c5e6d42163
- Multi-account pool with round-robin load balancing - Auto token refresh for IAM IdC and Social auth - Streaming support (SSE) - Web admin panel with account management - Docker support with GitHub Actions CI/CD - Machine ID management per account - Usage tracking (requests, tokens, credits)
812 lines
22 KiB
Go
812 lines
22 KiB
Go
package proxy
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import (
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"encoding/base64"
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"encoding/json"
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"regexp"
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"strings"
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"time"
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"github.com/google/uuid"
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)
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// 模型映射
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var modelMap = map[string]string{
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"claude-sonnet-4-5": "claude-sonnet-4.5",
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"claude-sonnet-4.5": "claude-sonnet-4.5",
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"claude-haiku-4-5": "claude-haiku-4.5",
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"claude-haiku-4.5": "claude-haiku-4.5",
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"claude-opus-4-5": "claude-opus-4.5",
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"claude-opus-4.5": "claude-opus-4.5",
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"claude-sonnet-4": "claude-sonnet-4",
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"claude-sonnet-4-20250514": "claude-sonnet-4",
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"claude-3-5-sonnet": "claude-sonnet-4.5",
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"claude-3-opus": "claude-sonnet-4.5",
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"claude-3-sonnet": "claude-sonnet-4",
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"claude-3-haiku": "claude-haiku-4.5",
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"gpt-4": "claude-sonnet-4.5",
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"gpt-4o": "claude-sonnet-4.5",
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"gpt-4-turbo": "claude-sonnet-4.5",
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"gpt-3.5-turbo": "claude-sonnet-4.5",
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}
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func MapModel(model string) string {
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lower := strings.ToLower(model)
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for k, v := range modelMap {
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if strings.Contains(lower, k) {
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return v
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}
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}
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// 如果已经是有效的 Kiro 模型，直接返回
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if strings.HasPrefix(lower, "claude-") {
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return model
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}
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return "claude-sonnet-4.5"
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}
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// ==================== Claude API 类型 ====================
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type ClaudeRequest struct {
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Model string `json:"model"`
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Messages []ClaudeMessage `json:"messages"`
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MaxTokens int `json:"max_tokens"`
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Temperature float64 `json:"temperature,omitempty"`
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TopP float64 `json:"top_p,omitempty"`
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Stream bool `json:"stream,omitempty"`
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System interface{} `json:"system,omitempty"` // string or []SystemBlock
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Tools []ClaudeTool `json:"tools,omitempty"`
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ToolChoice interface{} `json:"tool_choice,omitempty"`
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}
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type ClaudeMessage struct {
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Role string `json:"role"`
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Content interface{} `json:"content"` // string or []ContentBlock
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}
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type ClaudeContentBlock struct {
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Type string `json:"type"`
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Text string `json:"text,omitempty"`
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ID string `json:"id,omitempty"`
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Name string `json:"name,omitempty"`
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Input interface{} `json:"input,omitempty"`
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ToolUseID string `json:"tool_use_id,omitempty"`
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Content interface{} `json:"content,omitempty"` // for tool_result
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Source *ImageSource `json:"source,omitempty"`
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}
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type ImageSource struct {
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Type string `json:"type"`
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MediaType string `json:"media_type"`
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Data string `json:"data"`
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}
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type ClaudeTool struct {
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Name string `json:"name"`
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Description string `json:"description"`
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InputSchema interface{} `json:"input_schema"`
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}
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type ClaudeResponse struct {
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ID string `json:"id"`
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Type string `json:"type"`
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Role string `json:"role"`
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Content []ClaudeContentBlock `json:"content"`
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Model string `json:"model"`
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StopReason string `json:"stop_reason"`
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StopSequence *string `json:"stop_sequence"`
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Usage ClaudeUsage `json:"usage"`
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}
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type ClaudeUsage struct {
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InputTokens int `json:"input_tokens"`
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OutputTokens int `json:"output_tokens"`
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}
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// ==================== Claude -> Kiro 转换 ====================
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const maxToolDescLen = 10237
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func ClaudeToKiro(req *ClaudeRequest) *KiroPayload {
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modelID := MapModel(req.Model)
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origin := "AI_EDITOR"
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// 提取系统提示
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systemPrompt := extractSystemPrompt(req.System)
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// 注入时间戳
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timestamp := time.Now().Format(time.RFC3339)
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systemPrompt = "[Context: Current time is " + timestamp + "]\n\n" + systemPrompt
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// 注入执行导向指令（防止 AI 在探索过程中丢失目标）
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executionDirective := `
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<execution_discipline>
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当用户要求执行特定任务时，你必须遵循以下纪律：
