添加中文注释
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@ -1,6 +1,6 @@
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# Agent Task Executor
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A flexible task execution framework that uses LLMs (Large Language Models).
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A flexible task execution framework that uses LLMs (Large Language Models).The framework provides a robust foundation for building task-specific executors while handling common execution concerns.
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## Features
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@ -8,38 +8,41 @@ import logging
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import asyncio
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from .llm_executor import LLMExecutor
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# 任务状态枚举
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class TaskStatus(Enum):
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PENDING = "pending"
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IN_PROGRESS = "in_progress"
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COMPLETED = "completed"
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FAILED = "failed"
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PENDING = "pending" # 等待中
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IN_PROGRESS = "in_progress" # 进行中
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COMPLETED = "completed" # 已完成
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FAILED = "failed" # 已失败
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# 步骤状态数据类
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@dataclass
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class StepState:
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step_id: str
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status: TaskStatus
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required_info: List[str]
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available_info: List[str]
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missing_info: List[str]
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resources_used: Dict[str, Any]
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step_id: str # 步骤ID
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status: TaskStatus # 当前状态
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required_info: List[str] # 所需信息
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available_info: List[str] # 可用信息
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missing_info: List[str] # 缺失信息
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resources_used: Dict[str, Any] # 已用资源
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# 任务执行器主类
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class TaskExecutor:
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MAX_RETRIES = 3
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TIMEOUT = 300 # seconds
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CHECKPOINT_INTERVAL = 5 # steps
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MAX_RETRIES = 3 # 最大重试次数
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TIMEOUT = 300 # 超时时间（秒）
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CHECKPOINT_INTERVAL = 5 # 检查点间隔（步骤数）
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def __init__(self, llm_model: str = None):
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"""Initialize TaskExecutor."""
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self.task_id = str(uuid.uuid4())
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self.start_time = time.time()
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self.checkpoints = []
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self.execution_path = []
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self.current_step = None
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self.retry_count = 0
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self.llm_executor = LLMExecutor(model=llm_model)
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self.task_input = None
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"""初始化任务执行器"""
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self.task_id = str(uuid.uuid4()) # 生成唯一任务ID
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self.start_time = time.time() # 记录开始时间
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self.checkpoints = [] # 检查点列表
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self.execution_path = [] # 执行路径记录
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self.current_step = None # 当前步骤
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self.retry_count = 0 # 重试计数器
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self.llm_executor = LLMExecutor(model=llm_model) # LLM执行器
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self.task_input = None # 任务输入
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# Configure logging
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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@ -47,144 +50,150 @@ class TaskExecutor:
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self.logger = logging.getLogger(f"TaskExecutor-{self.task_id}")
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def get_status_update(self) -> dict:
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"""Generate a status update for the current execution state."""
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"""生成当前执行状态的状态更新"""
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return {
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"task_id": self.task_id,
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
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"current_step": self.current_step,
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"checkpoints": self.checkpoints,
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"task_id": self.task_id, # 任务ID
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"), # 时间戳
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"current_step": self.current_step, # 当前步骤
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"checkpoints": self.checkpoints, # 检查点列表
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"resources": {
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"used": {
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"time": time.time() - self.start_time,
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"memory": "N/A" # To be implemented
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"time": time.time() - self.start_time, # 已用时间
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"memory": "N/A" # 待实现：内存使用
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},
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"available": {
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"time": self.TIMEOUT - (time.time() - self.start_time),
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"memory": "N/A" # To be implemented
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"time": self.TIMEOUT - (time.time() - self.start_time), # 剩余时间
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"memory": "N/A" # 待实现：可用内存
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}
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},
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"execution_path": self.execution_path,
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"status": TaskStatus.COMPLETED.value if self.current_step and self.current_step.get("status") == TaskStatus.COMPLETED.value else TaskStatus.IN_PROGRESS.value
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"execution_path": self.execution_path, # 执行路径
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"status": TaskStatus.COMPLETED.value if self.current_step and self.current_step.get("status") == TaskStatus.COMPLETED.value else TaskStatus.IN_PROGRESS.value # 当前状态
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}
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async def execute_step(self, step_id: str, step_data: Dict[str, Any]) -> bool:
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"""Execute a single step of the task using LLM."""
