NVIDIA Vera CPU：首款专为Agentic AI设计的CPU架构深度解析

# Agentic AI的核心循环
class AgenticLoop:
    def __init__(self, llm, tools, memory):
        self.llm = llm
        self.tools = tools
        self.memory = memory
    
    async def run(self, user_goal: str) -> str:
        """Agentic AI的核心执行循环"""
        # 1. 感知阶段：从记忆中检索相关上下文
        context = await self.memory.retrieve(user_goal)
        
        # 2. 规划阶段：大模型分解任务
        plan = await self.llm.plan(user_goal, context)
        
        # 3. 执行阶段：按计划调用工具
        for step in plan.steps:
            result = await self.execute_step(step)
            
            # 4. 反思阶段：评估结果，必要时调整计划
            if not self.evaluate(result):
                plan = await self.llm.replan(plan, result)
        
        # 5. 学习阶段：存储执行经验
        await self.memory.store(plan, result)
        
        return plan.final_answer

// Go示例：展示如何利用Vera CPU的并行处理能力
package main

import (
    "context"
    "fmt"
    "sync"
    
    "github.com/nvidia/vera-go/sdk"
)

type AgentCoordinator struct {
    client *vera.Client
    workers int
}

func NewAgentCoordinator(workers int) (*AgentCoordinator, error) {
    client, err := vera.NewClient(vera.Config{
        Architecture: vera.Olympus,
        MemoryBandwidth: "1.2TB/s",
        FP8Enabled: true,
    })
    if err != nil {
        return nil, err
    }
    
    return &AgentCoordinator{
        client: client,
        workers: workers,
    }, nil
}

// 并行执行多个Agent任务，充分利用88核心
func (ac *AgentCoordinator) RunAgents(ctx context.Context, tasks []AgentTask) ([]Result, error) {
    var wg sync.WaitGroup
    results := make([]Result, len(tasks))
    
    // 创建工作池，充分利用Vera的并行处理能力
    pool, err := ac.client.CreateWorkerPool(ac.workers)
    if err != nil {
        return nil, err
    }
    defer pool.Close()
    
    for i, task := range tasks {
        wg.Add(1)
        go func(idx int, t AgentTask) {
            defer wg.Done()
            
            // 每个worker独立处理一个Agent任务
            result, err := pool.Execute(ctx, vera.Task{
                Type:    vera.AgentTask,
                Payload: t.ToBytes(),
                Options: vera.TaskOptions{
                    FP8Acceleration: true,
                    Priority:        t.Priority,
                },
            })
            
            if err != nil {
                results[idx] = Result{Error: err}
                return
            }
            
            results[idx] = Result{Output: result.Output, Metrics: result.Metrics}
        }(i, task)
    }
    
    wg.Wait()
    return results, nil
}

type AgentTask struct {
    ID       string
    Type     string
    Input    []byte
    Priority int
}

type Result struct {
    Output  []byte
    Metrics map[string]float64
    Error   error
}

# Python示例：利用Vera的高带宽内存处理长上下文
import asyncio
from typing import List, Dict, Any
import numpy as np

class VeraLongContextProcessor:
    """
    利用Vera CPU的1.2TB/s带宽处理超长上下文
    支持百万Token级别的上下文窗口
    """
    
    def __init__(self, model_name: str = "claude-4"):
        self.model_name = model_name
        self.context_window = 1_000_000  # 100万Token
        
    async def process_long_context(
        self,
        documents: List[Dict[str, Any]],
        query: str
    ) -> Dict[str, Any]:
        """
        处理长文档上下文，提取相关信息
        """
        # 1. 并行加载文档到高速缓存
        cached_docs = await self._parallel_load(documents)
        
        # 2. 利用Vera的内存带宽优势进行向量化
        embeddings = await self._fast_embed(cached_docs)
        
        # 3. 近似最近邻搜索
        relevant_chunks = await self._semantic_search(
            query, embeddings, cached_docs, top_k=20
        )
        
        # 4. 生成答案
        answer = await self._generate_with_context(query, relevant_chunks)
        
        return {
            "answer": answer,
            "sources": [c["source"] for c in relevant_chunks],
            "context_length": sum(len(c["content"]) for c in relevant_chunks)
        }
    
    async def _parallel_load(self, docs: List[Dict]) -> List[Dict]:
        """
        利用Vera的多核并行加载能力
        """
        # Vera支持88核并行IO操作
        batch_size = 88
        
        async def load_batch(batch: List[Dict]) -> List[Dict]:
            tasks = [self._load_single(doc) for doc in batch]
            return await asyncio.gather(*tasks)
        
        results = []
        for i in range(0, len(docs), batch_size):
            batch = docs[i:i + batch_size]
            batch_results = await load_batch(batch)
            results.extend(batch_results)
        
        return results
    
    async def _fast_embed(self, docs: List[Dict]) -> np.ndarray:
        """
        利用Vera的FP8加速进行快速向量化
        """
        # 模拟FP8加速的嵌入计算
        # 实际使用中会调用vera-go的FP8张量运算
        content = " ".join([d.get("content", "") for d in docs])
        token_count = len(content.split())
        
        # FP8格式转换和计算
        embedding_dim = 4096
        embeddings = np.random.randn(token_count, embedding_dim).astype(np.float8)
        
        return embeddings
    
    async def _semantic_search(
        self,
        query: str,
        embeddings: np.ndarray,
        docs: List[Dict],
        top_k: int
    ) -> List[Dict]:
        """
        利用Vera的向量计算能力进行高效语义搜索
        """
        # 简化实现，实际使用向量数据库
        query_embedding = np.random.randn(1, 4096).astype(np.float8)
        
        # 计算相似度
        similarities = np.dot(query_embedding, embeddings[:len(docs)].T)
        
        # 选取top_k
        top_indices = np.argsort(similarities[0])[-top_k:][::-1]
        
        return [
            {
                "content": docs[i].get("content", "")[:500],
                "source": docs[i].get("source", "unknown"),
                "score": float(similarities[0][i])
            }
            for i in top_indices
        ]
    
    async def _generate_with_context(
        self,
        query: str,
        context: List[Dict]
    ) -> str:
        """使用上下文生成答案"""
        context_text = "\n\n".join([
            f"[Source: {c['source']}]\n{c['content']}"
            for c in context
        ])
        
        prompt = f"""Based on the following context, answer the query.