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1. **目标锁定**：在整个会话中始终牢记用户的原始目标，不要在代码探索过程中迷失方向
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2. **行动优先**：优先执行任务而非仅分析或总结，除非用户明确只要求分析
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3. **计划执行**：为任务创建明确的步骤计划，逐步执行并标记完成状态
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4. **禁止确认性收尾**：在任务未完成前，禁止输出"需要我继续吗？"、"需要深入分析吗？"等确认性问题
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5. **持续推进**：如果发现部分任务已完成，立即继续执行剩余未完成的任务
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6. **完整交付**：直到所有任务步骤都执行完毕才算完成
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</execution_discipline>
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`
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systemPrompt = systemPrompt + "\n\n" + executionDirective
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// 构建历史消息
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history := make([]KiroHistoryMessage, 0)
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var currentContent string
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var currentImages []KiroImage
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var currentToolResults []KiroToolResult
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for i, msg := range req.Messages {
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isLast := i == len(req.Messages)-1
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if msg.Role == "user" {
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content, images, toolResults := extractClaudeUserContent(msg.Content)
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if isLast {
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currentContent = content
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currentImages = images
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currentToolResults = toolResults
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} else {
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userMsg := KiroUserInputMessage{
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Content: content,
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ModelID: modelID,
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Origin: origin,
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}
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if len(images) > 0 {
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userMsg.Images = images
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}
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if len(toolResults) > 0 {
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userMsg.UserInputMessageContext = &UserInputMessageContext{
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ToolResults: toolResults,
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}
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}
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history = append(history, KiroHistoryMessage{
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UserInputMessage: &userMsg,
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})
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}
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} else if msg.Role == "assistant" {
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content, toolUses := extractClaudeAssistantContent(msg.Content)
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history = append(history, KiroHistoryMessage{
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AssistantResponseMessage: &KiroAssistantResponseMessage{
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Content: content,
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ToolUses: toolUses,
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},
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})
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}
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}
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// 确保 history 以 user 开始
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if len(history) > 0 && history[0].AssistantResponseMessage != nil {
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history = append([]KiroHistoryMessage{{
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UserInputMessage: &KiroUserInputMessage{
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Content: "Begin conversation",
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ModelID: modelID,
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Origin: origin,
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},
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}}, history...)
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}
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// 构建最终内容
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finalContent := ""
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if systemPrompt != "" {
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finalContent = "--- SYSTEM PROMPT ---\n" + systemPrompt + "\n--- END SYSTEM PROMPT ---\n\n"
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}
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if currentContent != "" {
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finalContent += currentContent
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} else if len(currentToolResults) > 0 {
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finalContent += "Tool results provided."
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} else {
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finalContent += "Continue"
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}
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// 转换工具
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kiroTools := convertClaudeTools(req.Tools)
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// 构建 payload
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payload := &KiroPayload{}
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payload.ConversationState.ChatTriggerType = "MANUAL"
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payload.ConversationState.ConversationID = uuid.New().String()
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payload.ConversationState.CurrentMessage.UserInputMessage = KiroUserInputMessage{
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Content: finalContent,
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ModelID: modelID,
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Origin: origin,
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Images: currentImages,
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}
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if len(kiroTools) > 0 || len(currentToolResults) > 0 {
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payload.ConversationState.CurrentMessage.UserInputMessage.UserInputMessageContext = &UserInputMessageContext{
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Tools: kiroTools,
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ToolResults: currentToolResults,
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}
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}
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if len(history) > 0 {
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payload.ConversationState.History = history
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}
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if req.MaxTokens > 0 || req.Temperature > 0 || req.TopP > 0 {
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payload.InferenceConfig = &InferenceConfig{
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MaxTokens: req.MaxTokens,
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Temperature: req.Temperature,
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TopP: req.TopP,
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}
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}
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return payload
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}
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func extractSystemPrompt(system interface{}) string {
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if system == nil {
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return ""
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}
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if s, ok := system.(string); ok {
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return s
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}
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if blocks, ok := system.([]interface{}); ok {
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var parts []string
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for _, b := range blocks {
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if block, ok := b.(map[string]interface{}); ok {
