mirror of
https://github.com/agentscope-ai/ReMe.git
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96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
"""Success extraction operation for task memory generation.
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This module provides operations to extract task memories from successful
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trajectories, identifying patterns and strategies that lead to success.
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"""
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from typing import List
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from flowllm.core.context import C
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from flowllm.core.enumeration import Role
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from flowllm.core.op import BaseAsyncOp
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from flowllm.core.schema 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, parse_json_experience_response, get_trajectory_context
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@C.register_op()
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class SuccessExtractionOp(BaseAsyncOp):
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"""Extract task memories from successful trajectories.
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This operation analyzes successful trajectories (or their segments) to
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extract reusable patterns, strategies, and best practices that can be
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applied to similar future tasks.
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"""
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file_path: str = __file__
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async def async_execute(self):
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"""Extract task memories from successful trajectories"""
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success_trajectories: List[Trajectory] = self.context.success_trajectories
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if not success_trajectories:
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logger.info("No success trajectories found for extraction")
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return
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logger.info(f"Extracting task memories from {len(success_trajectories)} successful trajectories")
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success_task_memories = []
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# Process trajectories
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for trajectory in success_trajectories:
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if "segments" in trajectory.metadata:
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# Process segmented step sequences
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for segment in trajectory.metadata["segments"]:
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task_memories = await self._extract_success_task_memory_from_steps(segment, trajectory)
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success_task_memories.extend(task_memories)
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else:
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# Process entire trajectory
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task_memories = await self._extract_success_task_memory_from_steps(trajectory.messages, trajectory)
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success_task_memories.extend(task_memories)
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logger.info(f"Extracted {len(success_task_memories)} success task memories")
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# Add task memories to context
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self.context.success_task_memories = success_task_memories
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async def _extract_success_task_memory_from_steps(
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self,
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steps: List[Message],
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trajectory: Trajectory,
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) -> List[BaseMemory]:
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"""Extract task memory from successful step sequences"""
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step_content = merge_messages_content(steps)
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context = get_trajectory_context(trajectory, steps)
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prompt = self.prompt_format(
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prompt_name="success_step_task_memory_prompt",
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query=trajectory.metadata.get("query", ""),
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step_sequence=step_content,
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context=context,
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outcome="successful",
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)
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def parse_task_memories(message: Message) -> List[BaseMemory]:
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task_memories_data = parse_json_experience_response(message.content)
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task_memories = []
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for tm_data in task_memories_data:
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task_memory = TaskMemory(
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workspace_id=self.context.get("workspace_id", ""),
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when_to_use=tm_data.get("when_to_use", tm_data.get("condition", "")),
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content=tm_data.get("experience", ""),
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author=getattr(self.llm, "model_name", "system"),
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metadata=tm_data,
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)
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task_memories.append(task_memory)
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return task_memories
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return await self.llm.achat(
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messages=[FlowMessage(role=Role.USER, content=prompt)],
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callback_fn=parse_task_memories,
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)
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