSION 5.11.1.
457 lines
24 KiB
Python
457 lines
24 KiB
Python
"""FlowDeck — AgentEngine: ReAct orchestrator (v4.14.0).
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`objective → comprehension → context → reasoning ↔ action → result`.
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The engine drives the LLM (which only emits tool intentions), gates each call
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through PermissionManager, executes it via ToolRegistry, journals every action
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to `agent_actions` with an undo snapshot, and yields a stream of SSE events so
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the UI can render reasoning + actions live. Since v4.14.0 a freshly created
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conversation is automatically renamed with a descriptive title derived from its
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content so the history stays easy to browse.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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from app.config import settings
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from app.db import get_conn
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from app.services.context_builder import ContextBuilder
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from app.services.llm_client import LLMClient
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from app.services.permission_manager import PermissionManager
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from app.services.tool_registry import ToolRegistry
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logger = logging.getLogger(__name__)
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MAX_ITERATIONS = 12
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# Compact in-app guide so the LLM can answer « comment faire… ? » questions even
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# when no document is attached to the conversation (generic help / onboarding).
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APP_GUIDE = """## Guide de l'utilisateur FlowDeck (sert à répondre aux questions « comment … ? »)
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- **Pages** : le contenu est organisé en blocs (paragraphes, titres, listes, to-do, tableaux, images, formules, bases embarquées). La barre latérale liste les pages récentes, favoris, agents, partagées et publiées.
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- **Documents & espaces de travail** : un « document » est une page éditeur (type Notion) qui vit dans un espace de travail. Pour créer un document dans un espace : appelle `read_workspaces` (reprends le `workspace_name` ou l'id exact), puis `create_document`. Pour modifier un document existant : `read_document` puis `write_blocks` (blocs et/ou titre). Pour supprimer : `delete_document` (corbeille). `search_workspace` retrouve aussi les documents et les espaces par titre.
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- **Format des blocs** (pour `write_blocks`) : chaque bloc est un objet `{"type": "...", "content": "texte"}`. Le champ du texte s'appelle **`content`** (jamais `text`). Un script / code s'écrit dans un bloc `{"type": "code", "content": "...", "language": "powershell"}`. Les titres sont `heading_1`, `heading_2`, `heading_3`, `heading_4`. Autres types : paragraph, bulleted_list, numbered_list, to_do, quote, divider, toggle, callout.
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- **Collections (bases de données)** : des ensembles de pages structurées avec des propriétés (texte, nombre, sélection, dates…). Chaque collection peut avoir plusieurs vues : tableau, board (kanban), calendrier, galerie, liste, timeline, graphique, formulaire, carte, flux, gantt. Ajouter une propriété ou une vue = outils add_property / create_view.
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- **Créer du contenu** : « crée une collection X », « crée une page », « ajoute une propriété Statut à la collection Y » sont des actions que l'agent peut exécuter directement avec ses outils.
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- **Espaces de travail** : FlowDeck gère des espaces locaux et des dépôts Gitea/GitHub (pages privées dans un dépôt, issues reliées via read_gitea_issues). On change d'espace depuis le menu en bas à gauche (« Switch workspace »).
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- **Recherche** : la commande Ctrl+K / la barre de recherche du haut permet de retrouver pages et collections.
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- **Corbeille & Bibliothèque** : les pages supprimées vont dans la Corbeille ; Favoris / Récents / Partagés / Publiés se consultent dans la Bibliothèque.
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- **Réglages** : Paramètres (en bas à gauche → Settings) pour le compte, les notifications, les tags, les intégrations et la section « Agent & IA » (clés API, fournisseurs, modèle global).
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- **Agent IA** : ouvrable via le bouton 🤖 en bas à droite ou la section « Agents » du sidebar. On peut lui parler de la page ouverte, ou lui poser des questions générales sur l'utilisation de l'application.
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Quand la question est générale (« comment créer un kanban ? », « où sont mes favoris ? »), réponds de façon concise et guidée à partir de ces informations, sans inventer de fonctionnalités absentes."""
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# Deterministic auto-title heuristics (used when no real LLM is configured, and
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# as a fallback when the generated title is unusable). Ordered by priority: the
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# first matching intent wins.