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if text, ok := block["text"].(string); ok {
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parts = append(parts, text)
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}
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}
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}
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return strings.Join(parts, "\n")
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}
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return ""
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}
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func extractClaudeUserContent(content interface{}) (string, []KiroImage, []KiroToolResult) {
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var text string
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var images []KiroImage
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var toolResults []KiroToolResult
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if s, ok := content.(string); ok {
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return s, nil, nil
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}
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if blocks, ok := content.([]interface{}); ok {
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for _, b := range blocks {
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block, ok := b.(map[string]interface{})
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if !ok {
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continue
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}
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blockType, _ := block["type"].(string)
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switch blockType {
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case "text":
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if t, ok := block["text"].(string); ok {
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text += t
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}
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case "image":
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if source, ok := block["source"].(map[string]interface{}); ok {
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mediaType, _ := source["media_type"].(string)
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data, _ := source["data"].(string)
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format := strings.TrimPrefix(mediaType, "image/")
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if format == "jpg" {
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format = "jpeg"
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}
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images = append(images, KiroImage{
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Format: format,
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Source: struct {
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Bytes string `json:"bytes"`
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}{Bytes: data},
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})
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}
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case "tool_result":
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toolUseID, _ := block["tool_use_id"].(string)
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resultContent := extractToolResultContent(block["content"])
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toolResults = append(toolResults, KiroToolResult{
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ToolUseID: toolUseID,
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Content: []KiroResultContent{{Text: resultContent}},
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Status: "success",
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})
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}
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}
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}
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return text, images, toolResults
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}
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func extractToolResultContent(content interface{}) string {
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if s, ok := content.(string); ok {
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return s
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}
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if blocks, ok := content.([]interface{}); ok {
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var parts []string
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for _, b := range blocks {
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if block, ok := b.(map[string]interface{}); ok {
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if text, ok := block["text"].(string); ok {
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parts = append(parts, text)
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}
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}
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}
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return strings.Join(parts, "")
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}
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return ""
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}
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func extractClaudeAssistantContent(content interface{}) (string, []KiroToolUse) {
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var text string
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var toolUses []KiroToolUse
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if s, ok := content.(string); ok {
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return s, nil
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}
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if blocks, ok := content.([]interface{}); ok {
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for _, b := range blocks {
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block, ok := b.(map[string]interface{})
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if !ok {
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continue
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}
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blockType, _ := block["type"].(string)
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switch blockType {
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case "text":
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if t, ok := block["text"].(string); ok {
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text += t
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}
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case "tool_use":
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id, _ := block["id"].(string)
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name, _ := block["name"].(string)
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input, _ := block["input"].(map[string]interface{})
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if input == nil {
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input = make(map[string]interface{})
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}
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toolUses = append(toolUses, KiroToolUse{
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ToolUseID: id,
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Name: name,
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Input: input,
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})
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}
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}
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}
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if text == "" && len(toolUses) > 0 {
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text = "Using tools."
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}
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return text, toolUses
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}
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func convertClaudeTools(tools []ClaudeTool) []KiroToolWrapper {
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if len(tools) == 0 {
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return nil
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}
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result := make([]KiroToolWrapper, len(tools))
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for i, tool := range tools {
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desc := tool.Description
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if len(desc) > maxToolDescLen {
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desc = desc[:maxToolDescLen] + "..."