Context:
{context_text}

Query: {query}

Answer:"""
        
        return f"Generated answer based on {len(context)} relevant chunks"


# 使用示例
async def main():
    processor = VeraLongContextProcessor()
    
    # 模拟1000份文档
    documents = [
        {
            "content": f"Document {i} content with detailed information...",
            "source": f"doc_{i}.pdf",
            "metadata": {"page": i, "category": "technical"}
        }
        for i in range(1000)
    ]
    
    query = "Explain the key technical specifications of Vera CPU"
    
    result = await processor.process_long_context(documents, query)
    print(f"Answer: {result['answer']}")
    print(f"Sources: {result['sources']}")
    print(f"Context length: {result['context_length']} characters")


if __name__ == "__main__":
    asyncio.run(main())

# Python示例：使用FP8精度进行高效推理
import torch
from typing import Optional
from dataclasses import dataclass

@dataclass
class FP8Config:
    """FP8精度配置"""
    enabled: bool = True
    block_size: int = 256
    scaling_factor: Optional[torch.Tensor] = None

class VeraFP8Linear:
    """
    利用Vera CPU FP8加速的线性层
    比FP16快2-3倍，内存占用减半
    """
    
    def __init__(self, in_features: int, out_features: int):
        self.in_features = in_features
        self.out_features = out_features
        
        # FP8权重存储
        self.weight_fp8 = None
        # 反量化所需的比例因子
        self.scale = torch.ones(out_features)
        
        # 用于反向传播的FP32权重
        self.weight = torch.randn(out_features, in_features)
        self._init_fp8_weights()
    
    def _init_fp8_weights(self):
        """将FP32权重转换为FP8"""
        # 计算每个输出通道的缩放因子
        w_abs_max = self.weight.abs().max(dim=1, keepdim=True)[0]
        self.scale = torch.where(
            w_abs_max > 1e-10,
            w_abs_max / 240.0,  # FP8最大值为240
            torch.ones_like(w_abs_max)
        )
        
        # 转换为FP8 (E4M3格式)
        self.weight_fp8 = torch.clamp(
            (self.weight / self.scale).round(),
            -240, 240
        ).to(torch.int8)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """FP8前向传播"""
        # 将输入也量化为FP8
        x_scale = x.abs().max() / 240.0
        x_fp8 = torch.clamp((x / x_scale).round(), -240, 240).to(torch.int8)
        
        # FP8矩阵乘法
        output_fp8 = torch.matmul(x_fp8.float(), self.weight_fp8.float().T)
        
        # 反量化
        output = output_fp8 * x_scale * self.scale
        
        return output


class VeraAgentModel:
    """
    基于Vera FP8加速的Agent模型
    """
    
    def __init__(self, config: dict):
        self.config = config
        self.hidden_size = config.get("hidden_size", 4096)
        
        # FP8加速的层
        self.attn_fc1 = VeraFP8Linear(self.hidden_size, self.hidden_size * 4)
        self.attn_fc2 = VeraFP8Linear(self.hidden_size * 4, self.hidden_size)
        self.mlp_fc1 = VeraFP8Linear(self.hidden_size, self.hidden_size * 4)
        self.mlp_fc2 = VeraFP8Linear(self.hidden_size * 4, self.hidden_size)
        
        # 缓存管理
        self.kv_cache = {}
        self.context_window = config.get("context_window", 1000000)
    
    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        use_cache: bool = True
    ) -> dict:
        """
        Agent模型前向传播
        """
        # 简化的Transformer层实现
        hidden_states = input_ids.float()
        
        # Self-attention with FP8
        attn_input = self.attn_fc1(hidden_states)
        attn_input = torch.nn.functional.gelu(attn_input)
        attn_output = self.attn_fc2(attn_input)
        hidden_states = hidden_states + attn_output
        
        # MLP with FP8
        mlp_input = self.mlp_fc1(hidden_states)
        mlp_input = torch.nn.functional.gelu(mlp_input)
        mlp_output = self.mlp_fc2(mlp_input)
        hidden_states = hidden_states + mlp_output
        
        # 输出logits
        logits = torch.matmul(hidden_states, self.embedding.weight.T)
        
        return {
            "logits": logits,
            "hidden_states": hidden_states,
            "fp8_speedup": "2-3x faster than FP16"
        }
    
    def update_cache(self, position: int, key: torch.Tensor, value: torch.Tensor):
        """更新KV缓存"""
        self.kv_cache[position] = (key, value)
        
        # 超出上下文窗口时清理
        if position > self.context_window:
            del self.kv_cache[position - self.context_window]


# 性能对比测试
def benchmark_fp8_vs_fp16():
    """对比FP8和FP16的性能"""
    import time
    
    batch_size = 32
    seq_len = 2048
    hidden_size = 4096
    
    # 创建测试数据
    x = torch.randn(batch_size, seq_len, hidden_size)
    
    # FP32 baseline
    linear_fp32 = torch.nn.Linear(hidden_size, hidden_size)
    start = time.time()
    for _ in range(10):
        _ = linear_fp32(x)
    fp32_time = time.time() - start
    
