File size: 4,918 Bytes
8d3471e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | package util
import (
"ds2api/internal/toolcall"
"fmt"
"strings"
"time"
"github.com/google/uuid"
)
// BuildOpenAIChatCompletion is kept for backward compatibility.
// Prefer internal/format/openai.BuildChatCompletion for new code.
func BuildOpenAIChatCompletion(completionID, model, finalPrompt, finalThinking, finalText string, toolNames []string) map[string]any {
detected := toolcall.ParseToolCalls(finalText, toolNames)
finishReason := "stop"
messageObj := map[string]any{"role": "assistant", "content": finalText}
if strings.TrimSpace(finalThinking) != "" {
messageObj["reasoning_content"] = finalThinking
}
if len(detected) > 0 {
finishReason = "tool_calls"
messageObj["tool_calls"] = toolcall.FormatOpenAIToolCalls(detected, nil)
messageObj["content"] = nil
}
promptTokens := CountPromptTokens(finalPrompt, model)
reasoningTokens := CountOutputTokens(finalThinking, model)
completionTokens := CountOutputTokens(finalText, model)
return map[string]any{
"id": completionID,
"object": "chat.completion",
"created": time.Now().Unix(),
"model": model,
"choices": []map[string]any{{"index": 0, "message": messageObj, "finish_reason": finishReason}},
"usage": map[string]any{
"prompt_tokens": promptTokens,
"completion_tokens": reasoningTokens + completionTokens,
"total_tokens": promptTokens + reasoningTokens + completionTokens,
"completion_tokens_details": map[string]any{
"reasoning_tokens": reasoningTokens,
},
},
}
}
// BuildOpenAIResponseObject is kept for backward compatibility.
// Prefer internal/format/openai.BuildResponseObject for new code.
func BuildOpenAIResponseObject(responseID, model, finalPrompt, finalThinking, finalText string, toolNames []string) map[string]any {
detected := toolcall.ParseToolCalls(finalText, toolNames)
exposedOutputText := finalText
output := make([]any, 0, 2)
if len(detected) > 0 {
// Keep structured tool output only; avoid leaking raw tool-call JSON
// into response.output_text for clients reading completed responses.
exposedOutputText = ""
toolCalls := make([]any, 0, len(detected))
for _, tc := range detected {
toolCalls = append(toolCalls, map[string]any{
"type": "tool_call",
"name": tc.Name,
"arguments": tc.Input,
})
}
output = append(output, map[string]any{
"type": "tool_calls",
"tool_calls": toolCalls,
})
} else {
content := []any{
map[string]any{
"type": "output_text",
"text": finalText,
},
}
if finalThinking != "" {
content = append([]any{map[string]any{
"type": "reasoning",
"text": finalThinking,
}}, content...)
}
output = append(output, map[string]any{
"type": "message",
"id": "msg_" + strings.ReplaceAll(uuid.NewString(), "-", ""),
"role": "assistant",
"content": content,
})
}
promptTokens := CountPromptTokens(finalPrompt, model)
reasoningTokens := CountOutputTokens(finalThinking, model)
completionTokens := CountOutputTokens(finalText, model)
return map[string]any{
"id": responseID,
"type": "response",
"object": "response",
"created_at": time.Now().Unix(),
"status": "completed",
"model": model,
"output": output,
"output_text": exposedOutputText,
"usage": map[string]any{
"input_tokens": promptTokens,
"output_tokens": reasoningTokens + completionTokens,
"total_tokens": promptTokens + reasoningTokens + completionTokens,
},
}
}
// BuildClaudeMessageResponse is kept for backward compatibility.
// Prefer internal/format/claude.BuildMessageResponse for new code.
func BuildClaudeMessageResponse(messageID, model string, normalizedMessages []any, finalThinking, finalText string, toolNames []string) map[string]any {
detected := toolcall.ParseToolCalls(finalText, toolNames)
content := make([]map[string]any, 0, 4)
if finalThinking != "" {
content = append(content, map[string]any{"type": "thinking", "thinking": finalThinking})
}
stopReason := "end_turn"
if len(detected) > 0 {
stopReason = "tool_use"
for i, tc := range detected {
content = append(content, map[string]any{
"type": "tool_use",
"id": fmt.Sprintf("toolu_%d_%d", time.Now().Unix(), i),
"name": tc.Name,
"input": tc.Input,
})
}
} else {
if finalText == "" {
finalText = "抱歉，没有生成有效的响应内容。"
}
content = append(content, map[string]any{"type": "text", "text": finalText})
}
return map[string]any{
"id": messageID,
"type": "message",
"role": "assistant",
"model": model,
"content": content,
"stop_reason": stopReason,
"stop_sequence": nil,
"usage": map[string]any{
"input_tokens": CountPromptTokens(fmt.Sprintf("%v", normalizedMessages), model),
"output_tokens": CountOutputTokens(finalThinking, model) + CountOutputTokens(finalText, model),
},
}
}
|