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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 {} |