mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-10-06 02:48:22 +00:00
49c73b15b3
- Update app.py to use async service initialization - Refactor multiple ops to use async_execute instead of execute - Add support for stream and use_async flags in config - Update LLM usage to use achat instead of chat - Add new LLM models and update existing ones in config - Improve error handling and logging in several ops - Update dependencies and Python version requirements
69 lines
3.1 KiB
Python
69 lines
3.1 KiB
Python
import json
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from typing import List
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from flowllm import C, BaseLLMOp
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from flowllm.enumeration.role import Role
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from flowllm.schema.message import Message as FlowMessage
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from loguru import logger
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from reme_ai.schema import Message, Trajectory
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from reme_ai.schema.memory import BaseMemory, TaskMemory
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from reme_ai.utils.op_utils import merge_messages_content
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@C.register_op()
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class SimpleSummaryOp(BaseLLMOp):
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file_path: str = __file__
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async def summary_trajectory(self, trajectory: Trajectory) -> List[BaseMemory]:
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execution_process = merge_messages_content(trajectory.messages)
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success_score_threshold: float = self.op_params.get("success_score_threshold", 0.9)
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logger.info(f"success_score_threshold={success_score_threshold}")
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execution_result = "success" if trajectory.score >= success_score_threshold else "fail"
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summary_prompt = self.prompt_format(prompt_name="summary_prompt",
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execution_process=execution_process,
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execution_result=execution_result,
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summary_example=self.get_prompt("summary_example"))
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def parse_content(message: Message):
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content = message.content
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memory_list = []
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try:
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if "```" in content:
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content = content.split("```")[1].strip()
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if content.startswith("json"):
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content = content.strip("json")
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for exp_dict in json.loads(content):
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when_to_use = exp_dict.get("when_to_use", "").strip()
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memory = exp_dict.get("memory", "").strip()
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if when_to_use and memory:
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memory_list.append(TaskMemory(workspace_id=self.context.get("workspace_id", ""),
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when_to_use=when_to_use,
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content=memory,
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author=getattr(self.llm, "model_name", "system")))
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return memory_list
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except Exception as e:
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logger.exception(f"parse content failed!\n{content}")
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raise e
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return await self.llm.achat(messages=[FlowMessage(role=Role.USER, content=summary_prompt)], callback_fn=parse_content)
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async def async_execute(self):
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trajectories: list = self.context.trajectories
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trajectories: List[Trajectory] = [Trajectory(**x) if isinstance(x, dict) else x for x in trajectories]
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memory_list: List[BaseMemory] = []
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for trajectory in trajectories:
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memories = await self.summary_trajectory(trajectory)
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if memories:
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memory_list.extend(memories)
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self.context.response.answer = json.dumps([x.model_dump() for x in memory_list])
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self.context.response.metadata["memory_list"] = memory_list
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for memory in memory_list:
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logger.info(f"add memory: when_to_use={memory.when_to_use}\ncontent={memory.content}")
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