Update tools/analysis_tools.py

tools/analysis_tools.py

@@ -7,12 +7,18 @@ from src.models import load_all_models
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-@tool
-def trigger_interview_analysis(user_id: str, job_offer_id: str, conversation_history: List[Dict], job_description: str):
+class InterviewAnalysisArgs(BaseModel):
+    """Arguments for the trigger_interview_analysis tool."""
+    user_id: str = Field(..., description="The unique identifier for the user, provided in the system prompt.")
+    job_offer_id: str = Field(..., description="The unique identifier for the job offer, provided in the system prompt.")
+    job_description: str = Field(..., description="The full JSON string of the job offer description.")
+    conversation_history: List[Dict[str, Any]] = Field(..., description="The complete conversation history between the user and the agent.")
+
+@tool("trigger_interview_analysis", args_schema=InterviewAnalysisArgs)
+def trigger_interview_analysis(user_id: str, job_offer_id: str, job_description: str, conversation_history: List[Dict[str, Any]]):
     """
-    Appelle cet outil pour terminer l'entretien et lancer l'analyse finale.
-    IMPORTANT: Utilise EXCLUSIVEMENT les valeurs pour 'user_id' et 'job_offer_id' qui te sont fournies dans le contexte technique du prompt système. Ne les invente JAMAIS à partir de la conversation.
-    Tu DOIS aussi fournir l'historique complet de la conversation (conversation_history) et la description du poste (job_description).
+    Call this tool to end the interview and launch the final analysis.
+    You MUST provide all arguments: user_id, job_offer_id, job_description, and the complete conversation_history.
     """
     try:
         logger.info(f"Outil 'trigger_interview_analysis' appelé pour user_id: {user_id} et job_offer_id: {job_offer_id}")
@@ -28,7 +34,6 @@ def trigger_interview_analysis(user_id: str, job_offer_id: str, conversation_his
         feedback_path = f"/tmp/feedbacks/{user_id}.json"
         with open(feedback_path, "w", encoding="utf-8") as f:
             json.dump({"status": "completed", "feedback_data": feedback_data}, f, ensure_ascii=False, indent=4)
-
         logger.info(f"Analyse pour l'utilisateur {user_id} terminée et sauvegardée dans {feedback_path}.")
         return "L'analyse a été déclenchée et terminée avec succès."