refactor: adjust ToolCallingAgent to use new IO and ChatHistory, added helper functions

src/backend/base/langflow/components/agents/ToolCallingAgent.py

@@ -2,63 +2,99 @@ from typing import List, Optional
 
 from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate
-
-from langflow.base.agents.agent import LCAgentComponent
+from langchain.agents import AgentExecutor
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+from langflow.schema.message import Message
+from langflow.custom import Component
+from langflow.io import HandleInput, TextInput, BoolInput, Output
 from langflow.field_typing import LanguageModel, Text, Tool
 from langflow.schema import Data
 
 
-class ToolCallingAgentComponent(LCAgentComponent):
+class ToolCallingAgentComponent(Component):
     display_name: str = "Tool Calling Agent"
     description: str = "Agent that uses tools. Only models that are compatible with function calling are supported."
+    icon = "Agent"
 
-    def build_config(self):
-        return {
-            "llm": {"display_name": "LLM"},
-            "tools": {"display_name": "Tools"},
-            "user_prompt": {
-                "display_name": "Prompt",
-                "multiline": True,
-                "info": "This prompt must contain 'input' key.",
-            },
-            "handle_parsing_errors": {
-                "display_name": "Handle Parsing Errors",
-                "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
-                "advanced": True,
-            },
-            "memory": {
-                "display_name": "Memory",
-                "info": "Memory to use for the agent.",
-            },
-            "input_value": {
-                "display_name": "Inputs",
-                "info": "Input text to pass to the agent.",
-            },
-        }
+    inputs = [
+        HandleInput(
+            name="llm",
+            display_name="LLM",
+            input_types=["LanguageModel"],
+        ),
+        HandleInput(
+            name="tools",
+            display_name="Tools",
+            input_types=["Tool"],
+            is_list=True,
+        ),
+        TextInput(
+            name="user_prompt",
+            display_name="Prompt",
+            info="This prompt must contain 'input' key.",
+            value="{input}",
+        ),
+        BoolInput(
+            name="handle_parsing_errors",
+            display_name="Handle Parsing Errors",
+            info="If True, the agent will handle parsing errors. If False, the agent will raise an error.",
+            advanced=True,
+            value=True,
+        ),
+        HandleInput(
+            name="memory",
+            display_name="Memory",
+            input_types=["Data"],
+            info="Memory to use for the agent.",
+        ),
+        TextInput(
+            name="input_value",
+            display_name="Inputs",
+            info="Input text to pass to the agent.",
+        ),
+    ]
 
-    async def build(
-        self,
-        input_value: str,
-        llm: LanguageModel,
-        tools: List[Tool],
-        user_prompt: str = "{input}",
-        message_history: Optional[List[Data]] = None,
-        system_message: str = "You are a helpful assistant",
-        handle_parsing_errors: bool = True,
-    ) -> Text:
-        if "input" not in user_prompt:
+    outputs = [
+        Output(display_name="Text", name="text_output", method="run_agent"),
+    ]
+
+    async def run_agent(self) -> Message:
+        if "input" not in self.user_prompt:
             raise ValueError("Prompt must contain 'input' key.")
         messages = [
-            ("system", system_message),
+            ("system", "You are a helpful assistant"),
             (
                 "placeholder",
                 "{chat_history}",
             ),
-            ("human", user_prompt),
+            ("human", self.user_prompt),
             ("placeholder", "{agent_scratchpad}"),
         ]
         prompt = ChatPromptTemplate.from_messages(messages)
-        agent = create_tool_calling_agent(llm, tools, prompt)
-        result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors)
+        agent = create_tool_calling_agent(self.llm, self.tools, prompt)
+
+        runnable = AgentExecutor.from_agent_and_tools(
+            agent=agent,
+            tools=self.tools,
+            verbose=True,
+            handle_parsing_errors=self.handle_parsing_errors,
+        )
+        input_dict: dict[str, str | list[BaseMessage]] = {"input": self.input_value}
+        if hasattr(self, "memory") and self.memory:
+            input_dict["chat_history"] = self.convert_chat_history(self.memory)
+        result = await runnable.ainvoke(input_dict)
         self.status = result
-        return result
+
+        if "output" not in result:
+            raise ValueError("Output key not found in result. Tried 'output'.")
+
+        result_string = result["output"]
+
+        return Message(text=result_string)
+
+    def convert_chat_history(self, chat_history: List[Data]) -> List[Dict[str, str]]:
+        messages = []
+        for item in chat_history:
+            role = "user" if item.sender == "User" else "assistant"
+            messages.append({"role": role, "content": item.text})
+        return messages