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🐛 fix(ai/query-editor/mac-window): 修复模型兼容性并优化即时执行与窗口交互
- AI 兼容性:为 Anthropic Provider 补齐 tools/tool_use/tool_result 协议转换,支持工具调用与流式工具结果解析 - 降级策略:OpenAI 兼容接口在 tools 请求返回 400/422/404 时自动回退为纯文本模式 - 配置修复:调整 MiniMax 预设为 Anthropic 兼容端点并更新默认模型列表 - 状态隔离:AI 聊天面板停止将动态模型列表写回供应商配置,避免污染静态 models 数据 - 编辑器修复:QueryEditor 在 runImmediately 场景下避免重复追加 SQL,改为直接选中并执行 - 交互优化:修复 macOS 原生窗口控制切换与标题栏点击行为,避免窗口按钮状态异常
This commit is contained in:
@@ -1215,9 +1215,6 @@ function App() {
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if (target?.closest('[data-no-titlebar-toggle="true"]')) {
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return;
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}
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if (useNativeMacWindowControls) {
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return;
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}
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void handleTitleBarWindowToggle();
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};
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@@ -333,21 +333,8 @@ export const AIChatPanel: React.FC<AIChatPanelProps> = ({
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}
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}, [activeProvider?.id]);
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useEffect(() => {
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if (activeProvider && dynamicModels.length > 0) {
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const currentModels = activeProvider.models || [];
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if (JSON.stringify(currentModels) !== JSON.stringify(dynamicModels)) {
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try {
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const Service = (window as any).go?.aiservice?.Service;
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const payload = { ...activeProvider, models: dynamicModels };
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Service?.AISaveProvider?.(payload);
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setActiveProvider(payload);
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} catch (e) {
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console.warn('Failed to cache models', e);
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}
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}
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}
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}, [activeProvider, dynamicModels]);
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// dynamicModels 仅在内存中使用,不再写回供应商配置,避免污染静态 models 列表
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const fetchDynamicModels = useCallback(async () => {
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try {
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@@ -34,7 +34,7 @@ const PROVIDER_PRESETS: ProviderPreset[] = [
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{ key: 'anthropic', label: 'Claude', icon: <ExperimentOutlined />, desc: 'Claude Opus/Sonnet 4.6', color: '#d97706', backendType: 'anthropic', defaultBaseUrl: 'https://api.anthropic.com', defaultModel: 'claude-sonnet-4-6', models: ['claude-opus-4-6', 'claude-sonnet-4-6'] },
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{ key: 'gemini', label: 'Gemini', icon: <CloudOutlined />, desc: 'Gemini 3.1 / 2.5 系列', color: '#059669', backendType: 'gemini', defaultBaseUrl: 'https://generativelanguage.googleapis.com', defaultModel: 'gemini-2.5-flash', models: ['gemini-3.1-pro', 'gemini-2.5-flash', 'gemini-2.5-pro'] },
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{ key: 'volcengine', label: '火山引擎', icon: <CloudOutlined />, desc: '火山方舟 / 豆包大模型', color: '#0ea5e9', backendType: 'openai', defaultBaseUrl: 'https://ark.cn-beijing.volces.com/api/v3', defaultModel: 'ep-xxxxxx', models: [] },
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{ key: 'minimax', label: 'MiniMax', icon: <ExperimentOutlined />, desc: 'abab6.5 / abab7 系列', color: '#e11d48', backendType: 'openai', defaultBaseUrl: 'https://api.minimax.chat/v1', defaultModel: 'abab7-chat-preview', models: ['abab7-chat-preview', 'abab6.5-chat', 'abab6.5g-chat'] },
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{ key: 'minimax', label: 'MiniMax', icon: <ExperimentOutlined />, desc: 'abab6.5 / abab7 系列', color: '#e11d48', backendType: 'anthropic', defaultBaseUrl: 'https://api.minimaxi.com/anthropic', defaultModel: 'MiniMax-Text-01', models: ['MiniMax-Text-01', 'MiniMax-Text-01-vision', 'MiniMax-Text-01-search', 'MiniMax-Text-01-code', 'MiniMax-Text-01-web', 'MiniMax-Text-01-sql', 'MiniMax-Text-01-python', 'MiniMax-Text-01-math', 'MiniMax-Text-01-doc'] },
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{ key: 'ollama', label: 'Ollama', icon: <AppstoreOutlined />, desc: '本地部署开源模型', color: '#78716c', backendType: 'openai', defaultBaseUrl: 'http://localhost:11434/v1', defaultModel: 'llama3', models: [] },
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{ key: 'custom', label: '自定义', icon: <AppstoreOutlined />, desc: '自定义 API 端点', color: '#64748b', backendType: 'custom', defaultBaseUrl: '', defaultModel: '', models: [] },
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];
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@@ -2040,11 +2040,25 @@ const QueryEditor: React.FC<{ tab: TabData }> = ({ tab }) => {
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setCurrentDb(dbName);
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}
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const editor = editorRef.current;
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const monaco = monacoRef.current;
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if (editor && monaco) {
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let position = editor.getPosition();
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const model = editor.getModel();
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const existingContent = editor.getValue?.() || '';
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// runImmediately 模式下,如果编辑器内容已是待注入的 SQL(TabManager 创建时已传入),
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// 跳过追加,直接选中全部内容并执行
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if (e.detail.runImmediately && existingContent.trim() === sqlText.trim()) {
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if (model) {
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const lineCount = model.getLineCount();
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const maxCol = model.getLineMaxColumn(lineCount);
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editor.setSelection(new monaco.Range(1, 1, lineCount, maxCol));
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editor.focus();
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setTimeout(() => handleRun(), 500);
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}
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} else {
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let position = editor.getPosition();
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if (!position && model) {
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const lineCount = model.getLineCount();
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const maxCol = model.getLineMaxColumn(lineCount);
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@@ -2081,6 +2095,7 @@ const QueryEditor: React.FC<{ tab: TabData }> = ({ tab }) => {
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setTimeout(() => handleRun(), 500);
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}
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}
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}
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} else {
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setQuery((prev: string) => prev ? prev + '\n' + sqlText : sqlText);
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message.success('代码已追加');
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@@ -151,7 +151,7 @@ const TabManager: React.FC = () => {
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id: newTabId,
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type: 'query',
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title: '新建查询',
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query: '',
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query: sql,
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connectionId: resolvedConnId,
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dbName: resolvedDbName
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});
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@@ -72,13 +72,23 @@ func (p *AnthropicProvider) Validate() error {
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return nil
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}
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// --- 请求体类型 ---
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type anthropicRequest struct {
