mirror of
https://github.com/githubhjs/CLIProxyAPIPlus.git
synced 2026-07-12 01:25:13 +00:00
feat(kiro): 代码优化重构 + OpenAI翻译器实现
This commit is contained in:
Ravens2121
@@ -1,348 +0,0 @@
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// Package chat_completions provides request translation from OpenAI to Kiro format.
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package chat_completions
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import (
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"bytes"
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"encoding/json"
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"strings"
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"github.com/tidwall/gjson"
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"github.com/tidwall/sjson"
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)
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// reasoningEffortToBudget maps OpenAI reasoning_effort values to Claude thinking budget_tokens.
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// OpenAI uses "low", "medium", "high" while Claude uses numeric budget_tokens.
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var reasoningEffortToBudget = map[string]int{
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"low": 4000,
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"medium": 16000,
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"high": 32000,
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}
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// ConvertOpenAIRequestToKiro transforms an OpenAI Chat Completions API request into Kiro (Claude) format.
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// Kiro uses Claude-compatible format internally, so we primarily pass through to Claude format.
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// Supports tool calling: OpenAI tools -> Claude tools, tool_calls -> tool_use, tool messages -> tool_result.
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// Supports reasoning/thinking: OpenAI reasoning_effort -> Claude thinking parameter.
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func ConvertOpenAIRequestToKiro(modelName string, inputRawJSON []byte, stream bool) []byte {
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rawJSON := bytes.Clone(inputRawJSON)
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root := gjson.ParseBytes(rawJSON)
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// Build Claude-compatible request
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out := `{"model":"","max_tokens":32000,"messages":[]}`
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// Set model
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out, _ = sjson.Set(out, "model", modelName)
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// Copy max_tokens if present
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if v := root.Get("max_tokens"); v.Exists() {
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out, _ = sjson.Set(out, "max_tokens", v.Int())
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}
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// Copy temperature if present
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if v := root.Get("temperature"); v.Exists() {
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out, _ = sjson.Set(out, "temperature", v.Float())
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}
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// Copy top_p if present
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if v := root.Get("top_p"); v.Exists() {
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out, _ = sjson.Set(out, "top_p", v.Float())
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}
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// Handle OpenAI reasoning_effort parameter -> Claude thinking parameter
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// OpenAI format: {"reasoning_effort": "low"|"medium"|"high"}
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// Claude format: {"thinking": {"type": "enabled", "budget_tokens": N}}
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if v := root.Get("reasoning_effort"); v.Exists() {
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effort := v.String()
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if budget, ok := reasoningEffortToBudget[effort]; ok {
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thinking := map[string]interface{}{
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"type": "enabled",
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"budget_tokens": budget,
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}
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out, _ = sjson.Set(out, "thinking", thinking)
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}
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}
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// Also support direct thinking parameter passthrough (for Claude API compatibility)
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// Claude format: {"thinking": {"type": "enabled", "budget_tokens": N}}
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if v := root.Get("thinking"); v.Exists() && v.IsObject() {
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out, _ = sjson.Set(out, "thinking", v.Value())
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}
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// Convert OpenAI tools to Claude tools format
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if tools := root.Get("tools"); tools.Exists() && tools.IsArray() {
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claudeTools := make([]interface{}, 0)
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for _, tool := range tools.Array() {
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if tool.Get("type").String() == "function" {
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fn := tool.Get("function")
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claudeTool := map[string]interface{}{
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"name": fn.Get("name").String(),
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"description": fn.Get("description").String(),
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}
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// Convert parameters to input_schema
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if params := fn.Get("parameters"); params.Exists() {
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claudeTool["input_schema"] = params.Value()
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} else {
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claudeTool["input_schema"] = map[string]interface{}{
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"type": "object",
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"properties": map[string]interface{}{},
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}
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}
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claudeTools = append(claudeTools, claudeTool)
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}
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}
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if len(claudeTools) > 0 {
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out, _ = sjson.Set(out, "tools", claudeTools)
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}
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}
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// Process messages
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messages := root.Get("messages")
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if messages.Exists() && messages.IsArray() {
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claudeMessages := make([]interface{}, 0)
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var systemPrompt string
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// Track pending tool results to merge with next user message
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var pendingToolResults []map[string]interface{}
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for _, msg := range messages.Array() {
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role := msg.Get("role").String()
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content := msg.Get("content")
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if role == "system" {
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// Extract system message
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if content.IsArray() {
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for _, part := range content.Array() {
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if part.Get("type").String() == "text" {
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systemPrompt += part.Get("text").String() + "\n"
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}
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}
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} else {
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systemPrompt = content.String()
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}
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continue
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}
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if role == "tool" {
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// OpenAI tool message -> Claude tool_result content block
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toolCallID := msg.Get("tool_call_id").String()
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toolContent := content.String()
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toolResult := map[string]interface{}{
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"type": "tool_result",
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"tool_use_id": toolCallID,
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}
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// Handle content - can be string or structured
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if content.IsArray() {
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contentParts := make([]interface{}, 0)
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for _, part := range content.Array() {
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if part.Get("type").String() == "text" {
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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"text": part.Get("text").String(),
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})
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}
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}
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toolResult["content"] = contentParts
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} else {
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toolResult["content"] = toolContent
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}
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pendingToolResults = append(pendingToolResults, toolResult)
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continue
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}
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claudeMsg := map[string]interface{}{
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"role": role,
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}
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// Handle assistant messages with tool_calls
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if role == "assistant" && msg.Get("tool_calls").Exists() {
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contentParts := make([]interface{}, 0)
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// Add text content if present
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if content.Exists() && content.String() != "" {
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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"text": content.String(),
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})
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}
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// Convert tool_calls to tool_use blocks
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for _, toolCall := range msg.Get("tool_calls").Array() {
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toolUseID := toolCall.Get("id").String()
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fnName := toolCall.Get("function.name").String()
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fnArgs := toolCall.Get("function.arguments").String()
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// Parse arguments JSON
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var argsMap map[string]interface{}
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if err := json.Unmarshal([]byte(fnArgs), &argsMap); err != nil {
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argsMap = map[string]interface{}{"raw": fnArgs}
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}
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contentParts = append(contentParts, map[string]interface{}{
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"type": "tool_use",
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"id": toolUseID,
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"name": fnName,
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"input": argsMap,
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})
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}
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claudeMsg["content"] = contentParts
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claudeMessages = append(claudeMessages, claudeMsg)
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continue
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}
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// Handle user messages - may need to include pending tool results
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if role == "user" && len(pendingToolResults) > 0 {
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contentParts := make([]interface{}, 0)
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// Add pending tool results first
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for _, tr := range pendingToolResults {
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contentParts = append(contentParts, tr)
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}
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pendingToolResults = nil
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// Add user content
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if content.IsArray() {
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for _, part := range content.Array() {
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partType := part.Get("type").String()
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if partType == "text" {
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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"text": part.Get("text").String(),
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})
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} else if partType == "image_url" {
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imageURL := part.Get("image_url.url").String()
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// Check if it's base64 format (data:image/png;base64,xxxxx)
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if strings.HasPrefix(imageURL, "data:") {
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// Parse data URL format
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// Format: data:image/png;base64,xxxxx
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commaIdx := strings.Index(imageURL, ",")
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if commaIdx != -1 {
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// Extract media_type (e.g., "image/png")
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header := imageURL[5:commaIdx] // Remove "data:" prefix
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mediaType := header
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if semiIdx := strings.Index(header, ";"); semiIdx != -1 {
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mediaType = header[:semiIdx]
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}
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// Extract base64 data
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base64Data := imageURL[commaIdx+1:]
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contentParts = append(contentParts, map[string]interface{}{
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"type": "image",
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"source": map[string]interface{}{
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"type": "base64",
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"media_type": mediaType,
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"data": base64Data,
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},
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})
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}
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} else {
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// Regular URL format - keep original logic
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contentParts = append(contentParts, map[string]interface{}{
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"type": "image",
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"source": map[string]interface{}{
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"type": "url",
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"url": imageURL,
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},
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})
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}
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}
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}
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} else if content.String() != "" {
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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"text": content.String(),
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})
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}
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claudeMsg["content"] = contentParts
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claudeMessages = append(claudeMessages, claudeMsg)
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continue
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}
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// Handle regular content
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if content.IsArray() {
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contentParts := make([]interface{}, 0)
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for _, part := range content.Array() {
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partType := part.Get("type").String()
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if partType == "text" {
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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"text": part.Get("text").String(),
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})
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} else if partType == "image_url" {
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imageURL := part.Get("image_url.url").String()
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// Check if it's base64 format (data:image/png;base64,xxxxx)
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if strings.HasPrefix(imageURL, "data:") {
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// Parse data URL format
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// Format: data:image/png;base64,xxxxx
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commaIdx := strings.Index(imageURL, ",")
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if commaIdx != -1 {
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// Extract media_type (e.g., "image/png")
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header := imageURL[5:commaIdx] // Remove "data:" prefix
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mediaType := header
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if semiIdx := strings.Index(header, ";"); semiIdx != -1 {
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mediaType = header[:semiIdx]
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}
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// Extract base64 data
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base64Data := imageURL[commaIdx+1:]
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contentParts = append(contentParts, map[string]interface{}{
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"type": "image",
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"source": map[string]interface{}{
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"type": "base64",
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"media_type": mediaType,
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"data": base64Data,
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},
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})
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}
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} else {
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// Regular URL format - keep original logic
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contentParts = append(contentParts, map[string]interface{}{
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"type": "image",
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"source": map[string]interface{}{
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"type": "url",
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"url": imageURL,
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},
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})
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}
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}
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}
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claudeMsg["content"] = contentParts
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} else {
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claudeMsg["content"] = content.String()
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}
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claudeMessages = append(claudeMessages, claudeMsg)
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}
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// If there are pending tool results without a following user message,
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// create a user message with just the tool results
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if len(pendingToolResults) > 0 {
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contentParts := make([]interface{}, 0)
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for _, tr := range pendingToolResults {
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contentParts = append(contentParts, tr)
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}
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claudeMessages = append(claudeMessages, map[string]interface{}{
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"role": "user",
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"content": contentParts,
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})
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}
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out, _ = sjson.Set(out, "messages", claudeMessages)
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if systemPrompt != "" {
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out, _ = sjson.Set(out, "system", systemPrompt)
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}
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}
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// Set stream
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out, _ = sjson.Set(out, "stream", stream)
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return []byte(out)
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}
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@@ -1,404 +0,0 @@
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// Package chat_completions provides response translation from Kiro to OpenAI format.
