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.huggingface.yaml

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+# .huggingface.yaml
+title: Demo Agent - Research Assistant
+sdk: gradio
+app_file: app.py
+tags:
+  - academic
+  - LLM
+  - planning
+  - agent
+license: cc0

README.md

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+🌀 Refreshing build...

agent.py

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+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 {}

app.py

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+# app.py
+import gradio as gr
+from agent import DemoAgent
+
+agent = DemoAgent(agent_name="demo_agent")
+
+def run_agent(input_text):
+    result = agent.run(input_text)
+    return result.get("result", "No result returned.")
+
+iface = gr.Interface(
+    fn=run_agent,
+    inputs=gr.Textbox(lines=5, label="Enter your research question"),
+    outputs=gr.Textbox(label="Agent Response"),
+    title="Demo Agent: Academic Research Assistant",
+    description="Find papers, summarize key findings, and generate research questions."
+)
+
+if __name__ == "__main__":
+    iface.launch()

config.json

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+{
+    "name": "demo_agent",
+    "description": [
+        "You are an academic research assistant. ",
+        "Help users find relevant research papers, summarize key findings, and generate potential research questions."
+    ],
+    "tools": [
+        "demo_author/arxiv"
+    ],
+    "meta": {
+        "author": "demo_author",
+        "version": "0.0.1",
+        "license": "CC0"
+    },
+    "build": {
+        "entry": "agent.py",
+        "module": "DemoAgent"
+    }
+}

meta_requirements.txt

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+arxiv