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_TITLE_INTENTS = (
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("Création", ("créer", "crée", "crées", "création", "nouveau", "nouvelle",
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"create", "creation")),
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("Ajout", ("ajouter", "ajoute", "ajout d", "ajoutons", "add")),
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("Renommage", ("renommer", "renomme", "renommage", "rename")),
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("Suppression", ("supprimer", "supprime", "suppression", "delete")),
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("Déplacement", ("déplacer", "déplace", "déplacement", "move")),
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("Mise à jour", ("modifier", "modifie", "modification", "mets à jour",
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"met à jour", "mettre à jour", "update", "éditer")),
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("Analyse", ("analyser", "analyse")),
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("Résumé", ("résumer", "résume", "résumé", "resume")),
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("Traduction", ("traduire", "traduis", "traduit", "traduction", "translate")),
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("Planification", ("planifier", "planifie", "préparer", "prépare", "organiser",
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"organise", "sprint", "agenda")),
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("Recherche", ("chercher", "cherche", "rechercher", "recherche", "trouver",
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"trouve", "liste", "lister", "search", "find")),
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)
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_TITLE_TYPES = (
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("collection", "collection", ("collection", "base de données", "database", "db")),
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("propriété", "propriété", ("propriété", "property")),
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("vue", "vue", (" vue", "view")),
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("board", "board", ("board", "kanban")),
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("sprint", "sprint", ("sprint",)),
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("document", "document", ("document", "note de réunion", "compte-rendu", "compte rendu")),
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("tâche", "tâche", ("tâche", "task", "tache")),
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("issue", "issue", ("issue",)),
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)
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class AgentEngine:
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def __init__(self, user_id: int, workspace_id: int | None = None,
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llm: LLMClient | None = None):
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self.user_id = user_id
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self.workspace_id = workspace_id
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self.llm = llm or LLMClient()
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self.tools = ToolRegistry()
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self.ctx = ContextBuilder(user_id, workspace_id)
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self.perms = PermissionManager(user_id)
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self._tokens = 0
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# ── Helpers ──
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def _load_agent(self, conversation_id: int) -> dict:
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with get_conn() as conn:
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row = conn.execute(
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"SELECT a.* FROM agents a JOIN agent_conversations c ON c.agent_id=a.id WHERE c.id=?",
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(conversation_id,),
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).fetchone()
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if not row:
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row = {"id": None, "name": "FlowDeck Agent", "icon": "🤖", "agent_type": "personal",
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"system_instructions": "", "model": settings.llm_model,
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"scope_json": "{}", "approval_mode": "auto"}
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return dict(row)
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def _build_system_prompt(self, agent: dict, skills=None) -> str:
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if skills is None:
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skills = []
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if isinstance(skills, dict):
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skills = [skills]
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lines = [
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"Tu es FlowDeck Agent, un agent IA qui réalise des tâches dans le workspace FlowDeck.",
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"Tu réfléchis (reasoning) puis agis en appelant les outils disponibles.",
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"Appelle UN ou PLUSIEURS outils pour atteindre l'objectif, puis conclus avec une réponse finale.",
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"N'invente jamais d'IDs : utilise ceux fournis dans le contexte.",
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f"Workspace courant : {self.workspace_id}.",
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]
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if agent.get("system_instructions"):
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lines.append(f"\nInstructions de l'agent {agent.get('name','')}:\n{agent['system_instructions']}")
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# Plusieurs skills peuvent être appliqués au même post : chacun injecte
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# son prompt dans les instructions système.
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for skill in skills:
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if skill:
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lines.append(f"\nSkill appliquée « {skill.get('name','')} »:\n{skill.get('prompt_template','')}")
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# L'utilisateur peut poser des questions d'aide sans contexte de document ;
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# le guide intégré permet d'y répondre (aucun outil requis).
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lines.append("\n" + APP_GUIDE)
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return "\n".join(lines)
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# ── Main run (async generator of SSE events) ──
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async def run(self, conversation_id: int, objective: str, *, model: str | None = None,
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mentions: list[str] | None = None, files: list[dict] | None = None,
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skill_id: int | None = None, skill_ids: list[int] | None = None,
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extra_context: str | None = None):
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agent = self._load_agent(conversation_id)
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scope = json.loads(agent.get("scope_json") or "{}")
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approval_mode = agent.get("approval_mode") or "auto"
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model = model or agent.get("model") or settings.llm_model
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ids = list(skill_ids or [])
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if skill_id and skill_id not in ids:
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ids.append(skill_id)
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skills = [s for s in (self._load_skill(i, scope) for i in ids) if s]
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system = self._build_system_prompt(agent, skills)
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context = self.ctx.build(mentions=mentions, files=files)
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if extra_context and extra_context.strip():
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context += "\n\n## Document / contexte fourni par l'utilisateur\n" + extra_context.strip()
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messages = [
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{"role": "system", "content": system},
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{"role": "user", "content": f"{objective}\n\n# Contexte\n{context}"},
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]
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self._persist_message(conversation_id, "user", objective)
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self._update_conversation(conversation_id, status="running")
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# Update the history title right away (before the run finishes) and
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# refine it once we have the final answer (_autotitle below).