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package chat_completions
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import (
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"context"
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"encoding/json"
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"strings"
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"time"
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"github.com/google/uuid"
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"github.com/tidwall/gjson"
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)
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// ConvertKiroResponseToOpenAI converts Kiro streaming response to OpenAI SSE format.
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// Handles Claude SSE events: content_block_start, content_block_delta, input_json_delta,
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// content_block_stop, message_delta, and message_stop.
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// Input may be in SSE format: "event: xxx\ndata: {...}" or raw JSON.
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func ConvertKiroResponseToOpenAI(ctx context.Context, model string, originalRequest, request, rawResponse []byte, param *any) []string {
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raw := string(rawResponse)
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var results []string
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// Handle SSE format: extract JSON from "data: " lines
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// Input format: "event: message_start\ndata: {...}"
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lines := strings.Split(raw, "\n")
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for _, line := range lines {
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line = strings.TrimSpace(line)
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if strings.HasPrefix(line, "data: ") {
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jsonPart := strings.TrimPrefix(line, "data: ")
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chunks := convertClaudeEventToOpenAI(jsonPart, model)
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results = append(results, chunks...)
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} else if strings.HasPrefix(line, "{") {
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// Raw JSON (backward compatibility)
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chunks := convertClaudeEventToOpenAI(line, model)
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results = append(results, chunks...)
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}
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}
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return results
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}
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// convertClaudeEventToOpenAI converts a single Claude JSON event to OpenAI format
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func convertClaudeEventToOpenAI(jsonStr string, model string) []string {
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root := gjson.Parse(jsonStr)
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var results []string
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eventType := root.Get("type").String()
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switch eventType {
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case "message_start":
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// Initial message event - emit initial chunk with role
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response := map[string]interface{}{
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"id": "chatcmpl-" + uuid.New().String()[:24],
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"object": "chat.completion.chunk",
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"created": time.Now().Unix(),
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"model": model,
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"choices": []map[string]interface{}{
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{
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"index": 0,
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"delta": map[string]interface{}{
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"role": "assistant",
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"content": "",
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},
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"finish_reason": nil,
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},
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},
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}
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result, _ := json.Marshal(response)
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results = append(results, string(result))
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return results
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case "content_block_start":
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// Start of a content block (text or tool_use)
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blockType := root.Get("content_block.type").String()
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index := int(root.Get("index").Int())
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if blockType == "tool_use" {
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// Start of tool_use block
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toolUseID := root.Get("content_block.id").String()
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toolName := root.Get("content_block.name").String()
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toolCall := map[string]interface{}{
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"index": index,
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"id": toolUseID,
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"type": "function",
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"function": map[string]interface{}{
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"name": toolName,
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"arguments": "",
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},
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}
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response := map[string]interface{}{
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"id": "chatcmpl-" + uuid.New().String()[:24],
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"object": "chat.completion.chunk",
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"created": time.Now().Unix(),
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"model": model,
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"choices": []map[string]interface{}{
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{
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"index": 0,
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"delta": map[string]interface{}{
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"tool_calls": []map[string]interface{}{toolCall},
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},
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"finish_reason": nil,
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},
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},
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}
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result, _ := json.Marshal(response)
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results = append(results, string(result))
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}
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return results
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case "content_block_delta":
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index := int(root.Get("index").Int())
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deltaType := root.Get("delta.type").String()
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if deltaType == "text_delta" {
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// Text content delta
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contentDelta := root.Get("delta.text").String()
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if contentDelta != "" {
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response := map[string]interface{}{
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"id": "chatcmpl-" + uuid.New().String()[:24],
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"object": "chat.completion.chunk",
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"created": time.Now().Unix(),
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"model": model,
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"choices": []map[string]interface{}{
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{
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"index": 0,
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"delta": map[string]interface{}{
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"content": contentDelta,
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},
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"finish_reason": nil,
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},
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},
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}
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result, _ := json.Marshal(response)
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results = append(results, string(result))
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}
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} else if deltaType == "thinking_delta" {
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// Thinking/reasoning content delta - convert to OpenAI reasoning_content format
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thinkingDelta := root.Get("delta.thinking").String()
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if thinkingDelta != "" {
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response := map[string]interface{}{
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"id": "chatcmpl-" + uuid.New().String()[:24],
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"object": "chat.completion.chunk",
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"created": time.Now().Unix(),
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"model": model,
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"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"delta": map[string]interface{}{
|
||||
"reasoning_content": thinkingDelta,
|
||||
},
|
||||
"finish_reason": nil,
|
||||
},
|
||||
},
|
||||
}
|
||||
result, _ := json.Marshal(response)
|
||||
results = append(results, string(result))
|
||||
}
|
||||
} else if deltaType == "input_json_delta" {
|
||||
// Tool input delta (streaming arguments)
|
||||
partialJSON := root.Get("delta.partial_json").String()
|
||||
if partialJSON != "" {
|
||||
toolCall := map[string]interface{}{
|
||||
"index": index,
|
||||
"function": map[string]interface{}{
|
||||
"arguments": partialJSON,
|
||||
},
|
||||
}
|
||||
|
||||
response := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:24],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"delta": map[string]interface{}{
|
||||
"tool_calls": []map[string]interface{}{toolCall},
|
||||
},
|
||||
"finish_reason": nil,
|
||||
},
|
||||
},
|
||||
}
|
||||
result, _ := json.Marshal(response)
|
||||
results = append(results, string(result))
|
||||
}
|
||||
}
|
||||
return results
|
||||
|
||||
case "content_block_stop":
|
||||
// End of content block - no output needed for OpenAI format
|
||||
return results
|
||||
|
||||
case "message_delta":
|
||||
// Final message delta with stop_reason and usage
|
||||
stopReason := root.Get("delta.stop_reason").String()
|
||||
if stopReason != "" {
|
||||
finishReason := "stop"
|
||||
if stopReason == "tool_use" {
|
||||
finishReason = "tool_calls"
|
||||
} else if stopReason == "end_turn" {
|
||||
finishReason = "stop"
|
||||
} else if stopReason == "max_tokens" {
|
||||
finishReason = "length"
|
||||
}
|
||||
|
||||
response := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:24],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"delta": map[string]interface{}{},
|
||||
"finish_reason": finishReason,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
// Extract and include usage information from message_delta event
|
||||
usage := root.Get("usage")
|
||||
if usage.Exists() {
|
||||
inputTokens := usage.Get("input_tokens").Int()
|
||||
outputTokens := usage.Get("output_tokens").Int()
|
||||
response["usage"] = map[string]interface{}{
|
||||
"prompt_tokens": inputTokens,
|
||||
"completion_tokens": outputTokens,
|
||||
"total_tokens": inputTokens + outputTokens,
|
||||
}
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(response)
|
||||
results = append(results, string(result))
|
||||
}
|
||||
return results
|
||||
|
||||
case "message_stop":
|
||||
// End of message - could emit [DONE] marker
|
||||
return results
|
||||
}
|
||||
|
||||
// Fallback: handle raw content for backward compatibility
|
||||
var contentDelta string
|
||||
if delta := root.Get("delta.text"); delta.Exists() {
|
||||
contentDelta = delta.String()
|
||||
} else if content := root.Get("content"); content.Exists() && root.Get("type").String() == "" {
|
||||
contentDelta = content.String()
|
||||
}
|
||||
|
||||
if contentDelta != "" {
|
||||
response := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:24],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"delta": map[string]interface{}{
|
||||
"content": contentDelta,
|
||||
},
|
||||
"finish_reason": nil,
|
||||
},
|
||||
},
|
||||
}
|
||||
result, _ := json.Marshal(response)
|
||||
results = append(results, string(result))
|
||||
}
|
||||
|
||||
// Handle tool_use content blocks (Claude format) - fallback
|
||||
toolUses := root.Get("delta.tool_use")
|
||||
if !toolUses.Exists() {
|
||||
toolUses = root.Get("tool_use")
|
||||
}
|
||||
if toolUses.Exists() && toolUses.IsObject() {
|
||||
inputJSON := toolUses.Get("input").String()
|
||||
if inputJSON == "" {
|
||||
if inputObj := toolUses.Get("input"); inputObj.Exists() {
|
||||
inputBytes, _ := json.Marshal(inputObj.Value())
|
||||
inputJSON = string(inputBytes)
|
||||
}
|
||||
}
|
||||
|
||||
toolCall := map[string]interface{}{
|
||||
"index": 0,
|
||||
"id": toolUses.Get("id").String(),
|
||||
"type": "function",
|
||||
"function": map[string]interface{}{
|
||||
"name": toolUses.Get("name").String(),
|
||||
"arguments": inputJSON,
|
||||
},
|
||||
}
|
||||
|
||||
response := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:24],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"delta": map[string]interface{}{
|
||||
"tool_calls": []map[string]interface{}{toolCall},
|
||||
},
|
||||
"finish_reason": nil,
|
||||
},
|
||||
},
|
||||
}
|
||||
result, _ := json.Marshal(response)
|
||||
results = append(results, string(result))
|
||||
}
|
||||
|
||||
return results
|
||||
}
|
||||
|
||||
// ConvertKiroResponseToOpenAINonStream converts Kiro non-streaming response to OpenAI format.
