fix(agent): v4.13.1 - actions contenu document (resume/traduire/ameliorer/meeting) sans outils, apercu + appliquer/rejeter, mock ignore contexte

app/services/llm_client.py

@@ -190,6 +190,11 @@ class LLMClient:
     async def _mock_complete(self, messages, model, tools) -> LLMResponse:
         user_content = self._last_user_content(messages)
         sys_content = self._system_content(messages)
+        # Only the user's objective drives the planner. The engine appends the
+        # workspace/document snapshot under "# Contexte"; that text must never
+        # trigger keyword heuristics (a doc mentioning "recherche"/"collection"
+        # used to misroute content requests into tool calls).
+        objective = user_content.split("\n# Contexte")[0]
 
         # Once tool results are already in the conversation, we have acted:
         # stop issuing new tool calls and conclude.
@@ -200,7 +205,7 @@ class LLMClient:
             )
 
         # Skill-driven: if the objective names a known skill, mirror its template.
-        skill_hint = self._extract_skill_hint(user_content)
+        skill_hint = self._extract_skill_hint(objective)
 
         if skill_hint == "sprint":
             return LLMResponse(
@@ -218,13 +223,13 @@ class LLMClient:
         # before the tool-intent heuristics and scoped to the user objective
         # only, so an injected "# Contexte" that happens to mention "collection"
         # can't misroute a writing request into a create-collection action.
-        draft = self._draft_reply(user_content.split("\n# Contexte")[0])
+        draft = self._draft_reply(objective)
         if draft:
             return LLMResponse(text=draft, model=model)
 
-        # Exact keyword → tool intent resolution.
+        # Exact keyword → tool intent resolution (objective only).
         for regex, builder in _CREATE_PATTERNS:
-            m = regex.search(user_content)
+            m = regex.search(objective)
             if m:
                 return LLMResponse(
                     tool_calls=[dict(name=name, arguments=self._bind_placeholders(args, sys_content)) for name, args in [builder(m)]],
@@ -233,7 +238,7 @@ class LLMClient:
                 )
 
         for regex, builder in _SEARCH_PATTERNS:
-            m = regex.search(user_content)
+            m = regex.search(objective)
             if m:
                 return LLMResponse(
                     tool_calls=[dict(name=name, arguments=args) for name, args in [builder(m)]],
@@ -242,9 +247,9 @@ class LLMClient:
                 )
 
         # Loose "collection <X>" detection → treat as a create intent.
-        low = user_content.lower()
+        low = objective.lower()
         if "collection" in low:
-            m = _COLLECTION_LINE.search(user_content)
+            m = _COLLECTION_LINE.search(objective)
             if m:
                 name = m.group(1).strip()
                 return LLMResponse(
@@ -256,7 +261,7 @@ class LLMClient:
 
         # Plain conversational objective → final answer (no tool).
         return LLMResponse(
-            text=self._summarize(user_content),
+            text=self._summarize(objective),
             model=model,
         )
 
@@ -335,6 +340,24 @@ class LLMClient:
                 "• Planifier le suivi et archiver ce compte-rendu.\n"
             )
 
+        # ── Traduction / analyse du document (hors-ligne) ──
+        if re.search(r"traduis|traduit|translate", low):
+            return (
+                "⚠️ **Traduction non disponible en mode hors-ligne** (aucun modèle d'IA "
+                "connecté).\n\n"
+                "Connectez un fournisseur dans **Paramètres → Agent & IA**, puis relancez "
+                "« Traduire cette page » : le document traduit apparaîtra ici, avec un aperçu "
+                "à approuver ou à rejeter avant application."
+            )
+        if re.search(r"r[ée]sum|am[ée]lior|sugg[èe]re des|propose des", low):
+            return (
+                "⚠️ **Cette action nécessite un modèle d'IA connecté** pour analyser le "
+                "document.\n\n"
+                "Configurez une clé API dans **Paramètres → Agent & IA**, puis relancez "
+                "l'action : l'agent générera la proposition ici, avec un aperçu à approuver "
+                "ou à rejeter avant application."
+            )
+
         # ── Page / document draft (contextual "Ask AI") ──
         if title:
             t = title[:80]