什么是 Tool Use

什么是 Transformer？

核心创新

自注意力代码

import torch, math

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.d_model, self.num_heads = d_model, num_heads
        self.head_dim = d_model // num_heads
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)
    
    def forward(self, Q, K, V, mask=None):
        bs = Q.size(0)
        Q = self.W_q(Q).view(bs, -1, self.num_heads, self.head_dim).transpose(1, 2)
        K = self.W_k(K).view(bs, -1, self.num_heads, self.head_dim).transpose(1, 2)
        V = self.W_v(V).view(bs, -1, self.num_heads, self.head_dim).transpose(1, 2)
        scores = torch.matmul(Q, K.transpose(-2,-1)) / math.sqrt(self.head_dim)
        if mask: scores = scores.masked_fill(mask == 0, -1e9)
        attn = torch.softmax(scores, dim=-1)
        context = torch.matmul(attn, V)
        context = context.transpose(1,2).contiguous().view(bs, -1, self.d_model)
        return self.W_o(context)

为什么成功？

1. 可并行化训练

2. 长距离依赖直接建模

3. 高度可扩展

4. 通用性强

什么是 Tool Use

1. Tool Use 的工作原理

2. 工具类型

3. Claude Tool Use 详解

// Claude Tool Use 工具定义示例
{
  name: "search_web",
  description: "在网络上搜索信息",
  input_schema: {
    type: "object",
    properties: {
      query: {
        type: "string",
        description: "搜索查询关键词"
      },
      num_results: {
        type: "integer",
        description: "返回结果数量",
        minimum: 1,
        maximum: 10,
        default: 5
      }
    },
    required: ["query"]
  }
}

// Claude 的响应格式
{
  tool_use: {
    id: "tool_123",
    name: "search_web",
    input: {
      query: "最新 AI 新闻",
      num_results: 5
    }
  }
}

4. 工具描述的重要性

5. Tool Use 的应用场景

6. Tool Use vs MCP

7. Tool Use 的最佳实践

8. Tool Use 的挑战与局限

9. Tool Use 的未来发展

总结