Nodes V1

AgentReact/utils/nodes.py

@@ -14,21 +14,18 @@ llm = ChatMistralAI( # LLM sans outils
 )
 
 # NODES
-def reponse_question(state: MessagesState):
-    """Noeud qui réponds à la question, en s'aidant si besoin des outils à disposition"""
+def call_to_LLM(state: MessagesState):
+    """Noeud qui s'occupe de gérer les appels au LLM"""
     # Initialisation du LLM
     model = llm.bind_tools(getTools())
     
     # Appel du LLM 
     return {"messages": [model.invoke(state["messages"])]}
 
-tool_node = ToolNode(tools=getTools()) # Node gérant les outils
-
-# fonction de routage : Après reponse_question, si le LLM veut appeler un outil, on va au tool_node, sinon on termine
+# fonction de routage : Après reponse_question, si le LLM veut appeler un outil, on va au tool_node
 def should_continue(state: MessagesState):
     """
-    Use in the conditional_edge to route to the ToolNode if the last message
-    has tool calls. Otherwise, route to the end.
+    Vérifier s'il y a un appel aux outils dans le dernier message
     """
     if isinstance(state, list):
         ai_message = state[-1]
@@ -39,4 +36,46 @@ def should_continue(state: MessagesState):
 
     if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0:
         return "tools"
-    return END
+    return "no_tools"
+
+def task_ended(state: MessagesState):
+    """
+    Vérifier si l'agent a terminé son cycle, ou s'il faut le relancer
+    """
+    if isinstance(state, list):
+        ai_message = state[-1]
+    elif messages := state.get("messages", []):
+        ai_message = messages[-1]
+    else:
+        raise ValueError(f"No messages found in input state to tool_edge: {state}")
+
+    if "terminé" in ai_message.content.lower():
+        return END
+    return "continue"
+
+
+class BasicToolNode: # De mon ancien projet, https://github.com/LJ5O/Assistant/blob/main/modules/Brain/src/LLM/graph/nodes/BasicToolNode.py
+    """A node that runs the tools requested in the last AIMessage."""
+
+    def __init__(self, tools: list) -> None:
+        self.tools_by_name = {tool.name: tool for tool in tools}
+
+    def __call__(self, inputs: dict):
+        if messages := inputs.get("messages", []):
+            message = messages[-1]
+        else:
+            raise ValueError("No message found in input")
+        outputs = []
+        for tool_call in message.tool_calls:
+            #print(tool_call["args"])
+            tool_result = self.tools_by_name[tool_call["name"]].invoke(
+                tool_call["args"]
+            )
+            outputs.append(
+                ToolMessage(
+                    content=json.dumps(tool_result),
+                    name=tool_call["name"],
+                    tool_call_id=tool_call["id"],
+                )
+            )
+        return {"messages": outputs}