|
||||
func ConvertKiroResponseToOpenAINonStream(ctx context.Context, model string, originalRequest, request, rawResponse []byte, param *any) string {
|
||||
root := gjson.ParseBytes(rawResponse)
|
||||
|
||||
var content string
|
||||
var reasoningContent string
|
||||
var toolCalls []map[string]interface{}
|
||||
|
||||
contentArray := root.Get("content")
|
||||
if contentArray.IsArray() {
|
||||
for _, item := range contentArray.Array() {
|
||||
itemType := item.Get("type").String()
|
||||
if itemType == "text" {
|
||||
content += item.Get("text").String()
|
||||
} else if itemType == "thinking" {
|
||||
// Extract thinking/reasoning content
|
||||
reasoningContent += item.Get("thinking").String()
|
||||
} else if itemType == "tool_use" {
|
||||
// Convert Claude tool_use to OpenAI tool_calls format
|
||||
inputJSON := item.Get("input").String()
|
||||
if inputJSON == "" {
|
||||
// If input is an object, marshal it
|
||||
if inputObj := item.Get("input"); inputObj.Exists() {
|
||||
inputBytes, _ := json.Marshal(inputObj.Value())
|
||||
inputJSON = string(inputBytes)
|
||||
}
|
||||
}
|
||||
toolCall := map[string]interface{}{
|
||||
"id": item.Get("id").String(),
|
||||
"type": "function",
|
||||
"function": map[string]interface{}{
|
||||
"name": item.Get("name").String(),
|
||||
"arguments": inputJSON,
|
||||
},
|
||||
}
|
||||
toolCalls = append(toolCalls, toolCall)
|
||||
}
|
||||
}
|
||||
} else {
|
||||
content = root.Get("content").String()
|
||||
}
|
||||
|
||||
inputTokens := root.Get("usage.input_tokens").Int()
|
||||
outputTokens := root.Get("usage.output_tokens").Int()
|
||||
|
||||
message := map[string]interface{}{
|
||||
"role": "assistant",
|
||||
"content": content,
|
||||
}
|
||||
|
||||
// Add reasoning_content if present (OpenAI reasoning format)
|
||||
if reasoningContent != "" {
|
||||
message["reasoning_content"] = reasoningContent
|
||||
}
|
||||
|
||||
// Add tool_calls if present
|
||||
if len(toolCalls) > 0 {
|
||||
message["tool_calls"] = toolCalls
|
||||
}
|
||||
|
||||
finishReason := "stop"
|
||||
if len(toolCalls) > 0 {
|
||||
finishReason = "tool_calls"
|
||||
}
|
||||
|
||||
response := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:24],
|
||||
"object": "chat.completion",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"message": message,
|
||||
"finish_reason": finishReason,
|
||||
},
|
||||
},
|
||||
"usage": map[string]interface{}{
|
||||
"prompt_tokens": inputTokens,
|
||||
"completion_tokens": outputTokens,
|
||||
"total_tokens": inputTokens + outputTokens,
|
||||
},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(response)
|
||||
return string(result)
|
||||
}
|
||||
+7
-6
@@ -1,4 +1,5 @@
|
||||
package chat_completions
|
||||
// Package openai provides translation between OpenAI Chat Completions and Kiro formats.
|
||||
package openai
|
||||
|
||||
import (
|
||||
. "github.com/router-for-me/CLIProxyAPI/v6/internal/constant"
|
||||
@@ -8,12 +9,12 @@ import (
|
||||
|
||||
func init() {
|
||||
translator.Register(
|
||||
OpenAI,
|
||||
Kiro,
|
||||
OpenAI, // source format
|
||||
Kiro, // target format
|
||||
ConvertOpenAIRequestToKiro,
|
||||
interfaces.TranslateResponse{
|
||||
Stream: ConvertKiroResponseToOpenAI,
|
||||
NonStream: ConvertKiroResponseToOpenAINonStream,
|
||||
Stream: ConvertKiroStreamToOpenAI,
|
||||
NonStream: ConvertKiroNonStreamToOpenAI,
|
||||
},
|
||||
)
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,368 @@
|
||||
// Package openai provides translation between OpenAI Chat Completions and Kiro formats.
|
||||
// This package enables direct OpenAI → Kiro translation, bypassing the Claude intermediate layer.
|
||||
//
|
||||
// The Kiro executor generates Claude-compatible SSE format internally, so the streaming response
|
||||
// translation converts from Claude SSE format to OpenAI SSE format.
|
||||
package openai
|
||||
|
||||
import (
|
||||
"bytes"
|
||||
"context"
|
||||
"encoding/json"
|
||||
"strings"
|
||||
|
||||
kirocommon "github.com/router-for-me/CLIProxyAPI/v6/internal/translator/kiro/common"
|
||||
"github.com/router-for-me/CLIProxyAPI/v6/sdk/cliproxy/usage"
|
||||
log "github.com/sirupsen/logrus"
|
||||
"github.com/tidwall/gjson"
|
||||
)
|
||||
|
||||
// ConvertKiroStreamToOpenAI converts Kiro streaming response to OpenAI format.
|
||||
// The Kiro executor emits Claude-compatible SSE events, so this function translates
|
||||
// from Claude SSE format to OpenAI SSE format.
|
||||
//
|
||||
// Claude SSE format:
|
||||
// - event: message_start\ndata: {...}
|
||||
// - event: content_block_start\ndata: {...}
|
||||
// - event: content_block_delta\ndata: {...}
|
||||
// - event: content_block_stop\ndata: {...}
|
||||
// - event: message_delta\ndata: {...}
|
||||
// - event: message_stop\ndata: {...}
|
||||
//
|
||||
// OpenAI SSE format:
|
||||
// - data: {"id":"...","object":"chat.completion.chunk",...}
|
||||
// - data: [DONE]
|
||||
func ConvertKiroStreamToOpenAI(ctx context.Context, model string, originalRequest, request, rawResponse []byte, param *any) []string {
|
||||
// Initialize state if needed
|
||||
if *param == nil {
|
||||
*param = NewOpenAIStreamState(model)
|
||||
}
|
||||
state := (*param).(*OpenAIStreamState)
|
||||
|
||||
// Parse the Claude SSE event
|
||||
responseStr := string(rawResponse)
|
||||
|
||||
// Handle raw event format (event: xxx\ndata: {...})
|
||||
var eventType string
|
||||
var eventData string
|
||||
|
||||
if strings.HasPrefix(responseStr, "event:") {
|
||||
// Parse event type and data
|
||||
lines := strings.SplitN(responseStr, "\n", 2)
|
||||
if len(lines) >= 1 {
|
||||
eventType = strings.TrimSpace(strings.TrimPrefix(lines[0], "event:"))
|
||||
}
|
||||
if len(lines) >= 2 && strings.HasPrefix(lines[1], "data:") {
|
||||
eventData = strings.TrimSpace(strings.TrimPrefix(lines[1], "data:"))
|
||||
}
|
||||
} else if strings.HasPrefix(responseStr, "data:") {
|
||||
// Just data line
|
||||
eventData = strings.TrimSpace(strings.TrimPrefix(responseStr, "data:"))
|
||||
} else {
|
||||
// Try to parse as raw JSON
|
||||
eventData = strings.TrimSpace(responseStr)
|
||||
}
|
||||
|
||||
if eventData == "" {
|
||||
return []string{}
|
||||
}
|
||||
|
||||
// Parse the event data as JSON
|
||||
eventJSON := gjson.Parse(eventData)
|
||||
if !eventJSON.Exists() {
|
||||
return []string{}
|
||||
}
|
||||
|
||||
// Determine event type from JSON if not already set
|
||||
if eventType == "" {
|
||||
eventType = eventJSON.Get("type").String()
|
||||
}
|
||||
|
||||
var results []string
|
||||
|
||||
switch eventType {
|
||||
case "message_start":
|
||||
// Send first chunk with role
|
||||
firstChunk := BuildOpenAISSEFirstChunk(state)
|
||||
results = append(results, firstChunk)
|
||||
|
||||
case "content_block_start":
|
||||
// Check block type
|
||||
blockType := eventJSON.Get("content_block.type").String()
|
||||
switch blockType {
|
||||
case "text":
|
||||
// Text block starting - nothing to emit yet
|
||||
case "thinking":
|
||||
// Thinking block starting - nothing to emit yet for OpenAI
|
||||
case "tool_use":
|
||||
// Tool use block starting
|
||||
toolUseID := eventJSON.Get("content_block.id").String()
|
||||
toolName := eventJSON.Get("content_block.name").String()
|
||||
chunk := BuildOpenAISSEToolCallStart(state, toolUseID, toolName)
|
||||
results = append(results, chunk)
|
||||
state.ToolCallIndex++
|
||||
}
|
||||
|
||||
case "content_block_delta":
|
||||
deltaType := eventJSON.Get("delta.type").String()
|
||||
switch deltaType {
|
||||
case "text_delta":
|
||||
textDelta := eventJSON.Get("delta.text").String()
|
||||
if textDelta != "" {
|
||||
chunk := BuildOpenAISSETextDelta(state, textDelta)
|
||||
results = append(results, chunk)
|
||||
}
|
||||
case "thinking_delta":
|
||||
// Convert thinking to reasoning_content for o1-style compatibility
|
||||
thinkingDelta := eventJSON.Get("delta.thinking").String()
|
||||
if thinkingDelta != "" {
|
||||
chunk := BuildOpenAISSEReasoningDelta(state, thinkingDelta)
|
||||
results = append(results, chunk)
|
||||
}
|
||||
case "input_json_delta":
|
||||
// Tool call arguments delta
|
||||
partialJSON := eventJSON.Get("delta.partial_json").String()
|
||||
if partialJSON != "" {
|
||||
// Get the tool index from content block index
|
||||
blockIndex := int(eventJSON.Get("index").Int())
|
||||
chunk := BuildOpenAISSEToolCallArgumentsDelta(state, partialJSON, blockIndex-1) // Adjust for 0-based tool index
|
||||
results = append(results, chunk)
|
||||
}
|
||||
}
|
||||
|
||||
case "content_block_stop":
|
||||
// Content block ended - nothing to emit for OpenAI
|
||||
|
||||
case "message_delta":
|
||||
// Message delta with stop_reason
|
||||
stopReason := eventJSON.Get("delta.stop_reason").String()
|
||||
finishReason := mapKiroStopReasonToOpenAI(stopReason)
|
||||
if finishReason != "" {
|
||||
chunk := BuildOpenAISSEFinish(state, finishReason)
|
||||
results = append(results, chunk)
|
||||
}
|
||||
|
||||
// Extract usage if present
|
||||
if eventJSON.Get("usage").Exists() {
|
||||
inputTokens := eventJSON.Get("usage.input_tokens").Int()
|
||||
outputTokens := eventJSON.Get("usage.output_tokens").Int()
|
||||
usageInfo := usage.Detail{
|
||||
InputTokens: inputTokens,
|
||||
OutputTokens: outputTokens,
|
||||
TotalTokens: inputTokens + outputTokens,
|
||||
}
|
||||
chunk := BuildOpenAISSEUsage(state, usageInfo)
|
||||
results = append(results, chunk)
|
||||
}
|
||||
|
||||
case "message_stop":
|
||||
// Final event - emit [DONE]
|
||||
results = append(results, BuildOpenAISSEDone())
|
||||
|
||||
case "ping":
|
||||
// Ping event with usage - optionally emit usage chunk
|
||||
if eventJSON.Get("usage").Exists() {
|
||||
inputTokens := eventJSON.Get("usage.input_tokens").Int()
|
||||
outputTokens := eventJSON.Get("usage.output_tokens").Int()
|
||||
usageInfo := usage.Detail{
|
||||
InputTokens: inputTokens,
|
||||
OutputTokens: outputTokens,
|
||||
TotalTokens: inputTokens + outputTokens,
|
||||
}
|
||||
chunk := BuildOpenAISSEUsage(state, usageInfo)
|
||||
results = append(results, chunk)
|
||||
}
|
||||
}
|
||||
|
||||
return results
|
||||
}
|
||||
|
||||
// ConvertKiroNonStreamToOpenAI converts Kiro non-streaming response to OpenAI format.