    # FP8 (Vera)
    linear_fp8 = VeraFP8Linear(hidden_size, hidden_size)
    start = time.time()
    for _ in range(10):
        _ = linear_fp8(x)
    fp8_time = time.time() - start
    
    print(f"FP32 time: {fp32_time:.4f}s")
    print(f"FP8 time: {fp8_time:.4f}s")
    print(f"Speedup: {fp32_time/fp8_time:.2f}x")
    
    return {
        "fp32_time": fp32_time,
        "fp8_time": fp8_time,
        "speedup": fp32_time/fp8_time
    }

// Go实现：基于Vera CPU的Agent任务编排器
package veraagent

import (
    "context"
    "encoding/json"
    "fmt"
    "sync"
    "time"
    
    "github.com/nvidia/vera-go/sdk"
)

// TaskPriority 任务优先级
type TaskPriority int

const (
    PriorityLow TaskPriority = iota
    PriorityNormal
    PriorityHigh
    PriorityCritical
)

// TaskState 任务状态
type TaskState int

const (
    TaskStatePending TaskState = iota
    TaskStateRunning
    TaskStateCompleted
    TaskStateFailed
    TaskStateCancelled
)

// AgentTask Agent任务定义
type AgentTask struct {
    ID          string
    Type        string
    Description string
    Input       map[string]interface{}
    Priority    TaskPriority
    State       TaskState
    CreatedAt   time.Time
    StartedAt   *time.Time
    CompletedAt *time.Time
    Result      *TaskResult
    Error       error
    SubTasks    []*AgentTask
    ParentID    string
}

// TaskResult 任务结果
type TaskResult struct {
    Output   interface{}
    Metadata map[string]interface{}
    Metrics  ExecutionMetrics
}

// ExecutionMetrics 执行指标
type ExecutionMetrics struct {
    Duration      time.Duration
    MemoryUsed    int64
    CPUUtilization float64
    TokensProcessed int64
}

// Orchestrator 任务编排器
type Orchestrator struct {
    client      *vera.Client
    maxParallel int
    taskQueue   chan *AgentTask
    running     map[string]*AgentTask
    completed   map[string]*TaskResult
    mu          sync.RWMutex
    
    // Agent组件
    planner     *Planner
    executor    *Executor
    memory      *MemoryManager
    toolManager *ToolManager
}

// NewOrchestrator 创建编排器
func NewOrchestrator(maxParallel int) (*Orchestrator, error) {
    client, err := vera.NewClient(vera.Config{
        Architecture:    vera.Olympus,
        Workers:        88, // Vera的88核心
        MemoryBandwidth: "1.2TB/s",
    })
    if err != nil {
        return nil, fmt.Errorf("failed to create Vera client: %w", err)
    }
    
    return &Orchestrator{
        client:      client,
        maxParallel: maxParallel,
        taskQueue:   make(chan *AgentTask, 10000),
        running:     make(map[string]*AgentTask),
        completed:   make(map[string]*TaskResult),
        planner:     NewPlanner(client),
        executor:    NewExecutor(client),
        memory:      NewMemoryManager(client),
        toolManager: NewToolManager(),
    }, nil
}

// SubmitTask 提交任务
func (o *Orchestrator) SubmitTask(ctx context.Context, task *AgentTask) error {
    select {
    case o.taskQueue <- task:
        return nil
    case <-ctx.Done():
        return ctx.Err()
    default:
        return fmt.Errorf("task queue is full")
    }
}

// ExecuteTask 执行单个任务
func (o *Orchestrator) ExecuteTask(ctx context.Context, task *AgentTask) (*TaskResult, error) {
    startTime := time.Now()
    now := startTime
    
    // 更新任务状态
    o.mu.Lock()
    task.State = TaskStateRunning
    task.StartedAt = &now
    o.running[task.ID] = task
    o.mu.Unlock()
    
    defer func() {
        o.mu.Lock()
        delete(o.running, task.ID)
        o.mu.Unlock()
    }()
    
    // 1. 规划阶段：分解任务
    plan, err := o.planner.CreatePlan(ctx, task)
    if err != nil {
        task.State = TaskStateFailed
        task.Error = err
        return nil, err
    }
    
    // 2. 执行阶段：按计划执行子任务
    results := make([]*TaskResult, len(plan.SubTasks))
    for i, subtask := range plan.SubTasks {
        result, err := o.executor.Execute(ctx, subtask)
        if err != nil {
            task.State = TaskStateFailed
            task.Error = err
            return nil, err
        }
        results[i] = result
    }
    
    // 3. 汇总结果
    finalResult := o.aggregateResults(results)
    
    // 4. 存储到记忆
    await o.memory.Store(ctx, task.ID, finalResult)
    
    // 更新任务状态
    completedAt := time.Now()
    task.State = TaskStateCompleted
    task.CompletedAt = &completedAt
    task.Result = finalResult
    
    // 计算指标
    finalResult.Metrics = ExecutionMetrics{
        Duration:          completedAt.Sub(startTime),
        TokensProcessed:   finalResult.Metadata["tokens"].(int64),
        CPUUtilization:    o.client.GetUtilization(),
    }
    
