mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-10-05 02:41:43 +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
47 lines
No EOL
1.7 KiB
Python
47 lines
No EOL
1.7 KiB
Python
from typing import List, Dict
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from flowllm import C, BaseOp
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from loguru import logger
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from reme_ai.schema import Trajectory
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@C.register_op()
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class TrajectoryPreprocessOp(BaseOp):
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file_path: str = __file__
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async def async_execute(self):
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"""Preprocess trajectories: validate and classify"""
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trajectories: list = self.context.get("trajectories", [])
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trajectories: List[Trajectory] = [Trajectory(**x) if isinstance(x, dict) else x for x in trajectories]
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# Classify trajectories
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classified = self._classify_trajectories(trajectories)
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logger.info(f"Classified trajectories - Success: {len(classified['success'])}, "
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f"Failure: {len(classified['failure'])}, All: {len(classified['all'])}")
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# Set context for downstream operators
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self.context.success_trajectories = classified['success']
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self.context.failure_trajectories = classified['failure']
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self.context.all_trajectories = classified['all']
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def _classify_trajectories(self, trajectories: List[Trajectory]) -> Dict[str, List[Trajectory]]:
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"""Classify trajectories based on score threshold"""
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success_trajectories = []
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failure_trajectories = []
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success_threshold = self.op_params.get("success_threshold", 1.0)
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for traj in trajectories:
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is_success = traj.score >= success_threshold
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if is_success:
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success_trajectories.append(traj)
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else:
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failure_trajectories.append(traj)
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return {
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'success': success_trajectories,
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'failure': failure_trajectories,
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'all': trajectories
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} |