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try:
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suggested = self._suggest_title(objective, None)
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if suggested:
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self._update_conversation(conversation_id, title=suggested[:80])
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except Exception: # noqa: BLE001 — never break a run because of the title
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logger.exception("Auto-title failed for conversation #%s", conversation_id)
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tool_schema = self.tools.schema(scope)
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final_text = None
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used_model = model or "" # peut être ajusté par un repli de modèle (404/410)
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try:
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for _step in range(settings.agent_max_iterations or MAX_ITERATIONS):
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if self._tokens >= settings.agent_max_tokens_budget:
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yield self._event("error", {"message": "Budget de tokens dépassé"})
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break
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response = await asyncio.wait_for(
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self.llm.complete(messages, model=model, tools=tool_schema, stream=True),
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timeout=settings.agent_run_timeout_seconds,
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)
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self._tokens += response.usage.get("total_tokens", 0) or 0
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if getattr(response, "notice", ""):
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yield self._event("notice", {"message": response.notice})
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used_model = getattr(response, "model", "") or used_model
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if response.text and response.text.strip():
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yield self._event("reasoning", {"content": response.text})
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if not response.tool_calls:
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messages.append({"role": "assistant", "content": response.text or ""})
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final_text = response.text or self._no_tool_message(response)
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yield self._event("final", {"content": final_text})
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break
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# L'API de chat exige que le message assistant qui *annonce* les appels
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# d'outils porte les `tool_calls` (avec id), puis que chaque résultat
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# d'outil soit fourni avec le `tool_call_id` correspondant. Sans cela
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# la passe suivante est refusée par le fournisseur (et l'agent retombait
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# silencieusement sur le mock hors-ligne).
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tool_specs = []
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for idx, call in enumerate(response.tool_calls):
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call_id = call.get("id") or f"call_{conversation_id}_{idx}_{self._tokens}"
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tool_specs.append({
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"id": call_id,
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"type": "function",
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"function": {
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"name": call["name"],
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"arguments": call.get("arguments_raw")
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or json.dumps(call.get("arguments") or {}, ensure_ascii=False),
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},
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})
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assistant_msg = {"role": "assistant", "content": response.text or ""}
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assistant_msg["tool_calls"] = tool_specs
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messages.append(assistant_msg)
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for idx, call in enumerate(response.tool_calls):
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tool, args = call["name"], call.get("arguments") or {}
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call_id = tool_specs[idx]["id"]
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denied = False
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try:
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self.perms.assert_can(tool, args, self.workspace_id, approval_mode)
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except Exception as exc: # permission / approval guard
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detail = self._exc_detail(exc)
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yield self._event("action", {"tool": tool, "status": "error", "detail": detail})
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self._log_action(conversation_id, tool, args, {}, "error", detail=detail)
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messages.append({
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"role": "tool", "tool_call_id": call_id,
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"content": json.dumps({"status": "error", "message": f"Permission refusée: {detail}"}, ensure_ascii=False),
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})
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denied = True
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if not denied:
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result = await self.tools.execute(tool, args, user_id=self.user_id)
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if result.status == "success":
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yield self._event("action", {
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"tool": tool, "status": result.status,
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"target_type": result.target_type, "target_id": result.target_id,
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"message": result.message,
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})
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self._log_action(conversation_id, tool, args, result.data, "success",
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target_type=result.target_type, target_id=result.target_id,
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undo=result.undo)
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messages.append({
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"role": "tool", "tool_call_id": call_id,
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"content": json.dumps({"status": "ok", "result": result.data, "target_id": result.target_id}, ensure_ascii=False),
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})
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else:
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yield self._event("action", {"tool": tool, "status": "error", "detail": result.message})
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self._log_action(conversation_id, tool, args, {}, "error", detail=result.message)
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messages.append({
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"role": "tool", "tool_call_id": call_id,
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"content": json.dumps({"status": "error", "message": result.message}, ensure_ascii=False),
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})
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if final_text is None:
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final_text = "Objectif traité. Consultez le journal des actions pour le détail."
