5.2 工具执行流水线

本节介绍工具从被调用到返回结果的完整执行过程，包括6个关键阶段、流水线的实现细节、进度报告机制和并发执行策略，并通过实例代码展示如何构建一个可靠的工具执行系统。

5.2.1 执行流水线的完整过程

工具从被调用到返回结果的完整流程可以分为以下6个阶段：

图 5-2：工具执行流水线的6个阶段

5.2.2 完整的执行流水线实现

工具执行流水线的实现可以分为四个主要部分：初始化和主流程、验证阶段、执行和结果处理、以及缓存管理。

第一部分：流水线初始化与主流程

首先定义执行流水线的主体和6个阶段的总体流程：

"""工具执行流水线"""

from dataclasses import dataclass
from typing import Optional, Dict, Any, Callable, Tuple
from anthropic.types import ToolUseBlock, ToolResultBlock
import asyncio
import time
import json

class ExecutionPipeline:
    """工具执行流水线"""

    def __init__(self,
                 tool_registry: ToolRegistry,
                 max_result_size: int = 1024 * 1024,
                 default_timeout: int = 30):
        self.tool_registry = tool_registry
        self.max_result_size = max_result_size
        self.default_timeout = default_timeout
        self.execution_history = []
        self.result_cache = {}

    async def execute(self,
                     tool_use: ToolUseBlock,
                     context: Any,
                     progress_callback: Optional[Callable] = None) -> ToolResultBlock:
        """执行工具的完整流水线"""
        execution_record = {
            "tool_use_id": tool_use.id,
            "tool_name": tool_use.name,
            "start_time": time.time(),
            "stages": {}
        }

        try:
            # 阶段1:工具发现
            tool, error = await self._find_tool(tool_use.name, execution_record)
            if error:
                return self._create_error_result(tool_use, error)

            # 阶段2:权限检查
            is_allowed, error = await self._check_permissions(
                tool, context, execution_record
            )
            if not is_allowed:
                return self._create_error_result(tool_use, error)

            # 阶段3:输入验证
            is_valid, error = await self._validate_input(
                tool, tool_use.input, execution_record
            )
            if not is_valid:
                return self._create_error_result(tool_use, error)

            # 阶段4:工具执行
            result, error = await self._execute_tool(
                tool, tool_use.input, execution_record, progress_callback
            )
            if error:
                return self._create_error_result(tool_use, error)

            # 阶段5:结果处理
            processed_result, error = await self._process_result(
                result, execution_record
            )
            if error:
                return self._create_error_result(tool_use, error)

            # 阶段6:持久化
            await self._persist_result(
                tool_use.id, processed_result, execution_record
            )

            self.execution_history.append(execution_record)

            return ToolResultBlock(
                tool_use_id=tool_use.id,
                content=str(processed_result),
                is_error=False
            )

        except Exception as e:
            execution_record["error"] = str(e)
            self.execution_history.append(execution_record)
            return self._create_error_result(tool_use, str(e))

设计说明：主流程采用“早期返回”(Early Return)模式。任何阶段失败都立即返回错误，避免继续执行。这提高了效率并简化了错误处理。

第二部分：验证阶段（工具发现、权限检查、输入验证）

这部分处理执行前的三个验证阶段：

    async def _find_tool(self, tool_name: str,
                        execution_record: Dict) -> Tuple[Optional[Tool], Optional[str]]:
        """阶段1:工具发现"""
        start = time.time()
        tool = self.tool_registry.get(tool_name)
        execution_record["stages"]["find_tool"] = {
            "duration": time.time() - start,
            "found": tool is not None
        }
        if not tool:
            return None, f"Tool '{tool_name}' not found"
        return tool, None

    async def _check_permissions(self, tool: Tool, context: Any,
                                execution_record: Dict) -> Tuple[bool, Optional[str]]:
        """阶段2:权限检查"""
        start = time.time()
        try:
            has_permission = tool.check_permissions(context)
            execution_record["stages"]["check_permissions"] = {
                "duration": time.time() - start,
                "result": has_permission
            }
            if not has_permission:
                return False, f"Permission denied for tool '{tool.name()}'"
            return True, None
        except Exception as e:
            return False, f"Permission check error: {str(e)}"

