| package util |
|
|
| import ( |
| "ds2api/internal/toolcall" |
| "fmt" |
| "strings" |
| "time" |
|
|
| "github.com/google/uuid" |
| ) |
|
|
| |
| |
| 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, |
| }, |
| }, |
| } |
| } |
|
|
| |
| |
| 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 { |
| |
| |
| 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, |
| }, |
| } |
| } |
|
|
| |
| |
| 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), |
| }, |
| } |
| } |
|
|