|
||||
// The Kiro executor returns Claude-compatible JSON responses, so this function translates
|
||||
// from Claude format to OpenAI format.
|
||||
func ConvertKiroNonStreamToOpenAI(ctx context.Context, model string, originalRequest, request, rawResponse []byte, param *any) string {
|
||||
// Parse the Claude-format response
|
||||
response := gjson.ParseBytes(rawResponse)
|
||||
|
||||
// Extract content
|
||||
var content string
|
||||
var toolUses []KiroToolUse
|
||||
var stopReason string
|
||||
|
||||
// Get stop_reason
|
||||
stopReason = response.Get("stop_reason").String()
|
||||
|
||||
// Process content blocks
|
||||
contentBlocks := response.Get("content")
|
||||
if contentBlocks.IsArray() {
|
||||
for _, block := range contentBlocks.Array() {
|
||||
blockType := block.Get("type").String()
|
||||
switch blockType {
|
||||
case "text":
|
||||
content += block.Get("text").String()
|
||||
case "thinking":
|
||||
// Skip thinking blocks for OpenAI format (or convert to reasoning_content if needed)
|
||||
case "tool_use":
|
||||
toolUseID := block.Get("id").String()
|
||||
toolName := block.Get("name").String()
|
||||
toolInput := block.Get("input")
|
||||
|
||||
var inputMap map[string]interface{}
|
||||
if toolInput.IsObject() {
|
||||
inputMap = make(map[string]interface{})
|
||||
toolInput.ForEach(func(key, value gjson.Result) bool {
|
||||
inputMap[key.String()] = value.Value()
|
||||
return true
|
||||
})
|
||||
}
|
||||
|
||||
toolUses = append(toolUses, KiroToolUse{
|
||||
ToolUseID: toolUseID,
|
||||
Name: toolName,
|
||||
Input: inputMap,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Extract usage
|
||||
usageInfo := usage.Detail{
|
||||
InputTokens: response.Get("usage.input_tokens").Int(),
|
||||
OutputTokens: response.Get("usage.output_tokens").Int(),
|
||||
}
|
||||
usageInfo.TotalTokens = usageInfo.InputTokens + usageInfo.OutputTokens
|
||||
|
||||
// Build OpenAI response
|
||||
openaiResponse := BuildOpenAIResponse(content, toolUses, model, usageInfo, stopReason)
|
||||
return string(openaiResponse)
|
||||
}
|
||||
|
||||
// ParseClaudeEvent parses a Claude SSE event and returns the event type and data
|
||||
func ParseClaudeEvent(rawEvent []byte) (eventType string, eventData []byte) {
|
||||
lines := bytes.Split(rawEvent, []byte("\n"))
|
||||
for _, line := range lines {
|
||||
line = bytes.TrimSpace(line)
|
||||
if bytes.HasPrefix(line, []byte("event:")) {
|
||||
eventType = string(bytes.TrimSpace(bytes.TrimPrefix(line, []byte("event:"))))
|
||||
} else if bytes.HasPrefix(line, []byte("data:")) {
|
||||
eventData = bytes.TrimSpace(bytes.TrimPrefix(line, []byte("data:")))
|
||||
}
|
||||
}
|
||||
return eventType, eventData
|
||||
}
|
||||
|
||||
// ExtractThinkingFromContent parses content to extract thinking blocks.
|
||||
// Returns cleaned content (without thinking tags) and whether thinking was found.
|
||||
func ExtractThinkingFromContent(content string) (string, string, bool) {
|
||||
if !strings.Contains(content, kirocommon.ThinkingStartTag) {
|
||||
return content, "", false
|
||||
}
|
||||
|
||||
var cleanedContent strings.Builder
|
||||
var thinkingContent strings.Builder
|
||||
hasThinking := false
|
||||
remaining := content
|
||||
|
||||
for len(remaining) > 0 {
|
||||
startIdx := strings.Index(remaining, kirocommon.ThinkingStartTag)
|
||||
if startIdx == -1 {
|
||||
cleanedContent.WriteString(remaining)
|
||||
break
|
||||
}
|
||||
|
||||
// Add content before thinking tag
|
||||
cleanedContent.WriteString(remaining[:startIdx])
|
||||
|
||||
// Move past opening tag
|
||||
remaining = remaining[startIdx+len(kirocommon.ThinkingStartTag):]
|
||||
|
||||
// Find closing tag
|
||||
endIdx := strings.Index(remaining, kirocommon.ThinkingEndTag)
|
||||
if endIdx == -1 {
|
||||
// No closing tag - treat rest as thinking
|
||||
thinkingContent.WriteString(remaining)
|
||||
hasThinking = true
|
||||
break
|
||||
}
|
||||
|
||||
// Extract thinking content
|
||||
thinkingContent.WriteString(remaining[:endIdx])
|
||||
hasThinking = true
|
||||
remaining = remaining[endIdx+len(kirocommon.ThinkingEndTag):]
|
||||
}
|
||||
|
||||
return strings.TrimSpace(cleanedContent.String()), strings.TrimSpace(thinkingContent.String()), hasThinking
|
||||
}
|
||||
|
||||
// ConvertOpenAIToolsToKiroFormat is a helper that converts OpenAI tools format to Kiro format
|
||||
func ConvertOpenAIToolsToKiroFormat(tools []map[string]interface{}) []KiroToolWrapper {
|
||||
var kiroTools []KiroToolWrapper
|
||||
|
||||
for _, tool := range tools {
|
||||
toolType, _ := tool["type"].(string)
|
||||
if toolType != "function" {
|
||||
continue
|
||||
}
|
||||
|
||||
fn, ok := tool["function"].(map[string]interface{})
|
||||
if !ok {
|
||||
continue
|
||||
}
|
||||
|
||||
name := kirocommon.GetString(fn, "name")
|
||||
description := kirocommon.GetString(fn, "description")
|
||||
parameters := fn["parameters"]
|
||||
|
||||
if name == "" {
|
||||
continue
|
||||
}
|
||||
|
||||
if description == "" {
|
||||
description = "Tool: " + name
|
||||
}
|
||||
|
||||
kiroTools = append(kiroTools, KiroToolWrapper{
|
||||
ToolSpecification: KiroToolSpecification{
|
||||
Name: name,
|
||||
Description: description,
|
||||
InputSchema: KiroInputSchema{JSON: parameters},
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
return kiroTools
|
||||
}
|
||||
|
||||
// OpenAIStreamParams holds parameters for OpenAI streaming conversion
|
||||
type OpenAIStreamParams struct {
|
||||
State *OpenAIStreamState
|
||||
ThinkingState *ThinkingTagState
|
||||
ToolCallsEmitted map[string]bool
|
||||
}
|
||||
|
||||
// NewOpenAIStreamParams creates new streaming parameters
|
||||
func NewOpenAIStreamParams(model string) *OpenAIStreamParams {
|
||||
return &OpenAIStreamParams{
|
||||
State: NewOpenAIStreamState(model),
|
||||
ThinkingState: NewThinkingTagState(),
|
||||
ToolCallsEmitted: make(map[string]bool),
|
||||
}
|
||||
}
|
||||
|
||||
// ConvertClaudeToolUseToOpenAI converts a Claude tool_use block to OpenAI tool_calls format
|
||||
func ConvertClaudeToolUseToOpenAI(toolUseID, toolName string, input map[string]interface{}) map[string]interface{} {
|
||||
inputJSON, _ := json.Marshal(input)
|
||||
return map[string]interface{}{
|
||||
"id": toolUseID,
|
||||
"type": "function",
|
||||
"function": map[string]interface{}{
|
||||
"name": toolName,
|
||||
"arguments": string(inputJSON),
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
// LogStreamEvent logs a streaming event for debugging
|
||||
func LogStreamEvent(eventType, data string) {
|
||||
log.Debugf("kiro-openai: stream event type=%s, data_len=%d", eventType, len(data))
|
||||
}
|
||||
@@ -0,0 +1,604 @@
|
||||
// Package openai provides request translation from OpenAI Chat Completions to Kiro format.
|
||||
// It handles parsing and transforming OpenAI API requests into the Kiro/Amazon Q API format,
|
||||
// extracting model information, system instructions, message contents, and tool declarations.
|
||||
package openai
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"strings"
|
||||
"time"
|
||||
"unicode/utf8"
|
||||
|
||||
"github.com/google/uuid"
|
||||
kirocommon "github.com/router-for-me/CLIProxyAPI/v6/internal/translator/kiro/common"
|
||||
log "github.com/sirupsen/logrus"
|
||||
"github.com/tidwall/gjson"
|
||||
)
|
||||
|
||||
// Kiro API request structs - reuse from kiroclaude package structure
|
||||
|
||||
// KiroPayload is the top-level request structure for Kiro API
|
||||
type KiroPayload struct {
|
||||
ConversationState KiroConversationState `json:"conversationState"`
|
||||
ProfileArn string `json:"profileArn,omitempty"`
|
||||
InferenceConfig *KiroInferenceConfig `json:"inferenceConfig,omitempty"`
|
||||
}
|
||||
|
||||
// KiroInferenceConfig contains inference parameters for the Kiro API.
