mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-16 23:41:10 +00:00
d0c9d89092
* feat(memory): add ContextChecker component for context size management * refactor(memory): restructure file-based memory tools and update imports * docs(readme): update documentation with detailed architecture and components * docs(readme): update Chinese documentation with enhanced memory management diagrams * refactor(cookbook): move cookbook files to test directory and clean up docs * docs(readme): update link path for old version documentation * docs(readme): update documentation with improved architecture diagrams and component details * docs(readme): update documentation with improved clarity and structure * refactor(docs): update in-memory memory documentation * docs(readme): add experiment reproduction link to quickstart guide
350 lines
13 KiB
Python
350 lines
13 KiB
Python
# flake8: noqa: E402, E501
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import os
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from typing import List, Any
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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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import datetime
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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 NEW_PROMPT_TEMPLATE
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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__(
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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 = 129024,
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num_trials: int = 1,
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use_memory: bool = False,
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use_memory_addition: bool = False,
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use_memory_deletion: bool = False,
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delete_freq: int = 10,
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freq_threshold: int = 5,
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utility_threshold: float = 0.5,
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memory_base_url: str = "http://0.0.0.0:8002/",
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memory_workspace_id: str = "appworld_v1",
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):
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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_trials: int = num_trials
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self.use_memory: bool = use_memory
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self.use_memory_addition: bool = use_memory_addition if use_memory else False
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self.use_memory_deletion: bool = use_memory_deletion if use_memory else False
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self.delete_freq: int = delete_freq
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self.freq_threshold: int = freq_threshold
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self.utility_threshold: float = utility_threshold
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self.memory_base_url: str = memory_base_url
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self.memory_workspace_id: str = memory_workspace_id
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self.llm_client = OpenAI()
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self.history: List[List[List[dict]]] = [[] for _ in range(num_trials)]
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self.retrieved_memory_list: List[List[List[Any]]] = [[] for _ in range(num_trials)]
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for run_id in range(num_trials):
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for _ in range(len(task_ids)):
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self.retrieved_memory_list[run_id].append([])
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self.history[run_id].append([])
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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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)
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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, run_id, task_index, previous_memories: None, world: AppWorld):
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app_descriptions = json.dumps(
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[{"name": k, "description": v} for (k, v) in world.task.app_descriptions.items()],
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indent=1,
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)
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dictionary = {"supervisor": world.task.supervisor, "app_descriptions": app_descriptions}
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sys_prompt = Template(NEW_PROMPT_TEMPLATE.lstrip()).render(dictionary)
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query = world.task.instruction
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if self.use_memory:
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if len(previous_memories) == 0:
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response = self.get_memory(world.task.instruction)
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if response and "memory_list" in response["metadata"]:
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self.retrieved_memory_list[run_id][task_index] = response["metadata"]["memory_list"]
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task_memory = response["answer"]
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logger.info(f"loaded task_memory: {task_memory}")
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query = (
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"Task:\n"
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+ query
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+ "\n\nSome Related Experience to help you to complete the task:\n"
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+ re.sub(r"(?i)\bMemory\s*(\d+)\s*[:]", r"Experience \1:", task_memory)
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)
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else:
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formatted_memories = []
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for i, memory in enumerate(previous_memories, 1):
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condition = memory["when_to_use"]
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memory_content = memory["content"]
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memory_text = f"Experience {i}:\n When to use: {condition}\n Content: {memory_content}\n"
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formatted_memories.append(memory_text)
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query = (
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"Task:\n"
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+ query
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+ "\n\nSome Related Experience to help you to complete the task:\n"
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+ "\n".join(formatted_memories)
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)
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messages = [
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{"role": "system", "content": sys_prompt},
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{"role": "user", "content": query},
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]
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self.history[run_id][task_index] = 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 extract_code_and_fix_content(
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self,
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text: str,
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ignore_multiple_calls=True,
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) -> tuple[str, str]:
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full_code_regex = r"```python\n(.*?)```"
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partial_code_regex = r".*```python\n(.*)"
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original_text = text
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output_code = ""
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match_end = 0
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# Handle multiple calls
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for re_match in re.finditer(full_code_regex, original_text, flags=re.DOTALL):
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code = re_match.group(1).strip()
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if ignore_multiple_calls:
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text = original_text[: re_match.end()]
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return code, text
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output_code += code + "\n"
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match_end = re_match.end()
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# check for partial code match at end (no terminating ```) following the last match
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partial_match = re.match(
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partial_code_regex,
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original_text[match_end:],
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flags=re.DOTALL,
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)
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if partial_match:
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output_code += partial_match.group(1).strip()
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# terminated due to stop condition. Add stop condition to output.