    async def _validate_input(self, tool: Tool, input_data: Dict[str, Any],
                             execution_record: Dict) -> Tuple[bool, Optional[str]]:
        """阶段3:输入验证"""
        start = time.time()
        schema = tool.input_schema()
        errors = []

        required = schema.get("required", [])
        for field in required:
            if field not in input_data:
                errors.append(f"Missing required field: {field}")

        properties = schema.get("properties", {})
        for field_name, field_value in input_data.items():
            if field_name not in properties:
                errors.append(f"Unknown field: {field_name}")
                continue
            field_schema = properties[field_name]
            if not self._check_type(field_value, field_schema):
                expected_type = field_schema.get("type", "unknown")
                errors.append(
                    f"Field '{field_name}' validation failed: "
                    f"expected {expected_type}"
                )

        execution_record["stages"]["validate_input"] = {
            "duration": time.time() - start,
            "valid": len(errors) == 0,
            "errors": errors
        }

        if errors:
            return False, "; ".join(errors)
        return True, None

    def _check_type(self, value: Any, schema: Dict[str, Any]) -> bool:
        """基于JSON Schema的类型验证"""
        import jsonschema
        try:
            jsonschema.validate(value, schema)
            return True
        except jsonschema.ValidationError:
            return False

设计说明：验证阶段按顺序执行，逐步变严格。工具发现最快，然后是权限检查，最后是复杂的输入验证。这样可以快速拒绝无效请求。

第三部分：执行和结果处理（工具执行、结果处理、持久化）

这部分处理核心的执行逻辑和结果的后处理：

    async def _execute_tool(self,
                           tool: Tool,
                           input_data: Dict[str, Any],
                           execution_record: Dict,
                           progress_callback: Optional[Callable] = None
                           ) -> Tuple[Any, Optional[str]]:
        """阶段4:工具执行(带超时控制)"""
        start = time.time()
        try:
            result = await asyncio.wait_for(
                tool.call(input_data),
                timeout=self.default_timeout
            )
            execution_record["stages"]["execute_tool"] = {
                "duration": time.time() - start,
                "success": True
            }
            return result, None
        except asyncio.TimeoutError:
            error = f"Tool execution timeout ({self.default_timeout}s)"
            execution_record["stages"]["execute_tool"] = {
                "duration": time.time() - start,
                "success": False,
                "error": "timeout"
            }
            return None, error
        except Exception as e:
            execution_record["stages"]["execute_tool"] = {
                "duration": time.time() - start,
                "success": False,
                "error": type(e).__name__
            }
            return None, f"Tool execution error: {str(e)}"

    async def _process_result(self, result: Any,
                             execution_record: Dict) -> Tuple[str, Optional[str]]:
        """阶段5:结果处理(序列化和截断)"""
        start = time.time()
        try:
            if isinstance(result, str):
                result_str = result
            elif isinstance(result, (dict, list)):
                result_str = json.dumps(result, indent=2)
            else:
                result_str = str(result)

            if len(result_str) > self.max_result_size:
                original_size = len(result_str)
                result_str = result_str[:self.max_result_size]
                result_str += f"\n... [Output truncated from {original_size} bytes]"
                execution_record["stages"]["process_result"] = {
                    "duration": time.time() - start,
                    "truncated": True,
                    "original_size": original_size,
                }
            else:
                execution_record["stages"]["process_result"] = {
                    "duration": time.time() - start,
                    "truncated": False,
                    "size": len(result_str)
                }
            return result_str, None
        except Exception as e:
            return None, f"Result processing error: {str(e)}"

    async def _persist_result(self, tool_use_id: str, result: str,
                             execution_record: Dict):
        """阶段6:持久化(缓存)"""
        start = time.time()
        self.result_cache[tool_use_id] = {
            "content": result,
            "timestamp": time.time()
        }
        execution_record["stages"]["persist_result"] = {
            "duration": time.time() - start,
            "cached": True
        }

    def _create_error_result(self, tool_use: ToolUseBlock,
                            error: str) -> ToolResultBlock:
        """创建错误结果"""
        return ToolResultBlock(
            tool_use_id=tool_use.id,
            content=error,
            is_error=True,
            error_type="ExecutionError"
        )