|
||||
type KiroInferenceConfig struct {
|
||||
MaxTokens int `json:"maxTokens,omitempty"`
|
||||
Temperature float64 `json:"temperature,omitempty"`
|
||||
}
|
||||
|
||||
// KiroConversationState holds the conversation context
|
||||
type KiroConversationState struct {
|
||||
ChatTriggerType string `json:"chatTriggerType"` // Required: "MANUAL"
|
||||
ConversationID string `json:"conversationId"`
|
||||
CurrentMessage KiroCurrentMessage `json:"currentMessage"`
|
||||
History []KiroHistoryMessage `json:"history,omitempty"`
|
||||
}
|
||||
|
||||
// KiroCurrentMessage wraps the current user message
|
||||
type KiroCurrentMessage struct {
|
||||
UserInputMessage KiroUserInputMessage `json:"userInputMessage"`
|
||||
}
|
||||
|
||||
// KiroHistoryMessage represents a message in the conversation history
|
||||
type KiroHistoryMessage struct {
|
||||
UserInputMessage *KiroUserInputMessage `json:"userInputMessage,omitempty"`
|
||||
AssistantResponseMessage *KiroAssistantResponseMessage `json:"assistantResponseMessage,omitempty"`
|
||||
}
|
||||
|
||||
// KiroImage represents an image in Kiro API format
|
||||
type KiroImage struct {
|
||||
Format string `json:"format"`
|
||||
Source KiroImageSource `json:"source"`
|
||||
}
|
||||
|
||||
// KiroImageSource contains the image data
|
||||
type KiroImageSource struct {
|
||||
Bytes string `json:"bytes"` // base64 encoded image data
|
||||
}
|
||||
|
||||
// KiroUserInputMessage represents a user message
|
||||
type KiroUserInputMessage struct {
|
||||
Content string `json:"content"`
|
||||
ModelID string `json:"modelId"`
|
||||
Origin string `json:"origin"`
|
||||
Images []KiroImage `json:"images,omitempty"`
|
||||
UserInputMessageContext *KiroUserInputMessageContext `json:"userInputMessageContext,omitempty"`
|
||||
}
|
||||
|
||||
// KiroUserInputMessageContext contains tool-related context
|
||||
type KiroUserInputMessageContext struct {
|
||||
ToolResults []KiroToolResult `json:"toolResults,omitempty"`
|
||||
Tools []KiroToolWrapper `json:"tools,omitempty"`
|
||||
}
|
||||
|
||||
// KiroToolResult represents a tool execution result
|
||||
type KiroToolResult struct {
|
||||
Content []KiroTextContent `json:"content"`
|
||||
Status string `json:"status"`
|
||||
ToolUseID string `json:"toolUseId"`
|
||||
}
|
||||
|
||||
// KiroTextContent represents text content
|
||||
type KiroTextContent struct {
|
||||
Text string `json:"text"`
|
||||
}
|
||||
|
||||
// KiroToolWrapper wraps a tool specification
|
||||
type KiroToolWrapper struct {
|
||||
ToolSpecification KiroToolSpecification `json:"toolSpecification"`
|
||||
}
|
||||
|
||||
// KiroToolSpecification defines a tool's schema
|
||||
type KiroToolSpecification struct {
|
||||
Name string `json:"name"`
|
||||
Description string `json:"description"`
|
||||
InputSchema KiroInputSchema `json:"inputSchema"`
|
||||
}
|
||||
|
||||
// KiroInputSchema wraps the JSON schema for tool input
|
||||
type KiroInputSchema struct {
|
||||
JSON interface{} `json:"json"`
|
||||
}
|
||||
|
||||
// KiroAssistantResponseMessage represents an assistant message
|
||||
type KiroAssistantResponseMessage struct {
|
||||
Content string `json:"content"`
|
||||
ToolUses []KiroToolUse `json:"toolUses,omitempty"`
|
||||
}
|
||||
|
||||
// KiroToolUse represents a tool invocation by the assistant
|
||||
type KiroToolUse struct {
|
||||
ToolUseID string `json:"toolUseId"`
|
||||
Name string `json:"name"`
|
||||
Input map[string]interface{} `json:"input"`
|
||||
}
|
||||
|
||||
// ConvertOpenAIRequestToKiro converts an OpenAI Chat Completions request to Kiro format.
|
||||
// This is the main entry point for request translation.
|
||||
// Note: The actual payload building happens in the executor, this just passes through
|
||||
// the OpenAI format which will be converted by BuildKiroPayloadFromOpenAI.
|
||||
func ConvertOpenAIRequestToKiro(modelName string, inputRawJSON []byte, stream bool) []byte {
|
||||
// Pass through the OpenAI format - actual conversion happens in BuildKiroPayloadFromOpenAI
|
||||
return inputRawJSON
|
||||
}
|
||||
|
||||
// BuildKiroPayloadFromOpenAI constructs the Kiro API request payload from OpenAI format.
|
||||
// Supports tool calling - tools are passed via userInputMessageContext.
|
||||
// origin parameter determines which quota to use: "CLI" for Amazon Q, "AI_EDITOR" for Kiro IDE.
|
||||
// isAgentic parameter enables chunked write optimization prompt for -agentic model variants.
|
||||
// isChatOnly parameter disables tool calling for -chat model variants (pure conversation mode).
|
||||
func BuildKiroPayloadFromOpenAI(openaiBody []byte, modelID, profileArn, origin string, isAgentic, isChatOnly bool) []byte {
|
||||
// Extract max_tokens for potential use in inferenceConfig
|
||||
var maxTokens int64
|
||||
if mt := gjson.GetBytes(openaiBody, "max_tokens"); mt.Exists() {
|
||||
maxTokens = mt.Int()
|
||||
}
|
||||
|
||||
// Extract temperature if specified
|
||||
var temperature float64
|
||||
var hasTemperature bool
|
||||
if temp := gjson.GetBytes(openaiBody, "temperature"); temp.Exists() {
|
||||
temperature = temp.Float()
|
||||
hasTemperature = true
|
||||
}
|
||||
|
||||
// Normalize origin value for Kiro API compatibility
|
||||
origin = normalizeOrigin(origin)
|
||||
log.Debugf("kiro-openai: normalized origin value: %s", origin)
|
||||
|
||||
messages := gjson.GetBytes(openaiBody, "messages")
|
||||
|
||||
// For chat-only mode, don't include tools
|
||||
var tools gjson.Result
|
||||
if !isChatOnly {
|
||||
tools = gjson.GetBytes(openaiBody, "tools")
|
||||
}
|
||||
|
||||
// Extract system prompt from messages
|
||||
systemPrompt := extractSystemPromptFromOpenAI(messages)
|
||||
|
||||
// Inject timestamp context
|
||||
timestamp := time.Now().Format("2006-01-02 15:04:05 MST")
|
||||
timestampContext := fmt.Sprintf("[Context: Current time is %s]", timestamp)
|
||||
if systemPrompt != "" {
|
||||
systemPrompt = timestampContext + "\n\n" + systemPrompt
|
||||
} else {
|
||||
systemPrompt = timestampContext
|
||||
}
|
||||
log.Debugf("kiro-openai: injected timestamp context: %s", timestamp)
|
||||
|
||||
// Inject agentic optimization prompt for -agentic model variants
|
||||
if isAgentic {
|
||||
if systemPrompt != "" {
|
||||
systemPrompt += "\n"
|
||||
}
|
||||
systemPrompt += kirocommon.KiroAgenticSystemPrompt
|
||||
}
|
||||
|
||||
// Convert OpenAI tools to Kiro format
|
||||
kiroTools := convertOpenAIToolsToKiro(tools)
|
||||
|
||||
// Process messages and build history
|
||||
history, currentUserMsg, currentToolResults := processOpenAIMessages(messages, modelID, origin)
|
||||
|
||||
// Build content with system prompt
|
||||
if currentUserMsg != nil {
|
||||
currentUserMsg.Content = buildFinalContent(currentUserMsg.Content, systemPrompt, currentToolResults)
|
||||
|
||||
// Deduplicate currentToolResults
|
||||
currentToolResults = deduplicateToolResults(currentToolResults)
|
||||
|
||||
// Build userInputMessageContext with tools and tool results
|
||||
if len(kiroTools) > 0 || len(currentToolResults) > 0 {
|
||||
currentUserMsg.UserInputMessageContext = &KiroUserInputMessageContext{
|
||||
Tools: kiroTools,
|
||||
ToolResults: currentToolResults,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Build payload
|
||||
var currentMessage KiroCurrentMessage
|
||||
if currentUserMsg != nil {
|
||||
currentMessage = KiroCurrentMessage{UserInputMessage: *currentUserMsg}
|
||||
} else {
|
||||
fallbackContent := ""
|
||||
if systemPrompt != "" {
|
||||
fallbackContent = "--- SYSTEM PROMPT ---\n" + systemPrompt + "\n--- END SYSTEM PROMPT ---\n"
|
||||
}
|
||||
currentMessage = KiroCurrentMessage{UserInputMessage: KiroUserInputMessage{
|
||||
Content: fallbackContent,
|
||||
ModelID: modelID,
|
||||
Origin: origin,
|
||||
}}
|
||||
}
|
||||
|
||||
// Build inferenceConfig if we have any inference parameters
|
||||
var inferenceConfig *KiroInferenceConfig
|
||||
if maxTokens > 0 || hasTemperature {
|
||||
inferenceConfig = &KiroInferenceConfig{}
|
||||
if maxTokens > 0 {
|
||||
inferenceConfig.MaxTokens = int(maxTokens)
|
||||
}
|
||||
if hasTemperature {
|
||||
inferenceConfig.Temperature = temperature
|
||||
}
|
||||
}
|
||||
|
||||
payload := KiroPayload{
|
||||
ConversationState: KiroConversationState{
|
||||
ChatTriggerType: "MANUAL",
|
||||
ConversationID: uuid.New().String(),
|
||||
CurrentMessage: currentMessage,
|
||||
History: history,
|
||||
},
|
||||
ProfileArn: profileArn,
|
||||
InferenceConfig: inferenceConfig,
|
||||
}
|
||||
|
||||
result, err := json.Marshal(payload)
|
||||
if err != nil {
|
||||
log.Debugf("kiro-openai: failed to marshal payload: %v", err)
|
||||
return nil
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
// normalizeOrigin normalizes origin value for Kiro API compatibility
|
||||
func normalizeOrigin(origin string) string {
|
||||
switch origin {
|
||||
case "KIRO_CLI":
|
||||
return "CLI"
|
||||
case "KIRO_AI_EDITOR":
|
||||
return "AI_EDITOR"
|
||||
case "AMAZON_Q":
|
||||
return "CLI"
|
||||
case "KIRO_IDE":
|
||||
return "AI_EDITOR"
|
||||
default:
|
||||
return origin
|
||||
}
|
||||
}
|
||||
|
||||
// extractSystemPromptFromOpenAI extracts system prompt from OpenAI messages
|
||||
func extractSystemPromptFromOpenAI(messages gjson.Result) string {
|
||||
if !messages.IsArray() {
|
||||
return ""
|
||||
}
|
||||
|
||||
var systemParts []string
|
||||
for _, msg := range messages.Array() {
|
||||
if msg.Get("role").String() == "system" {
|
||||
content := msg.Get("content")
|
||||
if content.Type == gjson.String {
|
||||
systemParts = append(systemParts, content.String())
|
||||
} else if content.IsArray() {
|
||||
// Handle array content format
|
||||
for _, part := range content.Array() {
|
||||
if part.Get("type").String() == "text" {
|
||||
systemParts = append(systemParts, part.Get("text").String())