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yield self._event("final", {"content": final_text})
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self._persist_message(conversation_id, "assistant", final_text,
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model=used_model, tokens=self._tokens)
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await self._autotitle(conversation_id, objective, final_text)
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except Exception as exc: # noqa: BLE001
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logger.exception("AgentEngine run failed")
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yield self._event("error", {"message": f"Erreur interne: {exc}"})
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finally:
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self._update_conversation(conversation_id, status="idle")
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# ── Skills ──
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def _load_skill(self, skill_id: int, scope: dict | None) -> dict | None:
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with get_conn() as conn:
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row = conn.execute("SELECT * FROM agent_skills WHERE id=?", (skill_id,)).fetchone()
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if not row:
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return None
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skill = dict(row)
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allowed = json.loads(skill.get("allowed_tools_json") or "[]")
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if allowed:
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skill["prompt_template"] = (skill.get("prompt_template") or "") + \
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"\nOutils autorisés: " + ", ".join(allowed)
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return skill
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# ── Audit & persistence ──
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def _log_action(self, conversation_id, tool, args, result, status,
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*, target_type="", target_id=None, undo=None, detail=""):
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with get_conn() as conn:
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conn.execute(
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"""INSERT INTO agent_actions
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(conversation_id, tool_name, target_type, target_id, payload_json,
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result_json, status, undo_snapshot_json, executed_by)
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VALUES (?,?,?,?,?,?,?,?,?)""",
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(conversation_id, tool, target_type,
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str(target_id) if target_id is not None else None,
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json.dumps(args, ensure_ascii=False),
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json.dumps(result, ensure_ascii=False, default=str),
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status,
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json.dumps(undo or {}, ensure_ascii=False),
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self.user_id),
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)
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conn.commit()
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def _persist_message(self, conversation_id, role, content, *, model="", tokens=0):
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with get_conn() as conn:
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conn.execute(
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"INSERT INTO agent_messages (conversation_id, role, content, model, tokens_used) VALUES (?,?,?,?,?)",
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(conversation_id, role, content, model, tokens),
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)
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conn.commit()
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def _update_conversation(self, conversation_id, *, status=None, title=None):
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with get_conn() as conn:
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sets, params = ["updated_at=CURRENT_TIMESTAMP"], []
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if status:
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sets.append("status=?")
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params.append(status)
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if title:
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sets.append("title=?")
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params.append(title)
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params.append(conversation_id)
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conn.execute(f"UPDATE agent_conversations SET {', '.join(sets)} WHERE id=?", params)
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conn.commit()
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# ── Misc ──
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@staticmethod
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def _event(etype: str, data: dict) -> dict:
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return {"type": etype, **data}
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@staticmethod
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def _no_tool_message(response) -> str:
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return "Je n'ai pas d'action à proposer pour cet objectif. Posez-moi une question plus précise ou demandez-moi de créer un élément."
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@staticmethod
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def _exc_detail(exc: Exception) -> str:
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detail = getattr(exc, "detail", None)
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return detail if isinstance(detail, str) else str(exc)
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# ── Auto-title (v4.14.0) ──
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async def _autotitle(self, conversation_id: int, objective: str, final_text: str | None):
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"""Rename the conversation with a descriptive title derived from the
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*latest* user request. Runs after every AI call so the history list
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always reflects the current topic and stays easy to browse."""
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try:
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suggested = self._suggest_title(objective, final_text)
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if suggested:
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self._update_conversation(conversation_id, title=suggested[:80])
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except Exception: # noqa: BLE001 — never break a run because of the title
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logger.exception("Auto-title failed for conversation #%s", conversation_id)
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@classmethod
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def _suggest_title(cls, objective: str | None, final_text: str | None) -> str:
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"""Produce a short descriptive title from the user objective (offline-safe)."""
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text = (objective or "").split("\n# Contexte", 1)[0].strip() or (final_text or "").strip()
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if not text:
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return "Conversation"
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# Strip the composer prefixes ("Contexte « X »", "Skill « Y »") that the
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# frontend prepends before the real user text.
