Delete agent.py with huggingface_hub

agent.py

@@ -1,206 +0,0 @@
-from cerebrum.llm.apis import llm_chat, llm_call_tool, llm_chat_with_json_output
-from cerebrum.interface import AutoTool
-import os
-import json
-
-def get_config():
-    from cerebrum.config.config_manager import config
-    return config
-config = get_config()
-
-
-
-aios_kernel_url = config.get_kernel_url()
-
-class DemoAgent:
-    def __init__(self, agent_name):
-        self.agent_name = agent_name
-        self.config = self.load_config()
-        self.tools = [
-            tool.get_tool_call_format()
-            for tool in AutoTool.from_batch_preloaded(self.config["tools"])
-        ]
-
-        self.plan_max_fail_times = 3
-        self.tool_call_max_fail_times = 3
-
-        self.start_time = None
-        self.end_time = None
-        self.request_waiting_times: list = []
-        self.request_turnaround_times: list = []
-        self.messages = []
-        self.workflow_mode = "manual"  # (manual, automatic)
-        self.rounds = 0
-
-    def load_config(self):
-        script_path = os.path.abspath(__file__)
-        script_dir = os.path.dirname(script_path)
-        config_file = os.path.join(script_dir, "config.json")
-
-        with open(config_file, "r") as f:
-            config = json.load(f)
-        return config
-    
-    def pre_select_tools(self, tool_names):
-        pre_selected_tools = []
-        for tool_name in tool_names:
-            for tool in self.tools:
-                if tool["function"]["name"] == tool_name:
-                    pre_selected_tools.append(tool)
-                    break
-        return pre_selected_tools
-    
-    def build_system_instruction(self):
-        prefix = "".join(["".join(self.config["description"])])
-
-        plan_instruction = "".join(
-            [
-                f"You are given the available tools from the tool list: {json.dumps(self.tools)} to help you solve problems. ",
-                "Generate a plan with comprehensive yet minimal steps to fulfill the task. ",
-                "The plan must follow the json format as below: ",
-                "[",
-                '{"action_type": "action_type_value", "action": "action_value","tool_use": [tool_name1, tool_name2,...]}',
-                '{"action_type": "action_type_value", "action": "action_value", "tool_use": [tool_name1, tool_name2,...]}',
-                "...",
-                "]",
-                "In each step of the planned plan, identify tools to use and recognize no tool is necessary. ",
-                "Followings are some plan examples. ",
-                "[" "[",
-                '{"action_type": "tool_use", "action": "gather information from arxiv. ", "tool_use": ["arxiv"]},',
-                '{"action_type": "chat", "action": "write a summarization based on the gathered information. ", "tool_use": []}',
-                "];",
-                "[",
-                '{"action_type": "tool_use", "action": "gather information from arxiv. ", "tool_use": ["arxiv"]},',
-                '{"action_type": "chat", "action": "understand the current methods and propose ideas that can improve ", "tool_use": []}',
-                "]",
-                "]",
-            ]
-        )
-
-        if self.workflow_mode == "manual":
-            self.messages.append({"role": "system", "content": prefix})
-
-        else:
-            assert self.workflow_mode == "automatic"
-            self.messages.append({"role": "system", "content": prefix})
-            self.messages.append({"role": "user", "content": plan_instruction})
-
-    def automatic_workflow(self):
-        for i in range(self.plan_max_fail_times):
-            response = llm_chat_with_json_output(
-                messages=self.messages,
-                message_return_type="json"
-            )["response"]["response_message"]
-
-            try:
-                workflow = json.loads(response)
-            except:
-                workflow = None
-
-            self.rounds += 1
-
-            if workflow:
-                return workflow
-
-            else:
-                self.messages.append(
-                    {
-                        "role": "assistant",
-                        "content": f"Fail {i+1} times to generate a valid plan. I need to regenerate a plan",
-                    }
-                )
-        return None
-
-    def manual_workflow(self):
-        workflow = [
-            {
-                "action_type": "call_tool",
-                "action": "Search for relevant papers",
-                "tool_use": ["demo_author/arxiv"],
-            },
-            {
-                "action_type": "chat",
-                "action": "Provide responses based on the user's query",
-                "tool_use": [],
-            },
-        ]
-        return workflow
-
-    def run(self, task_input):
-        self.build_system_instruction()
-
-        self.messages.append({"role": "user", "content": task_input})
-
-        workflow = None
-
-        if self.workflow_mode == "automatic":
-            workflow = self.automatic_workflow()
-            self.messages = self.messages[:1]  # clear long context
-
-        else:
-            assert self.workflow_mode == "manual"
-            workflow = self.manual_workflow()
-
-        self.messages.append(
-            {
-                "role": "user",
-                "content": f"[Thinking]: The workflow generated for the problem is {json.dumps(workflow)}. Follow the workflow to solve the problem step by step. ",
-            }
-        )
-
-        try:
-            if workflow:
-                final_result = ""
-
-                for i, step in enumerate(workflow):
-                    action_type = step["action_type"]
-                    action = step["action"]
-                    tool_use = step["tool_use"]
-
-                    prompt = f"At step {i + 1}, you need to: {action}. "
-                    self.messages.append({"role": "user", "content": prompt})
-
-                    if tool_use:
-                        selected_tools = self.pre_select_tools(tool_use)
-
-                    else:
-                        selected_tools = None
-
-                    if action_type == "call_tool":
-                        response = llm_call_tool(
-                            agent_name=self.agent_name,
-                            messages=self.messages,
-                            tools=selected_tools,
-                            base_url=aios_kernel_url
-                        )["response"]
-                    else:
-                        response = llm_chat(
-                            agent_name=self.agent_name,
-                            messages=self.messages,
-                            base_url=aios_kernel_url
-                        )["response"]
-                        
-                    self.messages.append({"role": "assistant", "content": response["response_message"]})
-
-                    self.rounds += 1
-
-
-                final_result = self.messages[-1]["content"]
-                
-                return {
-                    "agent_name": self.agent_name,
-                    "result": final_result,
-                    "rounds": self.rounds,
-                }
-
-            else:   
-                return {
-                    "agent_name": self.agent_name,
-                    "result": "Failed to generate a valid workflow in the given times.",
-                    "rounds": self.rounds,
-
-                }
-                
-        except Exception as e:
-
-            return {}