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return strings.Join(systemParts, "\n")
|
||||
}
|
||||
|
||||
// convertOpenAIToolsToKiro converts OpenAI tools to Kiro format
|
||||
func convertOpenAIToolsToKiro(tools gjson.Result) []KiroToolWrapper {
|
||||
var kiroTools []KiroToolWrapper
|
||||
if !tools.IsArray() {
|
||||
return kiroTools
|
||||
}
|
||||
|
||||
for _, tool := range tools.Array() {
|
||||
// OpenAI tools have type "function" with function definition inside
|
||||
if tool.Get("type").String() != "function" {
|
||||
continue
|
||||
}
|
||||
|
||||
fn := tool.Get("function")
|
||||
if !fn.Exists() {
|
||||
continue
|
||||
}
|
||||
|
||||
name := fn.Get("name").String()
|
||||
description := fn.Get("description").String()
|
||||
parameters := fn.Get("parameters").Value()
|
||||
|
||||
// CRITICAL FIX: Kiro API requires non-empty description
|
||||
if strings.TrimSpace(description) == "" {
|
||||
description = fmt.Sprintf("Tool: %s", name)
|
||||
log.Debugf("kiro-openai: tool '%s' has empty description, using default: %s", name, description)
|
||||
}
|
||||
|
||||
// Truncate long descriptions
|
||||
if len(description) > kirocommon.KiroMaxToolDescLen {
|
||||
truncLen := kirocommon.KiroMaxToolDescLen - 30
|
||||
for truncLen > 0 && !utf8.RuneStart(description[truncLen]) {
|
||||
truncLen--
|
||||
}
|
||||
description = description[:truncLen] + "... (description truncated)"
|
||||
}
|
||||
|
||||
kiroTools = append(kiroTools, KiroToolWrapper{
|
||||
ToolSpecification: KiroToolSpecification{
|
||||
Name: name,
|
||||
Description: description,
|
||||
InputSchema: KiroInputSchema{JSON: parameters},
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
return kiroTools
|
||||
}
|
||||
|
||||
// processOpenAIMessages processes OpenAI messages and builds Kiro history
|
||||
func processOpenAIMessages(messages gjson.Result, modelID, origin string) ([]KiroHistoryMessage, *KiroUserInputMessage, []KiroToolResult) {
|
||||
var history []KiroHistoryMessage
|
||||
var currentUserMsg *KiroUserInputMessage
|
||||
var currentToolResults []KiroToolResult
|
||||
|
||||
if !messages.IsArray() {
|
||||
return history, currentUserMsg, currentToolResults
|
||||
}
|
||||
|
||||
// Merge adjacent messages with the same role
|
||||
messagesArray := kirocommon.MergeAdjacentMessages(messages.Array())
|
||||
|
||||
// Build tool_call_id to name mapping from assistant messages
|
||||
toolCallIDToName := make(map[string]string)
|
||||
for _, msg := range messagesArray {
|
||||
if msg.Get("role").String() == "assistant" {
|
||||
toolCalls := msg.Get("tool_calls")
|
||||
if toolCalls.IsArray() {
|
||||
for _, tc := range toolCalls.Array() {
|
||||
if tc.Get("type").String() == "function" {
|
||||
id := tc.Get("id").String()
|
||||
name := tc.Get("function.name").String()
|
||||
if id != "" && name != "" {
|
||||
toolCallIDToName[id] = name
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for i, msg := range messagesArray {
|
||||
role := msg.Get("role").String()
|
||||
isLastMessage := i == len(messagesArray)-1
|
||||
|
||||
switch role {
|
||||
case "system":
|
||||
// System messages are handled separately via extractSystemPromptFromOpenAI
|
||||
continue
|
||||
|
||||
case "user":
|
||||
userMsg, toolResults := buildUserMessageFromOpenAI(msg, modelID, origin)
|
||||
if isLastMessage {
|
||||
currentUserMsg = &userMsg
|
||||
currentToolResults = toolResults
|
||||
} else {
|
||||
// CRITICAL: Kiro API requires content to be non-empty for history messages
|
||||
if strings.TrimSpace(userMsg.Content) == "" {
|
||||
if len(toolResults) > 0 {
|
||||
userMsg.Content = "Tool results provided."
|
||||
} else {
|
||||
userMsg.Content = "Continue"
|
||||
}
|
||||
}
|
||||
// For history messages, embed tool results in context
|
||||
if len(toolResults) > 0 {
|
||||
userMsg.UserInputMessageContext = &KiroUserInputMessageContext{
|
||||
ToolResults: toolResults,
|
||||
}
|
||||
}
|
||||
history = append(history, KiroHistoryMessage{
|
||||
UserInputMessage: &userMsg,
|
||||
})
|
||||
}
|
||||
|
||||
case "assistant":
|
||||
assistantMsg := buildAssistantMessageFromOpenAI(msg)
|
||||
if isLastMessage {
|
||||
history = append(history, KiroHistoryMessage{
|
||||
AssistantResponseMessage: &assistantMsg,
|
||||
})
|
||||
// Create a "Continue" user message as currentMessage
|
||||
currentUserMsg = &KiroUserInputMessage{
|
||||
Content: "Continue",
|
||||
ModelID: modelID,
|
||||
Origin: origin,
|
||||
}
|
||||
} else {
|
||||
history = append(history, KiroHistoryMessage{
|
||||
AssistantResponseMessage: &assistantMsg,
|
||||
})
|
||||
}
|
||||
|
||||
case "tool":
|
||||
// Tool messages in OpenAI format provide results for tool_calls
|
||||
// These are typically followed by user or assistant messages
|
||||
// Process them and merge into the next user message's tool results
|
||||
toolCallID := msg.Get("tool_call_id").String()
|
||||
content := msg.Get("content").String()
|
||||
|
||||
if toolCallID != "" {
|
||||
toolResult := KiroToolResult{
|
||||
ToolUseID: toolCallID,
|
||||
Content: []KiroTextContent{{Text: content}},
|
||||
Status: "success",
|
||||
}
|
||||
// Tool results should be included in the next user message
|
||||
// For now, collect them and they'll be handled when we build the current message
|
||||
currentToolResults = append(currentToolResults, toolResult)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return history, currentUserMsg, currentToolResults
|
||||
}
|
||||
|
||||
// buildUserMessageFromOpenAI builds a user message from OpenAI format and extracts tool results
|
||||
func buildUserMessageFromOpenAI(msg gjson.Result, modelID, origin string) (KiroUserInputMessage, []KiroToolResult) {
|
||||
content := msg.Get("content")
|
||||
var contentBuilder strings.Builder
|
||||
var toolResults []KiroToolResult
|
||||
var images []KiroImage
|
||||
|
||||
// Track seen toolCallIds to deduplicate
|
||||
seenToolCallIDs := make(map[string]bool)
|
||||
|
||||
if content.IsArray() {
|
||||
for _, part := range content.Array() {
|
||||
partType := part.Get("type").String()
|
||||
switch partType {
|
||||
case "text":
|
||||
contentBuilder.WriteString(part.Get("text").String())
|
||||
case "image_url":
|
||||
imageURL := part.Get("image_url.url").String()
|
||||
if strings.HasPrefix(imageURL, "data:") {
|
||||
// Parse data URL: data:image/png;base64,xxxxx
|
||||
if idx := strings.Index(imageURL, ";base64,"); idx != -1 {
|
||||
mediaType := imageURL[5:idx] // Skip "data:"
|
||||
data := imageURL[idx+8:] // Skip ";base64,"
|
||||
|
||||
format := ""
|
||||
if lastSlash := strings.LastIndex(mediaType, "/"); lastSlash != -1 {
|
||||
format = mediaType[lastSlash+1:]
|
||||
}
|
||||
|
||||
if format != "" && data != "" {
|
||||
images = append(images, KiroImage{
|
||||
Format: format,
|
||||
Source: KiroImageSource{
|
||||
Bytes: data,
|
||||
},
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if content.Type == gjson.String {
|
||||
contentBuilder.WriteString(content.String())
|
||||
}
|
||||
|
||||
// Check for tool_calls in the message (shouldn't be in user messages, but handle edge cases)
|
||||
_ = seenToolCallIDs // Used for deduplication if needed
|
||||
|
||||
userMsg := KiroUserInputMessage{
|
||||
Content: contentBuilder.String(),
|
||||
ModelID: modelID,
|
||||
Origin: origin,
|
||||
}
|
||||
|
||||
if len(images) > 0 {
|
||||
userMsg.Images = images
|
||||
}
|
||||
|
||||
return userMsg, toolResults
|
||||
}
|
||||
|
||||
// buildAssistantMessageFromOpenAI builds an assistant message from OpenAI format
|
||||
func buildAssistantMessageFromOpenAI(msg gjson.Result) KiroAssistantResponseMessage {
|
||||
content := msg.Get("content")
|
||||
var contentBuilder strings.Builder
|
||||
var toolUses []KiroToolUse
|
||||
|
||||
// Handle content
|
||||
if content.Type == gjson.String {
|
||||
contentBuilder.WriteString(content.String())
|
||||
} else if content.IsArray() {
|
||||
for _, part := range content.Array() {
|
||||
if part.Get("type").String() == "text" {
|
||||
contentBuilder.WriteString(part.Get("text").String())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Handle tool_calls
|
||||
toolCalls := msg.Get("tool_calls")
|
||||
if toolCalls.IsArray() {
|
||||
for _, tc := range toolCalls.Array() {
|
||||
if tc.Get("type").String() != "function" {
|
||||
continue
|
||||
}
|
||||
|
||||
toolUseID := tc.Get("id").String()
|
||||
toolName := tc.Get("function.name").String()
|
||||
toolArgs := tc.Get("function.arguments").String()
|
||||
|
||||
var inputMap map[string]interface{}
|
||||
if err := json.Unmarshal([]byte(toolArgs), &inputMap); err != nil {
|
||||
log.Debugf("kiro-openai: failed to parse tool arguments: %v", err)
|
||||
inputMap = make(map[string]interface{})
|
||||
}
|
||||
|
||||
toolUses = append(toolUses, KiroToolUse{
|
||||
ToolUseID: toolUseID,
|
||||
Name: toolName,
|
||||
Input: inputMap,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return KiroAssistantResponseMessage{
|
||||
Content: contentBuilder.String(),
|
||||
ToolUses: toolUses,
|
||||
}
|
||||
}
|
||||
|
||||
// buildFinalContent builds the final content with system prompt
|
||||
func buildFinalContent(content, systemPrompt string, toolResults []KiroToolResult) string {
|
||||
var contentBuilder strings.Builder
|
||||
|
||||
if systemPrompt != "" {
|
||||
contentBuilder.WriteString("--- SYSTEM PROMPT ---\n")
|
||||
contentBuilder.WriteString(systemPrompt)
|
||||
contentBuilder.WriteString("\n--- END SYSTEM PROMPT ---\n\n")
|
||||
}
|
||||
|
||||
contentBuilder.WriteString(content)
|
||||
finalContent := contentBuilder.String()
|
||||
|
||||
// CRITICAL: Kiro API requires content to be non-empty
|
||||
if strings.TrimSpace(finalContent) == "" {
|
||||
if len(toolResults) > 0 {
|
||||
finalContent = "Tool results provided."