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text = re.sub(
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r"(?:Contexte\s*«[^»]*»|Skill\s*«[^»]*»|Skill\s+«[^»]*»)(?:\s*[,;\n.])+\s*",
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"", text,
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).strip()
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if not text:
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return "Conversation"
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low = text.lower()
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intent = None
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for label, words in _TITLE_INTENTS:
|
|
if any(w in low for w in words):
|
|
intent = label
|
|
break
|
|
|
|
type_label = next(
|
|
(t for t, _noun, words in _TITLE_TYPES if any(w in low for w in words)), None
|
|
)
|
|
has_workspace = any(w in low for w in ("workspace", "espace de travail"))
|
|
quotes = [q.strip() for q in re.findall(r'[«"]([^«»"]{1,80})[»"]', text) if q.strip()]
|
|
subject = quotes[0] if quotes else None
|
|
ws = quotes[-1] if (has_workspace and len(quotes) > 1) else None
|
|
|
|
def _clean(s: str) -> str:
|
|
return re.sub(r"\s+", " ", s).strip(" .;:-")
|
|
|
|
if not subject and type_label:
|
|
noun = next((n for t, n, _w in _TITLE_TYPES if t == type_label), type_label)
|
|
m = re.search(
|
|
rf"\b{noun}\b\s*(?:nomm[ée]e?\s+|appel[ée]e?\s+|intitul[ée]e?\s+)?"
|
|
r'[«"]?\s*([A-Za-zÀ-ÿ0-9][A-Za-zÀ-ÿ0-9_ \-]{1,60}?)\s*[»"]?',
|
|
text, re.IGNORECASE,
|
|
)
|
|
if m:
|
|
subject = m.group(1).strip()
|
|
|
|
if intent and subject:
|
|
core = f"{intent} {type_label or 'élément'} « {subject} »" \
|
|
if type_label else f"{intent} « {subject} »"
|
|
if ws:
|
|
core += f" (dans {ws})"
|
|
return _clean(core)
|
|
|
|
generic = _clean(text)
|
|
return generic[:70] if generic else "Conversation"
|
|
|
|
|
|
def undo_action(action_id: int) -> bool:
|
|
"""Reverse a single agent action using its stored undo snapshot.
|
|
|
|
Returns True on success. Marks the action row `reverted`.
|
|
"""
|
|
with get_conn() as conn:
|
|
action = conn.execute("SELECT * FROM agent_actions WHERE id=?", (action_id,)).fetchone()
|
|
if not action:
|
|
raise ValueError(f"Action #{action_id} introuvable")
|
|
undo = json.loads(action["undo_snapshot_json"] or "{}")
|
|
op, table, rid = undo.get("action"), undo.get("table"), undo.get("id")
|
|
if not op or not table or rid is None:
|
|
raise ValueError(f"Action #{action_id} n'a pas de snapshot annulable")
|
|
|
|
if op == "delete":
|
|
conn.execute(f"DELETE FROM {table} WHERE id=?", (rid,))
|
|
elif op == "softdelete":
|
|
conn.execute(f"UPDATE {table} SET deleted_at=NULL WHERE id=?", (rid,))
|
|
elif op == "insert":
|
|
snapshot = undo.get("snapshot")
|
|
if not snapshot:
|
|
raise ValueError("Snapshot manquant pour insert")
|
|
cols = ", ".join(snapshot.keys())
|
|
ph = ", ".join("?" for _ in snapshot)
|
|
conn.execute(f"INSERT INTO {table} ({cols}) VALUES ({ph})", list(snapshot.values()))
|
|
elif op == "update":
|
|
snapshot = undo.get("snapshot")
|
|
if table == "collection_pages":
|
|
conn.execute(
|
|
"UPDATE collection_pages SET property_values_json=?, title=?, updated_at=CURRENT_TIMESTAMP WHERE id=?",
|
|
(json.dumps(snapshot, ensure_ascii=False), undo.get("title", ""), rid),
|
|
)
|
|
elif table == "pages":
|
|
if isinstance(snapshot, dict):
|
|
conn.execute(
|
|
"UPDATE pages SET content=?, content_format=?, title=?, updated_at=CURRENT_TIMESTAMP WHERE id=?",
|
|
(snapshot.get("content", ""),
|
|
snapshot.get("content_format", "markdown"),
|
|
snapshot.get("title", ""), rid),
|
|
)
|
|
else:
|
|
conn.execute("UPDATE pages SET content=?, updated_at=CURRENT_TIMESTAMP WHERE id=?",
|
|
(snapshot, rid))
|
|
else:
|
|
raise ValueError(f"Table non gérée pour rollback: {table}")
|
|
else:
|
|
raise ValueError(f"Opération de rollback inconnue: {op}")
|
|
|
|
conn.execute("UPDATE agent_actions SET status='reverted' WHERE id=?", (action_id,))
|
|
conn.commit()
|
|
return True
|