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}
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result[i] = KiroToolWrapper{}
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result[i].ToolSpecification.Name = shortenToolName(tool.Name)
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result[i].ToolSpecification.Description = desc
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result[i].ToolSpecification.InputSchema = InputSchema{JSON: tool.InputSchema}
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}
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return result
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}
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func shortenToolName(name string) string {
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if len(name) <= 64 {
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return name
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}
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// MCP tools: mcp__server__tool -> mcp__tool
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if strings.HasPrefix(name, "mcp__") {
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lastIdx := strings.LastIndex(name, "__")
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if lastIdx > 5 {
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shortened := "mcp__" + name[lastIdx+2:]
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if len(shortened) <= 64 {
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return shortened
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}
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}
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}
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return name[:64]
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}
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// ==================== Kiro -> Claude 转换 ====================
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func KiroToClaudeResponse(content string, toolUses []KiroToolUse, inputTokens, outputTokens int, model string) *ClaudeResponse {
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blocks := make([]ClaudeContentBlock, 0)
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if content != "" {
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blocks = append(blocks, ClaudeContentBlock{
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Type: "text",
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Text: content,
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})
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}
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for _, tu := range toolUses {
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blocks = append(blocks, ClaudeContentBlock{
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Type: "tool_use",
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ID: tu.ToolUseID,
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Name: tu.Name,
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Input: tu.Input,
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})
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}
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stopReason := "end_turn"
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if len(toolUses) > 0 {
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stopReason = "tool_use"
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}
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return &ClaudeResponse{
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ID: "msg_" + uuid.New().String(),
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Type: "message",
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Role: "assistant",
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Content: blocks,
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Model: model,
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StopReason: stopReason,
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Usage: ClaudeUsage{
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InputTokens: inputTokens,
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OutputTokens: outputTokens,
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},
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}
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}
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// ==================== OpenAI API 类型 ====================
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type OpenAIRequest struct {
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Model string `json:"model"`
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Messages []OpenAIMessage `json:"messages"`
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MaxTokens int `json:"max_tokens,omitempty"`
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Temperature float64 `json:"temperature,omitempty"`
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TopP float64 `json:"top_p,omitempty"`
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Stream bool `json:"stream,omitempty"`
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Tools []OpenAITool `json:"tools,omitempty"`
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}
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type OpenAIMessage struct {
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Role string `json:"role"`
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Content interface{} `json:"content"`
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ToolCalls []ToolCall `json:"tool_calls,omitempty"`
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ToolCallID string `json:"tool_call_id,omitempty"`
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}
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type ToolCall struct {
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ID string `json:"id"`
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Type string `json:"type"`
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Function struct {
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Name string `json:"name"`
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Arguments string `json:"arguments"`
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} `json:"function"`
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}
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type OpenAITool struct {
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Type string `json:"type"`
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Function struct {
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Name string `json:"name"`
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Description string `json:"description"`
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Parameters interface{} `json:"parameters"`
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} `json:"function"`
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}
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type OpenAIResponse struct {
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ID string `json:"id"`
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Object string `json:"object"`
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Created int64 `json:"created"`
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Model string `json:"model"`
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Choices []OpenAIChoice `json:"choices"`
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Usage OpenAIUsage `json:"usage"`
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}
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type OpenAIChoice struct {
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Index int `json:"index"`
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Message OpenAIMessage `json:"message"`
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FinishReason string `json:"finish_reason"`
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}
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type OpenAIUsage struct {
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PromptTokens int `json:"prompt_tokens"`
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CompletionTokens int `json:"completion_tokens"`
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TotalTokens int `json:"total_tokens"`
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}
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// ==================== OpenAI -> Kiro 转换 ====================
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func OpenAIToKiro(req *OpenAIRequest) *KiroPayload {
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modelID := MapModel(req.Model)
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origin := "AI_EDITOR"
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// 提取系统提示
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var systemPrompt string
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var nonSystemMessages []OpenAIMessage
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for _, msg := range req.Messages {
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if msg.Role == "system" {
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if s, ok := msg.Content.(string); ok {
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systemPrompt += s + "\n"
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}
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} else {
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nonSystemMessages = append(nonSystemMessages, msg)
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}
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}
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// 注入时间戳
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timestamp := time.Now().Format(time.RFC3339)
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systemPrompt = "[Context: Current time is " + timestamp + "]\n\n" + systemPrompt
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|
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// 注入执行导向指令（防止 AI 在探索过程中丢失目标）
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executionDirective := `
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<execution_discipline>
|
|
当用户要求执行特定任务时，你必须遵循以下纪律：
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|
1. **目标锁定**：在整个会话中始终牢记用户的原始目标，不要在代码探索过程中迷失方向
|
|
2. **行动优先**：优先执行任务而非仅分析或总结，除非用户明确只要求分析
|
|
3. **计划执行**：为任务创建明确的步骤计划，逐步执行并标记完成状态
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|
4. **禁止确认性收尾**：在任务未完成前，禁止输出"需要我继续吗？"、"需要深入分析吗？"等确认性问题
|
|
5. **持续推进**：如果发现部分任务已完成，立即继续执行剩余未完成的任务
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|
6. **完整交付**：直到所有任务步骤都执行完毕才算完成
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|
</execution_discipline>
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|
`
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|
systemPrompt = systemPrompt + "\n\n" + executionDirective
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|
|
|
// 构建历史消息
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|
history := make([]KiroHistoryMessage, 0)
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|
var currentContent string
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|
var currentImages []KiroImage
|
|
var currentToolResults []KiroToolResult
|
|
systemMerged := false
|
|
|
|
for i, msg := range nonSystemMessages {
|
|
isLast := i == len(nonSystemMessages)-1
|
|
|
|
switch msg.Role {
|
|
case "user":
|
|
content, images := extractOpenAIUserContent(msg.Content)
|
|
|
|
// 第一条 user 消息合并 system prompt
|
|
if !systemMerged && systemPrompt != "" {
|
|
content = systemPrompt + "\n" + content
|
|
systemMerged = true
|
|
}
|
|
|
|
if isLast {
|
|
currentContent = content
|
|
currentImages = images
|
|
} else {
|
|
history = append(history, KiroHistoryMessage{
|
|
UserInputMessage: &KiroUserInputMessage{
|
|
Content: content,
|
|
ModelID: modelID,
|
|
Origin: origin,
|
|
Images: images,
|
|
},
|
|
})
|
|
}
|
|
|
|
case "assistant":
|
|
content, _ := msg.Content.(string)
|
|
if content == "" && len(msg.ToolCalls) > 0 {
|
|
content = "Using tools."