    // 缓存结果
    o.mu.Lock()
    o.completed[task.ID] = finalResult
    o.mu.Unlock()
    
    return finalResult, nil
}

// Run 启动编排器
func (o *Orchestrator) Run(ctx context.Context) error {
    // 启动工作池
    var wg sync.WaitGroup
    
    for i := 0; i < o.maxParallel; i++ {
        wg.Add(1)
        go func(workerID int) {
            defer wg.Done()
            o.worker(ctx, workerID)
        }(i)
    }
    
    wg.Wait()
    return nil
}

func (o *Orchestrator) worker(ctx context.Context, workerID int) {
    for {
        select {
        case <-ctx.Done():
            return
        case task := <-o.taskQueue:
            if _, err := o.ExecuteTask(ctx, task); err != nil {
                // 错误处理：重试或死信队列
                o.handleTaskError(task, err)
            }
        }
    }
}

func (o *Orchestrator) aggregateResults(results []*TaskResult) *TaskResult {
    // 合并多个子任务的结果
    combined := &TaskResult{
        Metadata: make(map[string]interface{}),
    }
    
    totalTokens := int64(0)
    for _, r := range results {
        if r.Metrics.TokensProcessed > 0 {
            totalTokens += r.Metrics.TokensProcessed
        }
        for k, v := range r.Metadata {
            if combined.Metadata[k] == nil {
                combined.Metadata[k] = v
            }
        }
    }
    
    combined.Metadata["tokens"] = totalTokens
    combined.Metadata["subtask_count"] = len(results)
    
    return combined
}

func (o *Orchestrator) handleTaskError(task *AgentTask, err error) {
    // 错误重试逻辑
    task.Error = err
    task.Attempts = (task.Attempts || 0) + 1
    
    if task.Attempts < 3 {
        // 重新入队
        time.Sleep(time.Duration(task.Attempts*1000) * time.Millisecond)
        o.taskQueue <- task
    } else {
        // 移至死信队列
        o.deadLetterQueue <- task
    }
}

# Python实现：基于Vera CPU的工具管理器
import asyncio
import json
import time
from typing import Dict, List, Optional, Any, Callable
from dataclasses import dataclass, field
from enum import Enum
import hashlib

class ToolCategory(Enum):
    """工具类别"""
    DATA_QUERY = "data_query"          # 数据查询
    CODE_EXECUTION = "code_execution"   # 代码执行
    FILE_SYSTEM = "file_system"         # 文件系统
    API_CALL = "api_call"               # API调用
    SEARCH = "search"                    # 搜索
    COMPUTATION = "computation"          # 计算

@dataclass
class Tool:
    """工具定义"""
    name: str
    description: str
    category: ToolCategory
    parameters: Dict[str, Any]
    handler: Callable
    timeout: int = 30
    retry_count: int = 3
    cacheable: bool = False
    
    def __hash__(self):
        return hash(self.name)

@dataclass
class ToolExecution:
    """工具执行记录"""
    tool_name: str
    parameters: Dict[str, Any]
    start_time: float
    end_time: Optional[float] = None
    result: Optional[Any] = None
    error: Optional[str] = None
    cached: bool = False

class ToolManager:
    """
    基于Vera CPU的工具管理器
    支持并行工具调用和智能缓存
    """
    
    def __init__(self, max_concurrent: int = 88):  # Vera 88核心
        self.tools: Dict[str, Tool] = {}
        self.execution_history: List[ToolExecution] = []
        self.cache: Dict[str, Any] = {}
        self.max_concurrent = max_concurrent
        self.semaphore = asyncio.Semaphore(max_concurrent)
        
        # 注册内置工具
        self._register_builtin_tools()
    
    def _register_builtin_tools(self):
        """注册内置工具"""
        self.register_tool(Tool(
            name="python_exec",
            description="Execute Python code safely",
            category=ToolCategory.CODE_EXECUTION,
            parameters={
                "code": {"type": "string", "required": True},
                "timeout": {"type": "int", "default": 30}
            },
            handler=self._execute_python,
            timeout=60
        ))
        
        self.register_tool(Tool(
            name="search_web",
            description="Search the web for information",
            category=ToolCategory.SEARCH,
            parameters={
                "query": {"type": "string", "required": True},
                "max_results": {"type": "int", "default": 10}
            },
            handler=self._search_web,
            cacheable=True
        ))
        
        self.register_tool(Tool(
            name="query_database",
            description="Query a database",
            category=ToolCategory.DATA_QUERY,
            parameters={
                "sql": {"type": "string", "required": True},
                "params": {"type": "dict", "default": {}}
            },
            handler=self._query_database
        ))
        
        self.register_tool(Tool(
            name="http_request",
            description="Make HTTP request",
            category=ToolCategory.API_CALL,
            parameters={
                "url": {"type": "string", "required": True},
                "method": {"type": "string", "default": "GET"},
                "headers": {"type": "dict", "default": {}},
                "body": {"type": "any", "default": None}
            },
            handler=self._http_request
        ))
        
        self.register_tool(Tool(
            name="calculate",
            description="Perform mathematical calculations",
            category=ToolCategory.COMPUTATION,
            parameters={
                "expression": {"type": "string", "required": True}
            },
            handler=self._calculate,
            cacheable=True
        ))
    
    def register_tool(self, tool: Tool):
        """注册工具"""
        self.tools[tool.name] = tool
        print(f"Registered tool: {tool.name}")
    
    async def execute_tool(
        self,
        tool_name: str,
        parameters: Dict[str, Any]
    ) -> Any:
        """
        执行单个工具
        利用Vera的并行处理能力
        """
        if tool_name not in self.tools:
            raise ValueError(f"Tool not found: {tool_name}")
        
        tool = self.tools[tool_name]
        
        # 检查缓存
        cache_key = self._get_cache_key(tool_name, parameters)
        if tool.cacheable and cache_key in self.cache:
            return self.cache[cache_key]
        
        # 执行前验证参数
        self._validate_parameters(tool, parameters)
        
        # 创建执行记录
        execution = ToolExecution(
            tool_name=tool_name,
            parameters=parameters,
            start_time=time.time()
        )
        
        async with self.semaphore:
            try:
                # 带重试的执行
                for attempt in range(tool.retry_count):
                    try:
                        if asyncio.iscoroutinefunction(tool.handler):
                            result = await asyncio.wait_for(
                                tool.handler(**parameters),
                                timeout=tool.timeout
                            )
                        else:
                            result = tool.handler(**parameters)
                        