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if not text.endswith("\n"):
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text = text + "\n"
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text = text + "```"
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if len(output_code) == 0:
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return text, text
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else:
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return output_code, text
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def execute(self):
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result = []
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counter = 0
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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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t_result = None
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previous_memories = []
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# Run each task num_trials times
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for run_id in range(self.num_trials):
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start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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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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before_score = self.get_reward(world)
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for i in range(self.max_interactions):
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if i == 0:
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self.prompt_messages(
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run_id=run_id,
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task_index=task_index,
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previous_memories=previous_memories,
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world=world,
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)
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code_msg = self.call_llm(self.history[run_id][task_index])
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code, text = self.extract_code_and_fix_content(code_msg)
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self.history[run_id][task_index].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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self.history[run_id][task_index].append(
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{"role": "user", "content": "Output:\n```\n" + output + "```\n\n"},
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)
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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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if self.use_memory:
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if self.use_memory_addition:
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new_traj_list = [
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self.get_traj_from_task_history(task_id, self.history[run_id][task_index], after_score),
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]
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previous_memories = self.add_memory(new_traj_list)
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if after_score != 1:
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self.delete_memory_by_ids([mem["memory_id"] for mem in previous_memories])
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# update the freq & utility attributes of retrieved memories
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update_utility: bool = after_score == 1
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self.update_memory_information(self.retrieved_memory_list[run_id][task_index], update_utility)
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counter += 1
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if self.use_memory_deletion: # and counter % self.delete_freq == 0:
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self.delete_memory()
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t_result = {
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"task_id": world.task_id,
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"run_id": run_id,
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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": self.history[run_id][task_index],
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"task_start_time": start_time,
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}
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if after_score == 1:
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break
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result.append(t_result)
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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_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.memory_base_url}retrieve_task_memory",
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json={
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"workspace_id": self.memory_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 None
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logger.info(f"query: {query}, response: {result}")
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return result
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def get_traj_from_task_history(self, task_id: str, task_history: list, reward: float):
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pattern = r"\n\nSome Related Experience to help you to complete the task:.*"
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task_history[1]["content"] = re.sub(pattern, "", task_history[1]["content"], flags=re.DOTALL)
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return {
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"task_id": task_id,
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"messages": task_history,
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"score": reward,
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}
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def add_memory(self, trajectories):
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"""Generate a summary of conversation messages and create task memories"""
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response = requests.post(
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url=f"{self.memory_base_url}summary_task_memory",
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json={
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"workspace_id": self.memory_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 delete_memory_by_ids(self, memory_ids):
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response = requests.post(
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url=f"{self.memory_base_url}vector_store",
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json={
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"workspace_id": self.memory_workspace_id,
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"action": "delete_ids",
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"memory_ids": memory_ids,
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},
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)
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response.raise_for_status()
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def update_memory_information(self, memory_list, update_utility: bool = False):
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response = requests.post(
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url=f"{self.memory_base_url}record_task_memory",
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json={
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"workspace_id": self.memory_workspace_id,
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"memory_dicts": memory_list,
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"update_utility": update_utility,
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},
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)
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response.raise_for_status()
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logger.info(response.json())
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def delete_memory(self):
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response = requests.post(
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url=f"{self.memory_base_url}delete_task_memory",
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json={
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"workspace_id": self.memory_workspace_id,
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"freq_threshold": self.freq_threshold,
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"utility_threshold": self.utility_threshold,
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},
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)
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response.raise_for_status()
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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_trials=1)
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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()
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