Etape 1 OK, sans optionnel

backend/app/graph/nodes.py

@@ -1,18 +1,34 @@
+#nodes.py
 from app.agents.pm_agent import run_pm_agent
 from app.agents.dev_agent import run_dev_agent
 from app.agents.qa_agent import run_qa_agent
 from app.services.retrieval_service import find_existing_project
 from app.graph.state import WorkflowState
 
-async def pm_node(state: WorkflowState):
-    prompt = state["user_input"]
-    if state.get("user_feedback"):
-        prompt += f"\nRetour utilisateur pour correction : {state['user_feedback']}"
+async def pm_node(state: WorkflowState):    
+    history = state.get("chat_history", []) or []
+    
+    if state.get("status") == "spec_incomplete" and state.get("user_feedback"):
+        current_input = state["user_feedback"]
+        full_user_input = f"{state['user_input']}\n{current_input}"
+    else:
+        current_input = state["user_input"]
+        full_user_input = current_input
+    
+    spec = await run_pm_agent(user_input=current_input, history=history)
+    
+    updated_history = list(history)
+    updated_history.append({"role": "user", "content": current_input})
+    
+    if not spec.is_complete and spec.clarifying_question:
+        updated_history.append({"role": "assistant", "content": spec.clarifying_question})
 
-    spec = await run_pm_agent(prompt)
     return {
         "spec": spec.model_dump(),
-        "status": "spec_ready",
+        "status": "spec_ready" if spec.is_complete else "spec_incomplete", 
+        "chat_history": updated_history,
+        "user_input": full_user_input, 
+        "user_feedback": None,
         "loop_count": 0,
     }
 
@@ -53,4 +69,45 @@ async def human_review_node(state: WorkflowState):
         "existing_project_approved": True, 
         "is_completed": True,
         "status": "approved_by_human"
-    }
+    }
+
+# def inspect_specifications_gaps(spec_dict: dict) -> list[str]:
+#     missing_fields = []
+    
+#     # 1. Vérifications globales (clés alignées sur ProjectSpec)
+#     if not spec_dict.get("title"): missing_fields.append("title")
+#     if not spec_dict.get("description"): missing_fields.append("description")
+#     if not spec_dict.get("requirements"): missing_fields.append("requirements")
+        
+#     # 2. Entrées/Sorties
+#     io = spec_dict.get("io_config", {})
+#     if io.get("has_inputs") is True and not io.get("input_type"):
+#         missing_fields.append("io_config.input_type")
+#     if io.get("has_outputs") is True and not io.get("output_type"):
+#         missing_fields.append("io_config.output_type")
+            
+#     # 3. Authentification
+#     auth = spec_dict.get("auth_config", {})
+#     if auth.get("requires_auth") is True and not auth.get("auth_method"):
+#         missing_fields.append("auth_config.auth_method")
+                
+#     return missing_fields
+
+# def generate_clarifying_questions_prompt(missing_fields: list[str]) -> str:
+#     # Le mapping utilise désormais les EXACTES mêmes clés
+#     mapping_instructions = {
+#         "title": "- Préciser un titre pour le projet.",
+#         "description": "- Expliquer le but global du script.",
+#         "requirements": "- Lister les fonctionnalités attendues étape par étape.",
+#         "io_config.input_type": "- Préciser le type et le format des fichiers/données d'entrée (CSV, dossier, etc.).",
+#         "io_config.output_type": "- Préciser le type et le format attendus en sortie (Excel, PDF, log, etc.).",
+#         "auth_config.auth_method": "- Clarifier la méthode d'accès ou d'authentification exigée pour l'outil tiers."
+#     }
+    
+#     bullet_points = "\n".join([mapping_instructions[field] for field in missing_fields if field in mapping_instructions])
+    
+#     return f"""
+#     ATTENTION : Les données suivantes sont obligatoires mais absentes.
+#     Vous devez formuler une question naturelle et polie pour demander à l'utilisateur de préciser :
+#     {bullet_points}
+#     """

backend/app/graph/state.py

@@ -1,4 +1,4 @@
-from typing import TypedDict, Optional, Any, Dict
+from typing import TypedDict, Optional, Any, Dict, List
 
 class WorkflowState(TypedDict, total=False):
     user_input: str
@@ -10,4 +10,5 @@ class WorkflowState(TypedDict, total=False):
     loop_count: int                            # Compteur pour la Loop 1 (Dev <-> QA)
     user_feedback: Optional[str]               # Retours si l'utilisateur refuse le code final
     is_completed: bool                         # Statut de livraison finale
-    status: str
+    status: str
+    chat_history: List[Dict[str, str]]

backend/app/graph/workflow.py

@@ -12,7 +12,17 @@ from app.graph.nodes import (
     human_review_node,
 )
 
+def route_entry_point(state: WorkflowState):
+    if state.get("status") == "spec_approved":
+        return "retrieval"
+    return "pm"
+
 # --- Fonctions de Routage (Conditional Edges) ---
+def route_after_pm(state: WorkflowState):
+    current_status = state.get("status")
+    if current_status in ["spec_incomplete", "spec_ready"]:
+        return END
+    return "retrieval"
 
 def route_after_retrieval(state: WorkflowState):
     # Si un projet existe, on demande d'abord à l'humain (via le nœud de review)
@@ -55,8 +65,22 @@ graph.add_node("dev", dev_node)
 graph.add_node("qa", qa_node)
 graph.add_node("human_review", human_review_node)
 
-graph.set_entry_point("pm")
-graph.add_edge("pm", "retrieval")
+graph.set_conditional_entry_point(
+    route_entry_point,
+    {
+        "pm": "pm",
+        "retrieval": "retrieval",
+    },
+)
+
+graph.add_conditional_edges(
+    "pm",
+    route_after_pm,
+    {
+        END: END,
+        "retrieval": "retrieval",
+    },
+)
 
 # Étape 1 : Choix après recherche vectorielle
 graph.add_conditional_edges(