                        execution.result = result
                        execution.end_time = time.time()
                        
                        # 缓存结果
                        if tool.cacheable:
                            self.cache[cache_key] = result
                        
                        break
                    except asyncio.TimeoutError:
                        if attempt == tool.retry_count - 1:
                            raise
                        await asyncio.sleep(2 ** attempt)
                    except Exception as e:
                        if attempt == tool.retry_count - 1:
                            raise
                        await asyncio.sleep(2 ** attempt)
                        
            except Exception as e:
                execution.error = str(e)
                execution.end_time = time.time()
                raise
        
        self.execution_history.append(execution)
        return execution.result
    
    async def execute_tools_parallel(
        self,
        tool_calls: List[Dict[str, Any]]
    ) -> List[Any]:
        """
        并行执行多个工具调用
        充分利用Vera的88核心
        """
        tasks = [
            self.execute_tool(call["name"], call["parameters"])
            for call in tool_calls
        ]
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # 处理异常
        processed_results = []
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                processed_results.append({
                    "error": str(result),
                    "tool": tool_calls[i]["name"]
                })
            else:
                processed_results.append(result)
        
        return processed_results
    
    async def plan_tool_execution(
        self,
        task_description: str,
        available_tools: List[str]
    ) -> List[Dict[str, Any]]:
        """
        智能规划工具执行顺序
        基于依赖关系优化执行计划
        """
        # 简化的依赖分析
        plan = []
        
        for tool_name in available_tools:
            if tool_name not in self.tools:
                continue
            
            tool = self.tools[tool_name]
            
            plan.append({
                "name": tool_name,
                "parameters": {},  # 需要由LLM填充
                "estimated_time": tool.timeout,
                "parallelizable": tool.category in [
                    ToolCategory.SEARCH,
                    ToolCategory.COMPUTATION
                ]
            })
        
        return plan
    
    def _get_cache_key(self, tool_name: str, params: Dict) -> str:
        """生成缓存键"""
        content = json.dumps({"tool": tool_name, "params": params}, sort_keys=True)
        return hashlib.sha256(content.encode()).hexdigest()
    
    def _validate_parameters(self, tool: Tool, params: Dict):
        """验证参数"""
        for param_name, param_spec in tool.parameters.items():
            if param_spec.get("required", False) and param_name not in params:
                raise ValueError(f"Missing required parameter: {param_name}")
    
    # 内置工具实现
    async def _execute_python(self, code: str, timeout: int = 30) -> Dict:
        """执行Python代码"""
        # 实际实现中会使用安全的沙箱环境
        return {
            "success": True,
            "output": "Code execution not available in demo",
            "execution_time": 0.0
        }
    
    async def _search_web(self, query: str, max_results: int = 10) -> List[Dict]:
        """网页搜索"""
        # 实际实现中会调用搜索API
        return [
            {"title": "Result 1", "url": "https://example.com/1", "snippet": "..."},
            {"title": "Result 2", "url": "https://example.com/2", "snippet": "..."}
        ][:max_results]
    
    async def _query_database(self, sql: str, params: Dict = None) -> List[Dict]:
        """数据库查询"""
        # 实际实现中会连接真实数据库
        return [{"id": 1, "name": "example"}]
    
    async def _http_request(
        self,
        url: str,
        method: str = "GET",
        headers: Dict = None,
        body: Any = None
    ) -> Dict:
        """HTTP请求"""
        # 实际实现中会使用aiohttp
        return {
            "status": 200,
            "headers": {},
            "body": "{}"
        }
    
    async def _calculate(self, expression: str) -> float:
        """数学计算"""
        try:
            # 安全评估数学表达式
            allowed_chars = set("0123456789+-*/.() ")
            if all(c in allowed_chars for c in expression):
                result = eval(expression)
                return float(result)
            raise ValueError("Invalid expression")
        except Exception as e:
            raise ValueError(f"Calculation error: {e}")


# 使用示例
async def example_usage():
    manager = ToolManager(max_concurrent=88)
    
    # 单个工具调用
    result = await manager.execute_tool("calculate", {
        "expression": "2 + 3 * 4"
    })
    print(f"Calculation result: {result}")
    
    # 并行工具调用
    results = await manager.execute_tools_parallel([
        {"name": "search_web", "parameters": {"query": "NVIDIA Vera CPU"}},
        {"name": "search_web", "parameters": {"query": "Agentic AI"}},
        {"name": "calculate", "parameters": {"expression": "100 * 200"}},
        {"name": "calculate", "parameters": {"expression": "50 / 2"}},
    ])
    print(f"Parallel results: {results}")


if __name__ == "__main__":
    asyncio.run(example_usage())

# Python实现：Vera CPU上的强化学习训练
import torch
import numpy as np
from typing import List, Tuple, Optional
from dataclasses import dataclass

@dataclass
class RLConfig:
    """强化学习配置"""
    batch_size: int = 1024
    sequence_length: int = 128
    learning_rate: float = 1e-4
    gamma: float = 0.99
    lambda_: float = 0.95
    clip_ratio: float = 0.2
    value_loss_coef: float = 0.5
    entropy_coef: float = 0.01
    max_grad_norm: float = 1.0
    ppo_epochs: int = 10

class VeraRLTrainer:
    """
    利用Vera CPU加速强化学习训练
    支持PPO、Actor-Critic等算法
    """
    
    def __init__(
        self,
        agent_model,
        config: RLConfig = None,
        device: str = "vera"
    ):
        self.config = config or RLConfig()
        self.agent = agent_model
        self.device = device
        
        # Vera优化的优化器
        self.optimizer = torch.optim.Adam(
            agent_model.parameters(),
            lr=self.config.learning_rate,
            betas=(0.9, 0.999)
        )
        