|
||||
} else {
|
||||
finalContent = "Continue"
|
||||
}
|
||||
log.Debugf("kiro-openai: content was empty, using default: %s", finalContent)
|
||||
}
|
||||
|
||||
return finalContent
|
||||
}
|
||||
|
||||
// deduplicateToolResults removes duplicate tool results
|
||||
func deduplicateToolResults(toolResults []KiroToolResult) []KiroToolResult {
|
||||
if len(toolResults) == 0 {
|
||||
return toolResults
|
||||
}
|
||||
|
||||
seenIDs := make(map[string]bool)
|
||||
unique := make([]KiroToolResult, 0, len(toolResults))
|
||||
for _, tr := range toolResults {
|
||||
if !seenIDs[tr.ToolUseID] {
|
||||
seenIDs[tr.ToolUseID] = true
|
||||
unique = append(unique, tr)
|
||||
} else {
|
||||
log.Debugf("kiro-openai: skipping duplicate toolResult: %s", tr.ToolUseID)
|
||||
}
|
||||
}
|
||||
return unique
|
||||
}
|
||||
@@ -0,0 +1,264 @@
|
||||
// Package openai provides response translation from Kiro to OpenAI format.
|
||||
// This package handles the conversion of Kiro API responses into OpenAI Chat Completions-compatible
|
||||
// JSON format, transforming streaming events and non-streaming responses.
|
||||
package openai
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"sync/atomic"
|
||||
"time"
|
||||
|
||||
"github.com/google/uuid"
|
||||
"github.com/router-for-me/CLIProxyAPI/v6/sdk/cliproxy/usage"
|
||||
log "github.com/sirupsen/logrus"
|
||||
)
|
||||
|
||||
// functionCallIDCounter provides a process-wide unique counter for function call identifiers.
|
||||
var functionCallIDCounter uint64
|
||||
|
||||
// BuildOpenAIResponse constructs an OpenAI Chat Completions-compatible response.
|
||||
// Supports tool_calls when tools are present in the response.
|
||||
// stopReason is passed from upstream; fallback logic applied if empty.
|
||||
func BuildOpenAIResponse(content string, toolUses []KiroToolUse, model string, usageInfo usage.Detail, stopReason string) []byte {
|
||||
// Build the message object
|
||||
message := map[string]interface{}{
|
||||
"role": "assistant",
|
||||
"content": content,
|
||||
}
|
||||
|
||||
// Add tool_calls if present
|
||||
if len(toolUses) > 0 {
|
||||
var toolCalls []map[string]interface{}
|
||||
for i, tu := range toolUses {
|
||||
inputJSON, _ := json.Marshal(tu.Input)
|
||||
toolCalls = append(toolCalls, map[string]interface{}{
|
||||
"id": tu.ToolUseID,
|
||||
"type": "function",
|
||||
"index": i,
|
||||
"function": map[string]interface{}{
|
||||
"name": tu.Name,
|
||||
"arguments": string(inputJSON),
|
||||
},
|
||||
})
|
||||
}
|
||||
message["tool_calls"] = toolCalls
|
||||
// When tool_calls are present, content should be null according to OpenAI spec
|
||||
if content == "" {
|
||||
message["content"] = nil
|
||||
}
|
||||
}
|
||||
|
||||
// Use upstream stopReason; apply fallback logic if not provided
|
||||
finishReason := mapKiroStopReasonToOpenAI(stopReason)
|
||||
if finishReason == "" {
|
||||
finishReason = "stop"
|
||||
if len(toolUses) > 0 {
|
||||
finishReason = "tool_calls"
|
||||
}
|
||||
log.Debugf("kiro-openai: buildOpenAIResponse using fallback finish_reason: %s", finishReason)
|
||||
}
|
||||
|
||||
response := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:24],
|
||||
"object": "chat.completion",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{
|
||||
{
|
||||
"index": 0,
|
||||
"message": message,
|
||||
"finish_reason": finishReason,
|
||||
},
|
||||
},
|
||||
"usage": map[string]interface{}{
|
||||
"prompt_tokens": usageInfo.InputTokens,
|
||||
"completion_tokens": usageInfo.OutputTokens,
|
||||
"total_tokens": usageInfo.InputTokens + usageInfo.OutputTokens,
|
||||
},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(response)
|
||||
return result
|
||||
}
|
||||
|
||||
// mapKiroStopReasonToOpenAI converts Kiro/Claude stop_reason to OpenAI finish_reason
|
||||
func mapKiroStopReasonToOpenAI(stopReason string) string {
|
||||
switch stopReason {
|
||||
case "end_turn":
|
||||
return "stop"
|
||||
case "stop_sequence":
|
||||
return "stop"
|
||||
case "tool_use":
|
||||
return "tool_calls"
|
||||
case "max_tokens":
|
||||
return "length"
|
||||
case "content_filtered":
|
||||
return "content_filter"
|
||||
default:
|
||||
return stopReason
|
||||
}
|
||||
}
|
||||
|
||||
// BuildOpenAIStreamChunk constructs an OpenAI Chat Completions streaming chunk.
|
||||
// This is the delta format used in streaming responses.
|
||||
func BuildOpenAIStreamChunk(model string, deltaContent string, deltaToolCalls []map[string]interface{}, finishReason string, index int) []byte {
|
||||
delta := map[string]interface{}{}
|
||||
|
||||
// First chunk should include role
|
||||
if index == 0 && deltaContent == "" && len(deltaToolCalls) == 0 {
|
||||
delta["role"] = "assistant"
|
||||
delta["content"] = ""
|
||||
} else if deltaContent != "" {
|
||||
delta["content"] = deltaContent
|
||||
}
|
||||
|
||||
// Add tool_calls delta if present
|
||||
if len(deltaToolCalls) > 0 {
|
||||
delta["tool_calls"] = deltaToolCalls
|
||||
}
|
||||
|
||||
choice := map[string]interface{}{
|
||||
"index": 0,
|
||||
"delta": delta,
|
||||
}
|
||||
|
||||
if finishReason != "" {
|
||||
choice["finish_reason"] = finishReason
|
||||
} else {
|
||||
choice["finish_reason"] = nil
|
||||
}
|
||||
|
||||
chunk := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:12],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{choice},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(chunk)
|
||||
return result
|
||||
}
|
||||
|
||||
// BuildOpenAIStreamChunkWithToolCallStart creates a stream chunk for tool call start
|
||||
func BuildOpenAIStreamChunkWithToolCallStart(model string, toolUseID, toolName string, toolIndex int) []byte {
|
||||
toolCall := map[string]interface{}{
|
||||
"index": toolIndex,
|
||||
"id": toolUseID,
|
||||
"type": "function",
|
||||
"function": map[string]interface{}{
|
||||
"name": toolName,
|
||||
"arguments": "",
|
||||
},
|
||||
}
|
||||
|
||||
delta := map[string]interface{}{
|
||||
"tool_calls": []map[string]interface{}{toolCall},
|
||||
}
|
||||
|
||||
choice := map[string]interface{}{
|
||||
"index": 0,
|
||||
"delta": delta,
|
||||
"finish_reason": nil,
|
||||
}
|
||||
|
||||
chunk := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:12],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{choice},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(chunk)
|
||||
return result
|
||||
}
|
||||
|
||||
// BuildOpenAIStreamChunkWithToolCallDelta creates a stream chunk for tool call arguments delta
|
||||
func BuildOpenAIStreamChunkWithToolCallDelta(model string, argumentsDelta string, toolIndex int) []byte {
|
||||
toolCall := map[string]interface{}{
|
||||
"index": toolIndex,
|
||||
"function": map[string]interface{}{
|
||||
"arguments": argumentsDelta,
|
||||
},
|
||||
}
|
||||
|
||||
delta := map[string]interface{}{
|
||||
"tool_calls": []map[string]interface{}{toolCall},
|
||||
}
|
||||
|
||||
choice := map[string]interface{}{
|
||||
"index": 0,
|
||||
"delta": delta,
|
||||
"finish_reason": nil,
|
||||
}
|
||||
|
||||
chunk := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:12],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{choice},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(chunk)
|
||||
return result
|
||||
}
|
||||
|
||||
// BuildOpenAIStreamDoneChunk creates the final [DONE] stream event
|
||||
func BuildOpenAIStreamDoneChunk() []byte {
|
||||
return []byte("data: [DONE]")
|
||||
}
|
||||
|
||||
// BuildOpenAIStreamFinishChunk creates the final chunk with finish_reason
|
||||
func BuildOpenAIStreamFinishChunk(model string, finishReason string) []byte {
|
||||
choice := map[string]interface{}{
|
||||
"index": 0,
|
||||
"delta": map[string]interface{}{},
|
||||
"finish_reason": finishReason,
|
||||
}
|
||||
|
||||
chunk := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:12],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{choice},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(chunk)
|
||||
return result
|
||||
}
|
||||
|
||||
// BuildOpenAIStreamUsageChunk creates a chunk with usage information (optional, for stream_options.include_usage)
|
||||
func BuildOpenAIStreamUsageChunk(model string, usageInfo usage.Detail) []byte {
|
||||
chunk := map[string]interface{}{
|
||||
"id": "chatcmpl-" + uuid.New().String()[:12],
|
||||
"object": "chat.completion.chunk",
|
||||
"created": time.Now().Unix(),
|
||||
"model": model,
|
||||
"choices": []map[string]interface{}{},
|
||||
"usage": map[string]interface{}{
|
||||
"prompt_tokens": usageInfo.InputTokens,
|
||||
"completion_tokens": usageInfo.OutputTokens,
|
||||
"total_tokens": usageInfo.InputTokens + usageInfo.OutputTokens,
|
||||
},
|
||||
}
|
||||
|
||||
result, _ := json.Marshal(chunk)
|
||||
return result
|
||||
}
|
||||
|
||||
// GenerateToolCallID generates a unique tool call ID in OpenAI format
|
||||
func GenerateToolCallID(toolName string) string {
|
||||
return fmt.Sprintf("call_%s_%d_%d", toolName[:min(8, len(toolName))], time.Now().UnixNano(), atomic.AddUint64(&functionCallIDCounter, 1))
|
||||
}
|
||||
|
||||
// min returns the minimum of two integers
|
||||
func min(a, b int) int {
|
||||
if a < b {
|
||||
return a
|
||||
}
|
||||
return b
|
||||
}
|
||||
@@ -0,0 +1,207 @@
|
||||
// Package openai provides streaming SSE event building for OpenAI format.