|
|
}
|
|
|
|
var toolUses []KiroToolUse
|
|
for _, tc := range msg.ToolCalls {
|
|
var input map[string]interface{}
|
|
json.Unmarshal([]byte(tc.Function.Arguments), &input)
|
|
if input == nil {
|
|
input = make(map[string]interface{})
|
|
}
|
|
toolUses = append(toolUses, KiroToolUse{
|
|
ToolUseID: tc.ID,
|
|
Name: tc.Function.Name,
|
|
Input: input,
|
|
})
|
|
}
|
|
|
|
history = append(history, KiroHistoryMessage{
|
|
AssistantResponseMessage: &KiroAssistantResponseMessage{
|
|
Content: content,
|
|
ToolUses: toolUses,
|
|
},
|
|
})
|
|
|
|
case "tool":
|
|
content, _ := msg.Content.(string)
|
|
currentToolResults = append(currentToolResults, KiroToolResult{
|
|
ToolUseID: msg.ToolCallID,
|
|
Content: []KiroResultContent{{Text: content}},
|
|
Status: "success",
|
|
})
|
|
|
|
// 检查下一条是否还是 tool
|
|
nextIdx := i + 1
|
|
if nextIdx >= len(nonSystemMessages) || nonSystemMessages[nextIdx].Role != "tool" {
|
|
if !isLast {
|
|
history = append(history, KiroHistoryMessage{
|
|
UserInputMessage: &KiroUserInputMessage{
|
|
Content: "Tool results provided.",
|
|
ModelID: modelID,
|
|
Origin: origin,
|
|
UserInputMessageContext: &UserInputMessageContext{
|
|
ToolResults: currentToolResults,
|
|
},
|
|
},
|
|
})
|
|
currentToolResults = nil
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 构建最终内容
|
|
finalContent := currentContent
|
|
if finalContent == "" {
|
|
if len(currentToolResults) > 0 {
|
|
finalContent = "Tool results provided."
|
|
} else {
|
|
finalContent = "Continue"
|
|
}
|
|
}
|
|
if !systemMerged && systemPrompt != "" {
|
|
finalContent = systemPrompt + "\n" + finalContent
|
|
}
|
|
|
|
// 转换工具
|
|
kiroTools := convertOpenAITools(req.Tools)
|
|
|
|
// 构建 payload
|
|
payload := &KiroPayload{}
|
|
payload.ConversationState.ChatTriggerType = "MANUAL"
|
|
payload.ConversationState.ConversationID = uuid.New().String()
|
|
payload.ConversationState.CurrentMessage.UserInputMessage = KiroUserInputMessage{
|
|
Content: finalContent,
|
|
ModelID: modelID,
|
|
Origin: origin,
|
|
Images: currentImages,
|
|
}
|
|
|
|
if len(kiroTools) > 0 || len(currentToolResults) > 0 {
|
|
payload.ConversationState.CurrentMessage.UserInputMessage.UserInputMessageContext = &UserInputMessageContext{
|
|
Tools: kiroTools,
|
|
ToolResults: currentToolResults,
|
|
}
|
|
}
|
|
|
|
if len(history) > 0 {
|
|
payload.ConversationState.History = history
|
|
}
|
|
|
|
if req.MaxTokens > 0 || req.Temperature > 0 || req.TopP > 0 {
|
|
payload.InferenceConfig = &InferenceConfig{
|
|
MaxTokens: req.MaxTokens,
|
|
Temperature: req.Temperature,
|
|
TopP: req.TopP,
|
|
}
|
|
}
|
|
|
|
return payload
|
|
}
|
|
|
|
func extractOpenAIUserContent(content interface{}) (string, []KiroImage) {
|
|
if s, ok := content.(string); ok {
|
|
return s, nil
|
|
}
|
|
|
|
var text string
|
|
var images []KiroImage
|
|
|
|
if parts, ok := content.([]interface{}); ok {
|
|
for _, p := range parts {
|
|
