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560e746cea
* reformat code, support flowllm 0.1.9 * Update README.md add FLOW_USE_FRAMEWORK=true * Update README_ZH.md add FLOW_USE_FRAMEWORK=true * Update index.md add FLOW_USE_FRAMEWORK=true * add env FLOW_APP_NAME=ReMe
75 lines
3.1 KiB
Python
75 lines
3.1 KiB
Python
from typing import List
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from flowllm import C, BaseAsyncOp
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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, parse_json_experience_response, get_trajectory_context
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@C.register_op()
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class FailureExtractionOp(BaseAsyncOp):
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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 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 hasattr(trajectory, 'segments') and trajectory.segments:
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# Process segmented step sequences
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for segment in trajectory.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(self, steps: List[Message], trajectory: Trajectory) -> List[BaseMemory]:
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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[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(messages=[FlowMessage(role=Role.USER, content=prompt)], callback_fn=parse_task_memories)
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