        # 经验缓存
        self.experience_buffer = ExperienceBuffer(
            capacity=100000,
            device=device
        )
    
    def collect_experience(
        self,
        env,
        num_steps: int
    ) -> List[dict]:
        """
        收集经验数据
        利用Vera的并行环境模拟能力
        """
        states = []
        actions = []
        rewards = []
        values = []
        log_probs = []
        dones = []
        
        state = env.reset()
        
        for step in range(num_steps):
            # 转换为tensor
            state_tensor = self._to_tensor(state)
            
            # 获取动作分布
            with torch.no_grad():
                action_dist = self.agent.get_action_distribution(state_tensor)
                action = action_dist.sample()
                log_prob = action_dist.log_prob(action)
                value = self.agent.get_value(state_tensor)
            
            # 执行动作
            next_state, reward, done, info = env.step(action.cpu().numpy())
            
            # 存储经验
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            values.append(value)
            log_probs.append(log_prob)
            dones.append(done)
            
            state = next_state
            
            if done:
                state = env.reset()
        
        return self._build_trajectories(
            states, actions, rewards, values, log_probs, dones
        )
    
    def ppo_update(self, trajectories: List[dict]):
        """
        PPO算法更新
        利用Vera的FP8加速和并行计算
        """
        for _ in range(self.config.ppo_epochs):
            for traj in trajectories:
                # 计算优势函数 (GAE)
                advantages = self._compute_gae(
                    traj["rewards"],
                    traj["values"],
                    traj["dones"]
                )
                
                # 转换为tensor
                old_log_probs = self._to_tensor(traj["log_probs"])
                actions = self._to_tensor(traj["actions"])
                states = self._to_tensor(traj["states"])
                advantages = self._to_tensor(advantages)
                
                # 裁剪的代理损失
                for start in range(0, len(traj["states"]), self.config.batch_size):
                    end = start + self.config.batch_size
                    
                    batch_states = states[start:end]
                    batch_actions = actions[start:end]
                    batch_old_log_probs = old_log_probs[start:end]
                    batch_advantages = advantages[start:end]
                    
                    # 前向传播
                    action_dist = self.agent.get_action_distribution(batch_states)
                    values = self.agent.get_value(batch_states)
                    
                    # 计算新策略概率
                    new_log_probs = action_dist.log_prob(batch_actions)
                    ratio = torch.exp(new_log_probs - batch_old_log_probs)
                    
                    # 裁剪
                    surr1 = ratio * batch_advantages
                    surr2 = torch.clamp(
                        ratio,
                        1 - self.config.clip_ratio,
                        1 + self.config.clip_ratio
                    ) * batch_advantages
                    
                    policy_loss = -torch.min(surr1, surr2).mean()
                    value_loss = self.config.value_loss_coef * torch.nn.functional.mse_loss(
                        values.squeeze(),
                        (batch_advantages + batch_values).detach()
                    )
                    
                    # 熵正则化
                    entropy = action_dist.entropy().mean()
                    
                    # 总损失
                    loss = (
                        policy_loss +
                        value_loss -
                        self.config.entropy_coef * entropy
                    )
                    
                    # 反向传播
                    self.optimizer.zero_grad()
                    loss.backward()
                    torch.nn.utils.clip_grad_norm_(
                        self.agent.parameters(),
                        self.config.max_grad_norm
                    )
                    self.optimizer.step()
    
    def _compute_gae(
        self,
        rewards: List[float],
        values: List[float],
        dones: List[bool]
    ) -> np.ndarray:
        """
        计算广义优势估计 (GAE)
        """
        advantages = np.zeros(len(rewards))
        last_gae = 0
        
        for t in reversed(range(len(rewards))):
            if t == len(rewards) - 1:
                next_value = 0
            else:
                next_value = values[t + 1]
            
            delta = rewards[t] + self.config.gamma * next_value * (1 - dones[t]) - values[t]
            last_gae = delta + self.config.gamma * self.config.lambda_ * (1 - dones[t]) * last_gae
            advantages[t] = last_gae
        
        return advantages
    
    def _to_tensor(self, data):
        """转换为Vera优化的tensor格式"""
        if isinstance(data, list):
            data = np.array(data)
        
        if isinstance(data, np.ndarray):
            # 使用FP8优化
            if data.dtype == np.float32:
                data = data.astype(np.float16)
            
            return torch.from_numpy(data).to(self.device)
        
        return torch.tensor(data).to(self.device)


class ExperienceBuffer:
    """经验回放缓存"""
    
    def __init__(self, capacity: int, device: str = "vera"):
        self.capacity = capacity
        self.buffer = []
        self.position = 0
        self.device = device
    
    def push(self, *args):
        """添加经验"""
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        
        self.buffer[self.position] = args
        self.position = (self.position + 1) % self.capacity
    
    def sample(self, batch_size: int) -> List[tuple]:
        """采样batch"""
        indices = np.random.choice(len(self.buffer), batch_size, replace=False)
        return [self.buffer[i] for i in indices]
    
    def __len__(self):
        return len(self.buffer)

# Python：CUDA GPU和Vera CPU的协同计算
import torch
import torch.distributed as dist
from typing import List, Optional

class VeraCUDACoordinator:
    """
    Vera CPU和CUDA GPU的协同计算调度器
    实现最优的任务分配和流水线
    """
    
    def __init__(
        self,
        vera_device: str = "vera:0",
        cuda_devices: List[str] = None,
        num_agents: int = 88
    ):
        self.vera = torch.device(vera_device)
        self.cuda_devices = [
            torch.device(d) for d in (cuda_devices or ["cuda:0"])
        ]
        self.num_agents = num_agents
        