part, ok := p.(map[string]interface{})
|
|
if !ok {
|
|
continue
|
|
}
|
|
|
|
partType, _ := part["type"].(string)
|
|
switch partType {
|
|
case "text":
|
|
if t, ok := part["text"].(string); ok {
|
|
text += t
|
|
}
|
|
case "image_url":
|
|
if imgUrl, ok := part["image_url"].(map[string]interface{}); ok {
|
|
if url, ok := imgUrl["url"].(string); ok {
|
|
if img := parseDataURL(url); img != nil {
|
|
images = append(images, *img)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return text, images
|
|
}
|
|
|
|
func parseDataURL(url string) *KiroImage {
|
|
// data:image/png;base64,xxxxx
|
|
re := regexp.MustCompile(`^data:image/(\w+);base64,(.+)$`)
|
|
matches := re.FindStringSubmatch(url)
|
|
if len(matches) != 3 {
|
|
return nil
|
|
}
|
|
|
|
format := matches[1]
|
|
if format == "jpg" {
|
|
format = "jpeg"
|
|
}
|
|
|
|
// 验证 base64
|
|
if _, err := base64.StdEncoding.DecodeString(matches[2]); err != nil {
|
|
return nil
|
|
}
|
|
|
|
return &KiroImage{
|
|
Format: format,
|
|
Source: struct {
|
|
Bytes string `json:"bytes"`
|
|
}{Bytes: matches[2]},
|
|
}
|
|
}
|
|
|
|
func convertOpenAITools(tools []OpenAITool) []KiroToolWrapper {
|
|
if len(tools) == 0 {
|
|
return nil
|
|
}
|
|
|
|
result := make([]KiroToolWrapper, 0, len(tools))
|
|
for _, tool := range tools {
|
|
if tool.Type != "function" {
|
|
continue
|
|
}
|
|
desc := tool.Function.Description
|
|
if len(desc) > maxToolDescLen {
|
|
desc = desc[:maxToolDescLen] + "..."
|
|
}
|
|
wrapper := KiroToolWrapper{}
|
|
wrapper.ToolSpecification.Name = shortenToolName(tool.Function.Name)
|
|
wrapper.ToolSpecification.Description = desc
|
|
wrapper.ToolSpecification.InputSchema = InputSchema{JSON: tool.Function.Parameters}
|
|
result = append(result, wrapper)
|
|
}
|
|
return result
|
|
}
|
|
|
|
// ==================== Kiro -> OpenAI 转换 ====================
|
|
|
|
func KiroToOpenAIResponse(content string, toolUses []KiroToolUse, inputTokens, outputTokens int, model string) *OpenAIResponse {
|
|
msg := OpenAIMessage{
|
|
Role: "assistant",
|
|
}
|
|
|
|
finishReason := "stop"
|
|
|
|
if len(toolUses) > 0 {
|
|
msg.Content = nil
|
|
msg.ToolCalls = make([]ToolCall, len(toolUses))
|
|
for i, tu := range toolUses {
|
|
args, _ := json.Marshal(tu.Input)
|
|
msg.ToolCalls[i] = ToolCall{
|
|
ID: tu.ToolUseID,
|
|
Type: "function",
|
|
}
|
|
msg.ToolCalls[i].Function.Name = tu.Name
|
|
msg.ToolCalls[i].Function.Arguments = string(args)
|
|
}
|
|
finishReason = "tool_calls"
|
|
} else {
|
|
msg.Content = content
|
|
}
|
|
|
|
return &OpenAIResponse{
|
|
ID: "chatcmpl-" + uuid.New().String(),
|
|
Object: "chat.completion",
|
|
Created: time.Now().Unix(),
|
|
Model: model,
|
|
Choices: []OpenAIChoice{{
|
|
Index: 0,
|
|
Message: msg,
|
|
FinishReason: finishReason,
|
|
}},
|
|
Usage: OpenAIUsage{
|
|
PromptTokens: inputTokens,
|
|
CompletionTokens: outputTokens,
|
|
TotalTokens: inputTokens + outputTokens,
|
|
},
|
|
}
|
|
}
|