mirror of
https://github.com/agentscope-ai/ReMe.git
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95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
"""Failure extraction operation for task memory generation.
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This module provides operations to extract task memories from failed trajectories,
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identifying mistakes, pitfalls, and lessons learned from failures.
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"""
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from typing import List
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from loguru import logger
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from ....core.enumeration import MemoryType, Role
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from ....core.op import BaseOp
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from ....core.schema.memory_node import MemoryNode
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from ....core.schema.message import Message, Trajectory
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from ..utils import (
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get_trajectory_context,
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merge_messages_content,
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parse_json_experience_response,
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)
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class FailureExtraction(BaseOp):
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"""Extract task memories from failed trajectories.
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This operation analyzes failed trajectories (or their segments) to extract
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lessons learned, common mistakes, and anti-patterns that should be avoided
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in similar future tasks.
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"""
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async def execute(self):
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"""Extract task memories from failed trajectories"""
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failure_trajectories: List[Trajectory] = self.context.get("failure_trajectories", [])
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if not failure_trajectories:
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logger.info("No failure trajectories found for extraction")
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return
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logger.info(f"Extracting task memories from {len(failure_trajectories)} failed trajectories")
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failure_task_memories = []
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# Process trajectories
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for trajectory in failure_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_failure_task_memory_from_steps(segment, trajectory)
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failure_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_failure_task_memory_from_steps(trajectory.messages, trajectory)
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failure_task_memories.extend(task_memories)
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logger.info(f"Extracted {len(failure_task_memories)} failure task memories")
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# Add task memories to context
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self.context.failure_task_memories = failure_task_memories
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async def _extract_failure_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[MemoryNode]:
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"""Extract task memory from failed 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="failure_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="failed",
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)
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def parse_task_memories(message: Message) -> List[MemoryNode]:
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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 = MemoryNode(
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memory_type=MemoryType.PROCEDURAL,
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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.chat(
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messages=[Message(role=Role.USER, content=prompt)],
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callback_fn=parse_task_memories,
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)
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