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llama.py
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"""
Naive LLaMA client (with shell access) for AIOpsLab.
"""
import asyncio
from aiopslab.orchestrator import Orchestrator
from clients.utils.llm import LLaMAClient
from clients.utils.templates import DOCS
class Agent:
def __init__(self):
self.history = []
self.llm = LLaMAClient()
def init_context(self, problem_desc: str, instructions: str, apis: str):
"""Initialize the context for the agent."""
self.telemetry_apis = self._filter_dict(apis, lambda k, _: "get_logs" in k)
self.shell_api = self._filter_dict(apis, lambda k, _: "exec_shell" in k)
self.submit_api = self._filter_dict(apis, lambda k, _: "submit" in k)
stringify_apis = lambda apis: "\n\n".join(
[f"{k}\n{v}" for k, v in apis.items()]
)
self.system_message = DOCS.format(
prob_desc=problem_desc,
telemetry_apis=stringify_apis(self.telemetry_apis),
shell_api=stringify_apis(self.shell_api),
submit_api=stringify_apis(self.submit_api),
)
self.task_message = instructions
self.history.append({"role": "system", "content": self.system_message})
self.history.append({"role": "user", "content": self.task_message})
async def get_action(self, input) -> str:
"""Wrapper to interface the agent with OpsBench.
Args:
input (str): The input from the orchestrator/environment.
Returns:
str: The response from the agent.
"""
self.history.append({"role": "user", "content": input})
response = self.llm.run(self.history)
self.history.append({"role": "assistant", "content": response[0]})
return response[0]
def _filter_dict(self, dictionary, filter_func):
return {k: v for k, v in dictionary.items() if filter_func(k, v)}
if __name__ == "__main__":
agent = Agent()
orchestrator = Orchestrator()
orchestrator.register_agent(agent, name="llama-w-shell")
pid = "flower_model_misconfig-detection"
problem_desc, instructs, apis = orchestrator.init_problem(pid)
agent.init_context(problem_desc, instructs, apis)
asyncio.run(orchestrator.start_problem(max_steps=10))