mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-23 00:43:18 +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
126 lines
3.6 KiB
Python
126 lines
3.6 KiB
Python
import time
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import ray
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from dotenv import load_dotenv
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# from ray import logger
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from loguru import logger
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load_dotenv("../../.env")
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import json
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from pathlib import Path
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from bfcl_agent import BFCLAgent
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def run_agent(
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dataset_name: str,
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experiment_suffix: str,
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max_workers: int,
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num_trials: int = 1,
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model_name: str = "qwen3-8b",
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data_path: str = "data/multiturn_data_base_val.jsonl",
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answer_path: Path = Path("data/possible_answer"),
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use_memory: bool = False,
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use_memory_addition: bool = True,
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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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enable_thinking: bool = False,
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memory_base_url: str = "http://0.0.0.0:8002/",
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memory_workspace_id: str = "bfcl_v3",
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):
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experiment_name = dataset_name + "_" + experiment_suffix
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path: Path = Path(
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f"./exp_result/{model_name}/with_think" if enable_thinking else f"./exp_result/{model_name}/no_think",
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)
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path.mkdir(parents=True, exist_ok=True)
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with open(data_path, "r", encoding="utf-8") as f:
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task_ids = [json.loads(l)["id"] for l in f]
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result: list = []
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def dump_file():
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with open(path / f"{experiment_name}.jsonl", "a") as f:
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for x in result:
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f.write(json.dumps(x) + "\n")
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future_list: list = []
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for i in range(max_workers):
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actor = BFCLAgent.remote(
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index=i,
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task_ids=task_ids[i::max_workers],
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experiment_name=experiment_name,
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data_path=data_path,
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answer_path=answer_path,
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model_name=model_name,
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num_trials=num_trials,
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use_memory=use_memory,
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use_memory_addition=use_memory_addition,
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use_memory_deletion=use_memory_deletion,
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delete_freq=delete_freq,
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freq_threshold=freq_threshold,
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utility_threshold=utility_threshold,
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enable_thinking=enable_thinking,
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memory_base_url=memory_base_url,
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memory_workspace_id=memory_workspace_id,
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)
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future = actor.execute.remote()
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future_list.append(future)
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time.sleep(1)
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logger.info("submit complete")
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for i, future in enumerate(future_list):
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t_result = ray.get(future)
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if t_result:
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if isinstance(t_result, list):
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result.extend(t_result)
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else:
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result.append(t_result)
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logger.info(f"{i + 1}/{len(task_ids)} complete")
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dump_file()
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def main():
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max_workers = 4
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num_runs = 1
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num_trials = 2
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model_name = "qwen3-8b"
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use_memory = False
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use_memory_addition = False
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use_memory_deletion = False
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memory_base_url = "http://0.0.0.0:8002/"
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memory_workspace_id = "bfcl_v3"
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if max_workers > 1:
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ray.init(num_cpus=max_workers)
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for run_id in range(num_runs):
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run_agent(
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dataset_name="bfcl-multi-turn-base",
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experiment_suffix=f"wo-exp",
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model_name=model_name,
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max_workers=max_workers,
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num_trials=num_trials,
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data_path="data/multiturn_data_base_val.jsonl",
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answer_path=Path("data/possible_answer"),
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enable_thinking=False,
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use_memory=use_memory,
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use_memory_addition=use_memory_addition,
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use_memory_deletion=use_memory_deletion,
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delete_freq=5,
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freq_threshold=5,
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utility_threshold=0.5,
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memory_base_url=memory_base_url,
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memory_workspace_id=memory_workspace_id,
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)
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if __name__ == "__main__":
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main()
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