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"""使用LLM执行任务的单个步骤"""
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try:
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# 初始化当前步骤状态
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self.current_step = {
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"id": step_id,
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"name": step_data.get("name", "Unknown"),
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"status": TaskStatus.IN_PROGRESS.value,
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"progress": 0
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"id": step_id, # 步骤ID
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"name": step_data.get("name", "Unknown"), # 步骤名称
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"status": TaskStatus.IN_PROGRESS.value, # 状态设为进行中
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"progress": 0 # 进度初始化为0
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}
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# Check if execution should continue
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# 检查是否超时
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if time.time() - self.start_time > self.TIMEOUT:
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raise TimeoutError("Task execution timeout")
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raise TimeoutError("任务执行超时")
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# Execute step using LLM
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# 使用LLM执行步骤
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step_result = await self.llm_executor.execute_step(
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step_instruction=step_data.get("instruction", ""),
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step_input=step_data.get("input", {}),
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context=step_data.get("context", {})
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step_instruction=step_data.get("instruction", ""), # 步骤指令
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step_input=step_data.get("input", {}), # 步骤输入
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context=step_data.get("context", {}) # 上下文信息
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)
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# 检查步骤是否成功
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if not step_result["success"]:
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raise Exception(f"Step failed: {step_result['error']}")
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raise Exception(f"步骤失败: {step_result['error']}")
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# Update step status
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# 更新执行路径
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self.execution_path.append({
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"step_id": step_id,
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"result": step_result["output"]
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"step_id": step_id, # 步骤ID
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"result": step_result["output"] # 步骤结果
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})
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self.current_step["status"] = TaskStatus.COMPLETED.value
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self.current_step["progress"] = 100
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# 更新步骤状态
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self.current_step["status"] = TaskStatus.COMPLETED.value # 状态设为已完成
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self.current_step["progress"] = 100 # 进度设为100%
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# Create checkpoint if needed
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# 如果需要创建检查点
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if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
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self.create_checkpoint()
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return True
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except Exception as e:
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self.current_step["status"] = TaskStatus.FAILED.value
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return self.handle_error(e)
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# 处理异常
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self.current_step["status"] = TaskStatus.FAILED.value # 状态设为失败
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return self.handle_error(e) # 调用错误处理
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def validate_input(self, input_data: Dict[str, Any]) -> bool:
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"""Validate the input data for the task."""
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return True
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"""验证任务输入数据"""
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return True # 默认实现，子类可重写
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def create_checkpoint(self):
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"""Create a checkpoint of the current execution state."""
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"""创建当前执行状态的检查点"""
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checkpoint = {
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
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"task_id": self.task_id,
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"current_step": self.current_step,
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"execution_path": self.execution_path.copy(),
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"), # 时间戳
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"task_id": self.task_id, # 任务ID
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"current_step": self.current_step, # 当前步骤
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"execution_path": self.execution_path.copy(), # 执行路径副本
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"resources": {
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"used": {
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"time": time.time() - self.start_time,
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"memory": "N/A" # To be implemented
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"time": time.time() - self.start_time, # 已用时间
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"memory": "N/A" # 待实现：内存使用
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}
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}
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}
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self.checkpoints.append(checkpoint)
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self.logger.info(f"Created checkpoint: {json.dumps(checkpoint, indent=2)}")
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self.checkpoints.append(checkpoint) # 添加到检查点列表
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self.logger.info(f"创建检查点: {json.dumps(checkpoint, indent=2)}") # 记录日志
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def rollback_to_checkpoint(self, checkpoint_index: int):
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"""Rollback the execution to a specific checkpoint."""