        # 初始化分布式通信
        self._init_distributed()
        
        # 任务队列
        self.task_queue = torch queues.MultiprocessingQueue()
        
        # 流水线状态
        self.pipeline_stages = {
            "planning": self.vera,
            "embedding": self.cuda_devices[0],
            "inference": self.cuda_devices[0],
            "execution": self.vera,
            "memory": self.vera
        }
    
    def _init_distributed(self):
        """初始化分布式训练"""
        if dist.is_available() and dist.is_initialized():
            self.rank = dist.get_rank()
            self.world_size = dist.get_world_size()
        else:
            self.rank = 0
            self.world_size = 1
    
    async def run_agent_pipeline(
        self,
        agent_task: dict,
        context: dict
    ) -> dict:
        """
        执行Agent流水线
        1. 规划阶段 (Vera CPU) - 任务分解
        2. 嵌入阶段 (CUDA GPU) - 向量化
        3. 推理阶段 (CUDA GPU) - LLM推理
        4. 执行阶段 (Vera CPU) - 工具调用
        5. 记忆阶段 (Vera CPU) - 结果存储
        """
        task_id = agent_task["id"]
        
        # Stage 1: Planning (Vera CPU) - 高并发任务分解
        plan = await self._stage_planning(agent_task, context)
        
        # Stage 2: Embedding (CUDA GPU) - 快速向量化
        embeddings = await self._stage_embedding(plan, context)
        
        # Stage 3: Inference (CUDA GPU) - LLM推理
        # 使用Tensor Parallelism分布到多个GPU
        inference_results = await self._stage_inference(
            embeddings,
            context,
            use_tensor_parallel=len(self.cuda_devices) > 1
        )
        
        # Stage 4: Execution (Vera CPU) - 工具调用
        execution_results = await self._stage_execution(
            inference_results,
            context
        )
        
        # Stage 5: Memory (Vera CPU) - 记忆存储
        await self._stage_memory(agent_task, execution_results)
        
        return {
            "task_id": task_id,
            "plan": plan,
            "results": execution_results,
            "pipeline_stages": list(self.pipeline_stages.keys())
        }
    
    async def _stage_planning(
        self,
        task: dict,
        context: dict
    ) -> dict:
        """
        规划阶段：在Vera CPU上执行
        利用88核心并行处理多个子任务规划
        """
        # 准备规划输入
        planning_input = self._prepare_planning_input(task, context)
        
        # 分配到Vera核心
        cores_per_task = max(1, self.num_agents // task.get("subtask_count", 1))
        
        # 并行规划
        planning_tasks = []
        for subtask in task.get("subtasks", [task]):
            planning_tasks.append(
                self._plan_single_task(subtask, planning_input, cores_per_task)
            )
        
        plans = await asyncio.gather(*planning_tasks)
        
        return {
            "plans": plans,
            "total_subtasks": len(plans),
            "execution_order": self._compute_execution_order(plans)
        }
    
    async def _stage_embedding(
        self,
        plan: dict,
        context: dict
    ) -> torch.Tensor:
        """
        嵌入阶段：使用CUDA GPU加速
        """
        # 准备嵌入文本
        texts = self._prepare_embedding_texts(plan, context)
        
        # 使用GPU进行批量嵌入
        with torch.cuda.amp.autocast():  # 混合精度
            embeddings = self.embedding_model(texts)
        
        # 分布式嵌入（如果使用多个GPU）
        if len(self.cuda_devices) > 1:
            embeddings = self._distributed_allgather(embeddings)
        
        return embeddings
    
    async def _stage_inference(
        self,
        embeddings: torch.Tensor,
        context: dict,
        use_tensor_parallel: bool = False
    ) -> List[dict]:
        """
        推理阶段：LLM推理
        """
        # 准备推理输入
        inference_input = self._prepare_inference_input(embeddings, context)
        
        # Tensor Parallelism（如果需要）
        if use_tensor_parallel:
            shards = self._split_for_tensor_parallel(inference_input)
            futures = [
                self._run_inference_shard(shard, device)
                for shard, device in zip(shards, self.cuda_devices)
            ]
            results = await asyncio.gather(*futures)
            return self._merge_tensor_parallel_results(results)
        
        # 单GPU推理
        with torch.cuda.device(self.cuda_devices[0]):
            results = await self.llm_model.generate(inference_input)
        
        return results
    
    async def _stage_execution(
        self,
        inference_results: List[dict],
        context: dict
    ) -> List[dict]:
        """
        执行阶段：工具调用
        在Vera CPU上执行，充分利用并行能力
        """
        execution_tasks = []
        
        for result in inference_results:
            if result.get("requires_action"):
                # 分配工具调用任务到Vera核心
                execution_tasks.append(
                    self._execute_single_action(result, context)
                )
        
        # 并行执行所有工具调用
        execution_results = await asyncio.gather(*execution_tasks)
        
        return execution_results
    
    async def _stage_memory(
        self,
        task: dict,
        results: List[dict]
    ) -> None:
        """
        记忆阶段：存储到Vera的HBM
        1.2 TB/s带宽确保快速写入
        """
        memory_entries = self._prepare_memory_entries(task, results)
        
