mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-19 00:01:33 +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
110 lines
4.4 KiB
Python
110 lines
4.4 KiB
Python
import json
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import re
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from typing import List, Dict, Any
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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
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from reme_ai.schema.memory import BaseMemory
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@C.register_op()
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class MemoryValidationOp(BaseLLMOp):
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file_path: str = __file__
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async def async_execute(self):
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"""Validate quality of extracted task memories"""
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task_memories: List[BaseMemory] = []
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task_memories.extend(self.context.get("success_task_memories", []))
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task_memories.extend(self.context.get("failure_task_memories", []))
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task_memories.extend(self.context.get("comparative_task_memories", []))
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if not task_memories:
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logger.info("No task memories found for validation")
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return
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logger.info(f"Validating {len(task_memories)} extracted task memories")
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# Validate task memories
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validated_task_memories = []
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for task_memory in task_memories:
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validation_result = await self._validate_single_task_memory(task_memory)
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if validation_result and validation_result.get("is_valid", False):
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task_memory.score = validation_result.get("score", 0.0)
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validated_task_memories.append(task_memory)
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else:
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reason = validation_result.get("reason", "Unknown reason") if validation_result else "Validation failed"
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logger.warning(f"Task memory validation failed: {reason}")
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logger.info(f"Validated {len(validated_task_memories)} out of {len(task_memories)} task memories")
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# Update context
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self.context.response.answer = json.dumps([x.model_dump() for x in validated_task_memories])
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self.context.response.metadata["memory_list"] = validated_task_memories
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async def _validate_single_task_memory(self, task_memory: BaseMemory) -> Dict[str, Any]:
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"""Validate single task memory"""
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validation_info = await self._llm_validate_task_memory(task_memory)
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logger.info(f"Validating: {validation_info}")
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return validation_info
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async def _llm_validate_task_memory(self, task_memory: BaseMemory) -> Dict[str, Any]:
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"""Validate task memory using LLM"""
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try:
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prompt = self.prompt_format(
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prompt_name="task_memory_validation_prompt",
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condition=task_memory.when_to_use,
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task_memory_content=task_memory.content)
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def parse_validation(message: Message) -> Dict[str, Any]:
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try:
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response_content = message.content
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# Parse validation result
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# Extract JSON blocks
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json_pattern = r'```json\s*([\s\S]*?)\s*```'
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json_blocks = re.findall(json_pattern, response_content)
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if json_blocks:
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parsed = json.loads(json_blocks[0])
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else:
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parsed = {}
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is_valid = parsed.get("is_valid", True)
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score = parsed.get("score", 0.5)
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# Set validation threshold
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validation_threshold = self.op_params.get("validation_threshold", 0.5)
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return {
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"is_valid": is_valid and score >= validation_threshold,
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"score": score,
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"feedback": response_content,
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"reason": "" if (
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is_valid and score >= validation_threshold) else f"Low validation score ({score:.2f}) or marked as invalid"
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}
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except Exception as e_inner:
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logger.exception(f"Error parsing validation response: {e_inner}")
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return {
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"is_valid": False,
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"score": 0.0,
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"feedback": "",
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"reason": f"Parse error: {str(e_inner)}"
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}
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return await self.llm.achat(messages=[FlowMessage(role=Role.USER, content=prompt)], callback_fn=parse_validation)
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except Exception as e:
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logger.error(f"LLM validation failed: {e}")
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return {
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"is_valid": False,
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"score": 0.0,
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"feedback": "",
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"reason": f"LLM validation error: {str(e)}"
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}
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