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"""回滚到指定检查点"""
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if 0 <= checkpoint_index < len(self.checkpoints):
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checkpoint = self.checkpoints[checkpoint_index]
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# Implement state restoration logic
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self.logger.info(f"Rolling back to checkpoint: {checkpoint['timestamp']}")
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checkpoint = self.checkpoints[checkpoint_index] # 获取检查点
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# 实现状态恢复逻辑
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self.logger.info(f"回滚到检查点: {checkpoint['timestamp']}") # 记录日志
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return True
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return False
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return False # 检查点索引无效
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def get_next_actions(self) -> List[str]:
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"""Determine the next possible actions based on current state."""
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"""根据当前状态确定下一步可能的操作"""
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actions = []
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if self.current_step and self.current_step["status"] == TaskStatus.FAILED.value:
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actions.extend(["retry", "rollback", "abort"])
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actions.extend(["retry", "rollback", "abort"]) # 失败时可重试、回滚或中止
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elif self.current_step and self.current_step["status"] == TaskStatus.COMPLETED.value:
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actions.extend(["continue", "checkpoint"])
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actions.extend(["continue", "checkpoint"]) # 完成时可继续或创建检查点
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return actions
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def handle_error(self, error: Exception):
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"""Handle execution errors."""
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self.logger.error(f"Error occurred: {str(error)}")
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self.retry_count += 1
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"""处理执行错误"""
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self.logger.error(f"发生错误: {str(error)}") # 记录错误日志
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self.retry_count += 1 # 增加重试计数
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if self.retry_count >= self.MAX_RETRIES:
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self.logger.error("Max retries reached. Terminating execution.")
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self.logger.error("达到最大重试次数。终止执行。") # 记录终止日志
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return False
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# Implement error recovery logic
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return True
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# 实现错误恢复逻辑
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return True # 允许重试
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async def execute(self, task_input: Dict[str, Any]) -> Dict[str, Any]:
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"""Execute the complete task."""
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"""执行完整任务"""
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try:
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# Store task input
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# 存储任务输入
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self.task_input = task_input
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# 验证输入
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if not self.validate_input(task_input):
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raise ValueError("Invalid task input")
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raise ValueError("无效的任务输入")
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self.logger.info(f"Starting task execution: {self.task_id}")
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self.logger.info(f"开始任务执行: {self.task_id}") # 记录开始日志
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# Execute each step in sequence
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# 按顺序执行每个步骤
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if hasattr(self, 'task_steps'):
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for step in self.task_steps:
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if not await self.execute_step(step["id"], step):
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raise Exception(f"Step {step['id']} failed")
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raise Exception(f"步骤 {step['id']} 失败")
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# 按间隔创建检查点
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if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
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self.create_checkpoint()
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else:
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raise ValueError("No task steps defined")
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raise ValueError("未定义任务步骤")
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return self.get_status_update()
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return self.get_status_update() # 返回最终状态
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except Exception as e:
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self.logger.error(f"Task failed: {str(e)}")
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return self.get_status_update()
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self.logger.error(f"任务失败: {str(e)}") # 记录失败日志
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return self.get_status_update() # 返回失败状态
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@ -4,9 +4,12 @@ import time
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import json
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import re
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# 中文文本分析执行器
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class TextAnalysisExecutor(TaskExecutor):
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def __init__(self):
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"""初始化中文文本分析执行器"""
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super().__init__(llm_model="deepseek-chat")
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# 定义任务步骤
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self.task_steps = [
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{
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"id": "input_validation",
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@ -41,30 +44,31 @@ class TextAnalysisExecutor(TaskExecutor):
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]
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def validate_input(self, input_data: Dict[str, Any]) -> bool:
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"""Validate specific input requirements for the text analysis task."""
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"""验证文本分析任务的输入数据"""
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if "text" not in input_data:
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return False
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text = input_data.get("text", "")
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return isinstance(text, str) and len(text.strip()) > 0
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async def execute_step(self, step_id: str, step_data: Dict[str, Any]) -> bool:
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"""Execute a specific step of the text analysis task."""