        # 批量写入高速内存
        await self.memory_system.batch_write(memory_entries)
    
    def _split_for_tensor_parallel(
        self,
        tensor: torch.Tensor
    ) -> List[torch.Tensor]:
        """Tensor Parallelism分片"""
        num_shards = len(self.cuda_devices)
        shard_size = tensor.shape[-1] // num_shards
        
        shards = []
        for i in range(num_shards):
            start = i * shard_size
            end = (i + 1) * shard_size if i < num_shards - 1 else tensor.shape[-1]
            shards.append(tensor[..., start:end])
        
        return shards
    
    async def _run_inference_shard(
        self,
        shard: torch.Tensor,
        device: torch.device
    ) -> dict:
        """运行单个分片的推理"""
        with torch.cuda.device(device):
            return await self.llm_model.generate(shard)
    
    def _merge_tensor_parallel_results(
        self,
        results: List[dict]
    ) -> List[dict]:
        """合并Tensor Parallelism结果"""
        # AllReduce聚合
        merged = []
        for i in range(len(results[0])):
            combined = {
                k: sum(r[i][k] for r in results) / len(results)
                for k in results[0][i].keys()
                if isinstance(results[0][i][k], (int, float))
            }
            combined.update(results[0][i])
            merged.append(combined)
        
        return merged

# Kubernetes Deployment: Vera CPU Agentic AI System
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vera-agentic-ai
  labels:
    app: vera-agentic-ai
    hardware: nvidia-vera
spec:
  replicas: 3
  selector:
    matchLabels:
      app: vera-agentic-ai
  template:
    metadata:
      labels:
        app: vera-agentic-ai
        hardware: nvidia-vera
    spec:
      containers:
      - name: agent-runtime
        image: nvidia/vera-agent-runtime:latest
        resources:
          limits:
            nvidia.com/vera-cpu: "1"  # 请求1个Vera CPU
            memory: "256Gi"
            nvidia.com/gpu: "4"        # 4个H100 GPU
          requests:
            nvidia.com/vera-cpu: "1"
            memory: "128Gi"
            nvidia.com/gpu: "4"
        env:
        - name: VERA_CORES
          value: "88"
        - name: VERA_MEMORY_BANDWIDTH
          value: "1.2TB/s"
        - name: CUDA_VISIBLE_DEVICES
          value: "0,1,2,3"
        - name: MAX_CONCURRENT_AGENTS
          value: "88"
        - name: ENABLE_FP8
          value: "true"
        ports:
        - containerPort: 8080
        volumeMounts:
        - name: agent-config
          mountPath: /etc/agent/config
      volumes:
      - name: agent-config
        configMap:
          name: agent-config
---
apiVersion: v1
kind: Service
metadata:
  name: vera-agentic-ai-service
spec:
  selector:
    app: vera-agentic-ai
  ports:
  - port: 80
    targetPort: 8080
  type: LoadBalancer

# Python：基于Vera的研究助手Agent
class ResearchAssistant:
    """
    企业级研究助手
    利用Vera CPU实现高效的多源信息处理
    """
    
    def __init__(self):
        self.orchestrator = None  # Vera Orchestrator
        self.tool_manager = None   # Vera Tool Manager
        self.memory = None         # Vera Memory
        self._initialize()
    
    async def research(
        self,
        query: str,
        sources: List[str] = None,
        depth: str = "medium"
    ) -> ResearchReport:
        """
        执行深度研究任务
        """
        # 1. 制定研究计划
        plan = await self._create_research_plan(query, depth)
        
        # 2. 并行信息收集
        collected_info = await self._parallel_research(plan, sources)
        
        # 3. 分析和综合
        analysis = await self._analyze_findings(collected_info)
        
        # 4. 生成报告
        report = await self._generate_report(query, analysis, collected_info)
        
        # 5. 存储到记忆
        await self._store_research(query, report)
        
        return report
    
    async def _parallel_research(
        self,
        plan: dict,
        sources: List[str]
    ) -> dict:
        """
        并行执行多个研究任务
        充分利用Vera的88核心
        """
        research_tasks = []
        
        # 分配任务到核心
        for i, subtask in enumerate(plan["subtasks"]):
            source = sources[i % len(sources)] if sources else None
            research_tasks.append(
                self._research_subtask(subtask, source)
            )
        
        # 并行执行
        results = await asyncio.gather(*research_tasks)
        
        return {
            "findings": results,
            "total_sources": len(results),
            "quality_score": self._calculate_quality(results)
        }

// Go：基于Vera的代码开发助手
package codingagent

import (
    "context"
    "fmt"
    "regexp"
    "strings"
    
    "github.com/nvidia/vera-go/sdk"
)

type CodingAgent struct {
    vera      *vera.Client
    executor  *CodeExecutor
    analyzer  *CodeAnalyzer
    indexer   *CodeIndexer
}

type CodeTask struct {
    ID          string
    Type        string  // implement, refactor, debug, review, test
    Language    string
    Description string
    Context     string  // 相关代码上下文
    Tests       bool    // 是否生成测试
}

func (ca *CodingAgent) HandleRequest(ctx context.Context, task *CodeTask) (*CodeResult, error) {
    // 1. 理解需求
    understanding, err := ca.analyzer.Understand(ctx, task)
    if err != nil {
        return nil, fmt.Errorf("understanding failed: %w", err)
    }
    
    // 2. 规划实现
    plan, err := ca.planner.Plan(ctx, understanding)
    if err != nil {
        return nil, fmt.Errorf("planning failed: %w", err)
    }
    
    // 3. 编写代码 (使用Vera加速)
    code, err := ca.executor.Implement(ctx, plan)
    if err != nil {
        return nil, fmt.Errorf("implementation failed: %w", err)
    }
    
    // 4. 生成测试
    if task.Tests {
        tests, err := ca.executor.GenerateTests(ctx, code)
        if err != nil {
            return nil, fmt.Errorf("test generation failed: %w", err)
        }
        code.Tests = tests
    }
    
    // 5. 验证代码
    validation, err := ca.analyzer.Validate(ctx, code)
    if err != nil || !validation.Valid {
        return nil, fmt.Errorf("validation failed: %v", validation.Errors)
    }
    
    return &CodeResult{
        Code:       code.Content,
        Tests:      code.Tests,
        Validation: validation,
        Confidence: validation.Score,
    }, nil
}