|
||||
// This package handles the construction of OpenAI-compatible Server-Sent Events (SSE)
|
||||
// for streaming responses from Kiro API.
|
||||
package openai
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"time"
|
||||
|
||||
"github.com/google/uuid"
|
||||
"github.com/router-for-me/CLIProxyAPI/v6/sdk/cliproxy/usage"
|
||||
)
|
||||
|
||||
// OpenAIStreamState tracks the state of streaming response conversion
|
||||
type OpenAIStreamState struct {
|
||||
ChunkIndex int
|
||||
ToolCallIndex int
|
||||
HasSentFirstChunk bool
|
||||
Model string
|
||||
ResponseID string
|
||||
Created int64
|
||||
}
|
||||
|
||||
// NewOpenAIStreamState creates a new stream state for tracking
|
||||
func NewOpenAIStreamState(model string) *OpenAIStreamState {
|
||||
return &OpenAIStreamState{
|
||||
ChunkIndex: 0,
|
||||
ToolCallIndex: 0,
|
||||
HasSentFirstChunk: false,
|
||||
Model: model,
|
||||
ResponseID: "chatcmpl-" + uuid.New().String()[:24],
|
||||
Created: time.Now().Unix(),
|
||||
}
|
||||
}
|
||||
|
||||
// FormatSSEEvent formats a JSON payload as an SSE event
|
||||
func FormatSSEEvent(data []byte) string {
|
||||
return fmt.Sprintf("data: %s", string(data))
|
||||
}
|
||||
|
||||
// BuildOpenAISSETextDelta creates an SSE event for text content delta
|
||||
func BuildOpenAISSETextDelta(state *OpenAIStreamState, textDelta string) string {
|
||||
delta := map[string]interface{}{
|
||||
"content": textDelta,
|
||||
}
|
||||
|
||||
// Include role in first chunk
|
||||
if !state.HasSentFirstChunk {
|
||||
delta["role"] = "assistant"
|
||||
state.HasSentFirstChunk = true
|
||||
}
|
||||
|
||||
chunk := buildBaseChunk(state, delta, nil)
|
||||
result, _ := json.Marshal(chunk)
|
||||
state.ChunkIndex++
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// BuildOpenAISSEToolCallStart creates an SSE event for tool call start
|
||||
func BuildOpenAISSEToolCallStart(state *OpenAIStreamState, toolUseID, toolName string) string {
|
||||
toolCall := map[string]interface{}{
|
||||
"index": state.ToolCallIndex,
|
||||
"id": toolUseID,
|
||||
"type": "function",
|
||||
"function": map[string]interface{}{
|
||||
"name": toolName,
|
||||
"arguments": "",
|
||||
},
|
||||
}
|
||||
|
||||
delta := map[string]interface{}{
|
||||
"tool_calls": []map[string]interface{}{toolCall},
|
||||
}
|
||||
|
||||
// Include role in first chunk if not sent yet
|
||||
if !state.HasSentFirstChunk {
|
||||
delta["role"] = "assistant"
|
||||
state.HasSentFirstChunk = true
|
||||
}
|
||||
|
||||
chunk := buildBaseChunk(state, delta, nil)
|
||||
result, _ := json.Marshal(chunk)
|
||||
state.ChunkIndex++
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// BuildOpenAISSEToolCallArgumentsDelta creates an SSE event for tool call arguments delta
|
||||
func BuildOpenAISSEToolCallArgumentsDelta(state *OpenAIStreamState, argumentsDelta string, toolIndex int) string {
|
||||
toolCall := map[string]interface{}{
|
||||
"index": toolIndex,
|
||||
"function": map[string]interface{}{
|
||||
"arguments": argumentsDelta,
|
||||
},
|
||||
}
|
||||
|
||||
delta := map[string]interface{}{
|
||||
"tool_calls": []map[string]interface{}{toolCall},
|
||||
}
|
||||
|
||||
chunk := buildBaseChunk(state, delta, nil)
|
||||
result, _ := json.Marshal(chunk)
|
||||
state.ChunkIndex++
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// BuildOpenAISSEFinish creates an SSE event with finish_reason
|
||||
func BuildOpenAISSEFinish(state *OpenAIStreamState, finishReason string) string {
|
||||
chunk := buildBaseChunk(state, map[string]interface{}{}, &finishReason)
|
||||
result, _ := json.Marshal(chunk)
|
||||
state.ChunkIndex++
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// BuildOpenAISSEUsage creates an SSE event with usage information
|
||||
func BuildOpenAISSEUsage(state *OpenAIStreamState, usageInfo usage.Detail) string {
|
||||
chunk := map[string]interface{}{
|
||||
"id": state.ResponseID,
|
||||
"object": "chat.completion.chunk",
|
||||
"created": state.Created,
|
||||
"model": state.Model,
|
||||
"choices": []map[string]interface{}{},
|
||||
"usage": map[string]interface{}{
|
||||
"prompt_tokens": usageInfo.InputTokens,
|
||||
"completion_tokens": usageInfo.OutputTokens,
|
||||
"total_tokens": usageInfo.InputTokens + usageInfo.OutputTokens,
|
||||
},
|
||||
}
|
||||
result, _ := json.Marshal(chunk)
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// BuildOpenAISSEDone creates the final [DONE] SSE event
|
||||
func BuildOpenAISSEDone() string {
|
||||
return "data: [DONE]"
|
||||
}
|
||||
|
||||
// buildBaseChunk creates a base chunk structure for streaming
|
||||
func buildBaseChunk(state *OpenAIStreamState, delta map[string]interface{}, finishReason *string) map[string]interface{} {
|
||||
choice := map[string]interface{}{
|
||||
"index": 0,
|
||||
"delta": delta,
|
||||
}
|
||||
|
||||
if finishReason != nil {
|
||||
choice["finish_reason"] = *finishReason
|
||||
} else {
|
||||
choice["finish_reason"] = nil
|
||||
}
|
||||
|
||||
return map[string]interface{}{
|
||||
"id": state.ResponseID,
|
||||
"object": "chat.completion.chunk",
|
||||
"created": state.Created,
|
||||
"model": state.Model,
|
||||
"choices": []map[string]interface{}{choice},
|
||||
}
|
||||
}
|
||||
|
||||
// BuildOpenAISSEReasoningDelta creates an SSE event for reasoning content delta
|
||||
// This is used for o1/o3 style models that expose reasoning tokens
|
||||
func BuildOpenAISSEReasoningDelta(state *OpenAIStreamState, reasoningDelta string) string {
|
||||
delta := map[string]interface{}{
|
||||
"reasoning_content": reasoningDelta,
|
||||
}
|
||||
|
||||
// Include role in first chunk
|
||||
if !state.HasSentFirstChunk {
|
||||
delta["role"] = "assistant"
|
||||
state.HasSentFirstChunk = true
|
||||
}
|
||||
|
||||
chunk := buildBaseChunk(state, delta, nil)
|
||||
result, _ := json.Marshal(chunk)
|
||||
state.ChunkIndex++
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// BuildOpenAISSEFirstChunk creates the first chunk with role only
|
||||
func BuildOpenAISSEFirstChunk(state *OpenAIStreamState) string {
|
||||
delta := map[string]interface{}{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
}
|
||||
|
||||
state.HasSentFirstChunk = true
|
||||
chunk := buildBaseChunk(state, delta, nil)
|
||||
result, _ := json.Marshal(chunk)
|
||||
state.ChunkIndex++
|
||||
return FormatSSEEvent(result)
|
||||
}
|
||||
|
||||
// ThinkingTagState tracks state for thinking tag detection in streaming
|
||||
type ThinkingTagState struct {
|
||||
InThinkingBlock bool
|
||||
PendingStartChars int
|
||||
PendingEndChars int
|
||||
}
|
||||
|
||||
// NewThinkingTagState creates a new thinking tag state
|
||||
func NewThinkingTagState() *ThinkingTagState {
|
||||
return &ThinkingTagState{
|
||||
InThinkingBlock: false,
|
||||
PendingStartChars: 0,
|
||||
PendingEndChars: 0,
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user