设计说明：_execute_tool 使用 asyncio.wait_for 实现超时控制，防止卡顿。_process_result 对大结果进行截断，确保不超过最大大小限制。这两个措施都是保护系统稳定性的关键。

第四部分：执行历史和缓存管理

最后是查询执行历史和清理缓存的工具方法：

    def get_execution_history(self, tool_use_id: Optional[str] = None):
        """获取执行历史"""
        if tool_use_id:
            return [r for r in self.execution_history
                   if r["tool_use_id"] == tool_use_id]
        return self.execution_history

    def clear_cache(self, max_age: int = 3600):
        """清理缓存(删除超过max_age秒的项)"""
        now = time.time()
        to_delete = [
            key for key, value in self.result_cache.items()
            if now - value["timestamp"] > max_age
        ]
        for key in to_delete:
            del self.result_cache[key]
        return len(to_delete)

5.2.3 进度事件流

支持长时间运行的工具的进度报告：

from typing import Optional, Dict
import asyncio
import time

class ProgressMonitor:
    """进度监控器"""

    def __init__(self, tool_use_id: str):
        self.tool_use_id = tool_use_id
        self.progress_events = []

    def report_progress(self, step: int, total_steps: int,
                       status: str, estimated_time_remaining: Optional[float] = None):
        """报告进度"""
        event = {
            "tool_use_id": self.tool_use_id,
            "timestamp": time.time(),
            "step": step,
            "total_steps": total_steps,
            "percentage": (step / total_steps * 100) if total_steps > 0 else 0,
            "status": status,
            "estimated_time_remaining": estimated_time_remaining
        }
        self.progress_events.append(event)
        return event

    def get_current_progress(self) -> Optional[Dict]:
        """获取当前进度"""
        return self.progress_events[-1] if self.progress_events else None

# 在工具执行中使用
async def long_running_tool_call(progress_monitor: ProgressMonitor):
    """长时间运行的工具调用示例"""
    total_items = 1000

    for i in range(total_items):
        # 执行工作
        await asyncio.sleep(0.01)  # 模拟工作

        # 报告进度
        if i % 100 == 0:
            progress_monitor.report_progress(
                step=i,
                total_steps=total_items,
                status=f"Processing item {i}/{total_items}",
                estimated_time_remaining=(total_items - i) * 0.01
            )

    return f"Processed {total_items} items"

5.2.4 并发工具执行

具体实现如下：

from typing import List, Dict, Any
import asyncio

class StreamingToolExecutor:
    """并发工具执行器"""

    def __init__(self, pipeline: ExecutionPipeline,
                 max_concurrent: int = 5):
        self.pipeline = pipeline
        self.max_concurrent = max_concurrent

    async def execute_tools(self,
                           tool_uses: List[ToolUseBlock],
                           context: Any) -> Dict[str, ToolResultBlock]:
        """
        并发执行多个工具

        Args:
            tool_uses: 工具调用列表
            context: 执行上下文

        Returns:
            {tool_use_id: tool_result_block}
        """

        semaphore = asyncio.Semaphore(self.max_concurrent)

        async def execute_with_semaphore(tool_use):
            async with semaphore:
                return tool_use.id, await self.pipeline.execute(
                    tool_use, context
                )

        results = await asyncio.gather(
            *[execute_with_semaphore(tu) for tu in tool_uses],
            return_exceptions=True
        )

        # 组织结果
        result_dict = {}
        for tool_use_id, result in results:
            if isinstance(result, Exception):
                result_dict[tool_use_id] = ToolResultBlock(
                    tool_use_id=tool_use_id,
                    content=str(result),
                    is_error=True,
                    error_type=type(result).__name__
                )
            else:
                result_dict[tool_use_id] = result

        return result_dict

5.2.5 本节小结

工具执行流水线的设计决定了系统的可靠性和性能：

1. 6 个执行阶段 清晰分离，每个阶段有明确的职责和错误处理

2. 权限检查 在执行前进行，防止未授权的操作

3. 输入验证 基于 JSON Schema，确保工具收到有效参数

4. 超时控制 防止工具阻塞整个系统

5. 结果处理 包括序列化、截断、缓存，处理大结果的问题

6. 执行历史记录 支持审计和调试

7. 并发执行 和 进度报告 支持长时间运行的工具