mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-23 00:43:18 +00:00
225 lines
No EOL
8 KiB
Python
225 lines
No EOL
8 KiB
Python
import os
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from typing import List
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from tqdm import tqdm
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os.environ["APPWORLD_ROOT"] = "."
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from dotenv import load_dotenv
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load_dotenv("../../../.env")
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import re
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import time
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import json
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import ray
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import requests
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from appworld import AppWorld, load_task_ids
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from jinja2 import Template
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from loguru import logger
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from openai import OpenAI
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from prompt import PROMPT_TEMPLATE, PROMPT_TEMPLATE_WITH_EXPERIENCE
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@ray.remote
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class AppworldReactAgent:
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"""A minimal ReAct Agent for AppWorld tasks."""
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def __init__(self,
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index: int,
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task_ids: List[str],
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experiment_name: str,
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model_name: str = "qwen3-8b",
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temperature: float = 0.9,
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max_interactions: int = 30,
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max_response_size: int = 2048,
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num_runs: int = 1,
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use_task_memory: bool = False,
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make_task_memory: bool = False,
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api_url: str = "http://0.0.0.0:8002/",
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workspace_id: str="appworld_v1"):
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self.index: int = index
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self.task_ids: List[str] = task_ids
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self.experiment_name: str = experiment_name
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self.model_name: str = model_name
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self.temperature: float = temperature
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self.max_interactions: int = max_interactions
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self.max_response_size: int = max_response_size
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self.num_runs: int = num_runs
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self.use_task_memory: bool = use_task_memory
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self.make_task_memory: bool = make_task_memory
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self.api_url = api_url
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self.workspace_id = workspace_id
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self.llm_client = OpenAI()
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def call_llm(self, messages: list) -> str:
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for i in range(100):
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try:
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response = self.llm_client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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temperature=self.temperature,
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extra_body={"enable_thinking": False},
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seed=0)
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return response.choices[0].message.content
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except Exception as e:
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logger.exception(f"encounter error with {e.args}")
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time.sleep(1 + i * 10)
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return "call llm error"
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def prompt_messages(self,world: AppWorld) -> list[dict]:
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if self.use_task_memory:
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task_memory = self.get_task_memory(world.task.instruction)
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logger.info(f"loaded task_memory: {task_memory}")
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dictionary = {"supervisor": world.task.supervisor, "instruction": world.task.instruction, "experience": task_memory}
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else:
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dictionary = {"supervisor": world.task.supervisor, "instruction": world.task.instruction ,"experience": ""}
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print(dictionary)
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prompt = Template(PROMPT_TEMPLATE_WITH_EXPERIENCE.lstrip()).render(dictionary)
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messages: list[dict] = []
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# last_start = 0
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# for match in re.finditer("(USER|ASSISTANT|SYSTEM):\n", prompt):
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# last_end = match.span()[0]
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# if len(messages) == 0:
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# if last_end != 0:
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# raise ValueError(
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# f"Start of the prompt has no assigned role: {prompt[:last_end]}"
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# )
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# else:
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# messages[-1]["content"] = prompt[last_start:last_end]
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# role_type = match.group(1).lower()
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# messages.append({"role": role_type, "content": None})
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# last_start = match.span()[1]
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# messages[-1]["content"] = prompt[last_start:]
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messages.append({"role":"user", "content":prompt})
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return messages
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@staticmethod
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def get_reward(world) -> float:
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tracker = world.evaluate()
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num_passes = len(tracker.passes)
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num_failures = len(tracker.failures)
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return num_passes / (num_passes + num_failures)
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def execute(self):
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result = []
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for task_index, task_id in enumerate(tqdm(self.task_ids, desc=f"ray_index={self.index}")):
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# Run each task num_runs times
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for run_id in range(self.num_runs):
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with AppWorld(task_id=task_id, experiment_name=f"{self.experiment_name}_run_{run_id}") as world:
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history = self.prompt_messages(world=world)
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before_score = self.get_reward(world)
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for i in range(self.max_interactions):
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code = self.call_llm(history)
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history.append({"role": "assistant", "content": code})
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output = world.execute(code)
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if len(output) > self.max_response_size:
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# logger.warning(f"output exceed max size={len(output)}")
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output = output[:self.max_response_size]
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history.append({"role": "user", "content": output})
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if world.task_completed():
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break
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after_score = self.get_reward(world)
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uplift_score = after_score - before_score
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t_result = {
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"task_id": world.task_id,
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"run_id": run_id, # Add run_id field
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"experiment_name": self.experiment_name,
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"task_completed": world.task_completed(),
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"before_score": before_score,
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"after_score": after_score,
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"uplift_score": uplift_score,
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"task_history": history,
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}
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result.append(t_result)
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if self.make_task_memory:
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memory_list = self.make_task_memory(result)
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logger.info(f"Created {len(memory_list) if memory_list else 0} task memories")
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return result
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def handle_api_response(self, response: requests.Response):
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"""Handle API response with proper error checking"""
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if response.status_code != 200:
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print(f"Error: {response.status_code}")
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print(response.text)
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return None
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return response.json()
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def get_task_memory(self, query: str):
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"""Retrieve relevant task memories based on a query"""
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response = requests.post(
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url=f"{self.api_url}retrieve_task_memory",
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json={
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"workspace_id": self.workspace_id,
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"query": query,
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}
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)
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result = self.handle_api_response(response)
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if not result:
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return ""
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# Extract and return the answer
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answer = result.get("answer", "")
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print(f"Retrieved task memory: {answer}")
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return answer
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def make_task_memory(self, result):
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"""Generate a summary of conversation messages and create task memories"""
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if not result:
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print("No results to summarize")
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return
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# Prepare trajectories from results
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trajectories = []
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for r in result:
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if "task_history" in r:
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trajectories.append({
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"messages": r["task_history"],
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"score": float(r.get("uplift_score", 0.0))
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})
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if not trajectories:
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print("No trajectories to summarize")
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return
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response = requests.post(
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url=f"{self.api_url}summary_task_memory",
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json={
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"workspace_id": self.workspace_id,
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"trajectories": trajectories
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}
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)
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result = self.handle_api_response(response)
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if not result:
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return
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# Extract memory list from response
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memory_list = result.get("metadata", {}).get("memory_list", [])
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print(f"Task memory list created: {len(memory_list)} memories")
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return memory_list
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def main():
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dataset_name = "train"
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task_ids = load_task_ids(dataset_name)
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agent = AppworldReactAgent(index=0, task_ids=task_ids[0:1], experiment_name=dataset_name, num_runs=4)
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result = agent.execute()
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logger.info(f"result={json.dumps(result)}")
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if __name__ == "__main__":
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main() |