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"""执行文本分析任务的特定步骤"""
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try:
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# 初始化当前步骤状态
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self.current_step = {
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"id": step_id,
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"name": step_data["name"],
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"status": TaskStatus.IN_PROGRESS.value,
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"progress": 0
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"id": step_id, # 步骤ID
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"name": step_data["name"], # 步骤名称
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"status": TaskStatus.IN_PROGRESS.value, # 状态设为进行中
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"progress": 0 # 进度初始化为0
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}
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# Get step instruction and input
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# 获取步骤指令和输入
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instruction = step_data.get("instruction", "")
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step_input = {}
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# Prepare step-specific input
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# 准备步骤特定的输入
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if step_id == "input_validation":
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text = self.task_input.get("text", "")
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# Validate Chinese text encoding
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# 验证中文文本编码
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try:
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text.encode('utf-8').decode('utf-8')
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except UnicodeError:
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@ -73,28 +77,28 @@ class TextAnalysisExecutor(TaskExecutor):
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elif step_id == "text_preprocessing":
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text = self.task_input.get("text", "")
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# Basic Chinese text preprocessing
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# Normalize whitespace while preserving Chinese text structure
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# 中文文本预处理
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# 1. 规范化空白字符，同时保留中文文本结构
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text = re.sub(r'\s+', ' ', text).strip()
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# Normalize Chinese punctuation (simple example)
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# 2. 规范化中文标点符号（简单示例）
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text = text.replace('，', ',').replace('。', '.').replace('：', ':')
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step_input = {"text": text}
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elif step_id == "generate_summary":
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# Get the preprocessed text from previous step
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# 从上一步获取预处理后的文本
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prev_result = next(
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(step["result"] for step in self.execution_path if step["step_id"] == "text_preprocessing"),
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{}
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)
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step_input = {"text": prev_result.get("preprocessed_text", self.task_input.get("text", ""))}
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elif step_id == "extract_keywords":
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# Get the preprocessed text from previous step
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# 从上一步获取预处理后的文本
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prev_result = next(
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(step["result"] for step in self.execution_path if step["step_id"] == "text_preprocessing"),
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{}
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)
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step_input = {"text": prev_result.get("preprocessed_text", self.task_input.get("text", ""))}
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elif step_id == "final_analysis":
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# Get summary and keywords from previous steps
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# 从之前的步骤获取摘要和关键词
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summary_result = next(
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(step["result"] for step in self.execution_path if step["step_id"] == "generate_summary"),
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{}
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@ -108,7 +112,7 @@ class TextAnalysisExecutor(TaskExecutor):
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"keywords": keywords_result.get("keywords", [])
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}
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# Execute step using LLM
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# 使用LLM执行步骤
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step_result = await self.llm_executor.execute_step(
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step_instruction=instruction,
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step_input=step_input
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@ -117,17 +121,17 @@ class TextAnalysisExecutor(TaskExecutor):
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if not step_result.get("success", False):
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raise Exception(f"Step failed: {step_result.get('error', 'Unknown error')}")
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# Update execution path
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# 更新执行路径
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self.execution_path.append({
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"step_id": step_id,
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"result": step_result.get("output", {})
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})
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# Update step status
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# 更新步骤状态
|
||||
self.current_step["status"] = TaskStatus.COMPLETED.value
|
||||
self.current_step["progress"] = 100
|
||||
|
||||
# Create checkpoint if needed
|
||||
# 如果需要创建检查点
|
||||
if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
|
||||
self.create_checkpoint()
|
||||
|
||||
@ -139,32 +143,33 @@ class TextAnalysisExecutor(TaskExecutor):
|
||||
return False
|
||||
|
||||
def main():
|
||||
# Set console encoding to UTF-8
|
||||
"""主函数：执行中文文本分析示例"""
|
||||
# 设置控制台编码为UTF-8
|
||||
import sys, io
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
|
||||
|
||||
# Create the executor
|
||||
# 创建执行器实例
|
||||
executor = TextAnalysisExecutor()
|
||||
|
||||
# Sample Chinese text for analysis
|
||||
# 示例中文文本
|
||||
sample_text = """
|
||||
从 ChatGPT 到 Devin：AI 编程的四个发展阶段与范式转变 Koji：我们再聊一聊 AI 编程。编程领域今年取得了非常令人兴奋的进展。雨森一直有很强的框架归纳和总结能力。前不久你跟我分享过你提炼出来的 AI 编程发展四段论，要不要在播客里和大家分享一下？ 雨森：这其实是和很多朋友一起探讨得出的结果，是大家智慧的结晶。AI 编程从 ChatGPT 出现到现在也就两年出头的时间，但已经经历了四个阶段。 第一个阶段是让 AI 直接写代码，典型代表是早期的 ChatGPT、Claude。我们给它一个需求，比如「帮我写个贪吃蛇」，它就给出一段代码。在这个过程中，它既不知道我为什么要写贪吃蛇，也不知道代码运行情况如何。可能要我去本地编译运行后发现报错，再把错误告诉它，它才能给出调试后的结果。这时的 AI 完全就像一个只能通过邮件交流的笔友，是简单的问答模式。 第二阶段是以 GitHub Copilot 为代表，AI 开始拥有上下文，它可以把整个组织的代码库作为 context。这样 AI 就获得了大量新的背景信息。但这时用户还是需要手动把代码贴到 IDE 里面进行调试。我觉得这是 2.0 阶段，就是我们让 AI 拥有了 codebase 作为上下文。 2024 年一个非常大的进步是以 Cursor 为代表的编程 Copilot 的出现。它的核心理念是预测用户未来要写什么代码。根据你的代码库以及刚才写的代码，它预测你接下来要写什么代码、创建什么文件、做什么操作。这里面对于生成代码的质量和数量，以及文件的创建和修改都有很大提升。后来 Windsurf 还加入了对命令行操作的自动化，这样 AI 就能很好地使用我的电脑。原来的 AI 是在一张纸上写代码，我把代码抄走运行；现在 AI 可以在我的电脑上创建文件、执行命令行操作，进入到「我为你写」的阶段。 当我们觉得这已经很令人兴奋时，Devin 的出现带来了几个重要突破：首先，它可以异步工作。Cursor、Windsurf 这些工具虽然一步操作做的事情比较多，但仍然需要持续的注意力，即「我说一步它做一步」。而 Devin 可以持续工作，把用户的注意力释放出来。这是因为它多了一个 Planner，可以规划任务。 其次，它可以通过虚拟机执行更多操作，做更多调试工作。比如你写个网站，它可以自己用虚拟机去访问这个网站，检查前端后端的业务逻辑是否正确，并且可以随时打断和调整。大家用 Cursor 或者 ChatGPT 都知道，你无法在它输出的中间做调整，必须等它输出完后才能修改。但 Devin 就像真人一样，你可以在它完成任务时给出新指令，它会把这个结合到已有的 Planner 里调整计划。这就从「为你写」进化到了「为你做」。 总结一下这四个阶段：第一阶段是让 AI 写代码，代表是 ChatGPT；第二阶段是 AI 开放代码库，代表是 GitHub Copilot；第三阶段是 AI 可以自动写代码并执行，代表是 Cursor 和 Windsurf；第四阶段是 AI 虚拟员工，Devin 开创了一个很好的先例。
|
||||
"""
|
||||
|
||||
# Prepare input
|
||||
# 准备输入数据
|
||||
task_input = {
|
||||
"text": sample_text
|
||||
}
|
||||
|
||||
# Execute the task
|
||||
# 执行任务
|
||||
import asyncio
|
||||
result = asyncio.run(executor.execute(task_input))
|
||||
|
||||
# Print results with proper formatting
|
||||
# 格式化输出结果
|
||||
print("\n 文本分析结果:")
|
||||
print("=" * 50)
|
||||
|
||||
# Print each step's result with proper formatting
|
||||
# 打印每个步骤的结果
|
||||
for step in result.get("execution_path", []):
|
||||
step_id = step["step_id"]
|
||||
result_data = step["result"]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user