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Model string `json:"model"`
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Messages []anthropicMessage `json:"messages"`
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System string `json:"system,omitempty"`
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MaxTokens int `json:"max_tokens"`
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Temperature float64 `json:"temperature,omitempty"`
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Stream bool `json:"stream,omitempty"`
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Model string `json:"model"`
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Messages []anthropicMessage `json:"messages"`
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System string `json:"system,omitempty"`
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MaxTokens int `json:"max_tokens"`
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Temperature float64 `json:"temperature,omitempty"`
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Stream bool `json:"stream,omitempty"`
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Tools []anthropicTool `json:"tools,omitempty"`
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}
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// anthropicTool Anthropic 格式的工具定义
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type anthropicTool struct {
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Name string `json:"name"`
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Description string `json:"description,omitempty"`
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InputSchema any `json:"input_schema"`
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}
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type anthropicMessage struct {
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@@ -86,9 +96,66 @@ type anthropicMessage struct {
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Content interface{} `json:"content"`
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}
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// convertToolsToAnthropic 将 OpenAI 格式的 tools 转换为 Anthropic 格式
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func convertToolsToAnthropic(tools []ai.Tool) []anthropicTool {
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if len(tools) == 0 {
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return nil
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}
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result := make([]anthropicTool, 0, len(tools))
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for _, t := range tools {
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result = append(result, anthropicTool{
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Name: t.Function.Name,
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Description: t.Function.Description,
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InputSchema: t.Function.Parameters,
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})
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}
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return result
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}
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func buildAnthropicMessages(reqMessages []ai.Message) []anthropicMessage {
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messages := make([]anthropicMessage, 0, len(reqMessages))
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for _, m := range reqMessages {
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// tool result 消息:转换为 Anthropic 的 tool_result content block
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if m.Role == "tool" {
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messages = append(messages, anthropicMessage{
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Role: "user",
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Content: []map[string]interface{}{
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{
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"type": "tool_result",
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"tool_use_id": m.ToolCallID,
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"content": m.Content,
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},
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},
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})
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continue
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}
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// assistant 带 tool_calls:转换为 Anthropic 的 tool_use content block
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if m.Role == "assistant" && len(m.ToolCalls) > 0 {
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var contentParts []map[string]interface{}
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if m.Content != "" {
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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"text": m.Content,
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})
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}
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for _, tc := range m.ToolCalls {
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var input interface{}
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if err := json.Unmarshal([]byte(tc.Function.Arguments), &input); err != nil {
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input = map[string]interface{}{}
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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": tc.ID,
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"name": tc.Function.Name,
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"input": input,
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})
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}
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messages = append(messages, anthropicMessage{Role: "assistant", Content: contentParts})
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continue
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}
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// 图片消息
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if len(m.Images) > 0 {
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var contentParts []map[string]interface{}
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for _, img := range m.Images {
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@@ -106,7 +173,7 @@ func buildAnthropicMessages(reqMessages []ai.Message) []anthropicMessage {
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}
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text := m.Content
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if text == "" {
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text = "请描述和分析这张图片。" // 防止强 System Prompt 下模型仅看到空文本且忽略图片直接回复打招呼
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text = "请描述和分析这张图片。"
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}
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contentParts = append(contentParts, map[string]interface{}{
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"type": "text",
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@@ -120,11 +187,19 @@ func buildAnthropicMessages(reqMessages []ai.Message) []anthropicMessage {
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return messages
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}
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// --- 响应体类型 ---
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type anthropicContentBlock struct {
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Type string `json:"type"` // "text" | "tool_use"
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Text string `json:"text,omitempty"`
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ID string `json:"id,omitempty"` // tool_use
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Name string `json:"name,omitempty"` // tool_use
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Input json.RawMessage `json:"input,omitempty"` // tool_use
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}
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type anthropicResponse struct {
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Content []struct {
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Text string `json:"text"`
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} `json:"content"`
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Usage struct {
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Content []anthropicContentBlock `json:"content"`
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Usage struct {
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InputTokens int `json:"input_tokens"`
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OutputTokens int `json:"output_tokens"`
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} `json:"usage"`
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@@ -133,13 +208,20 @@ type anthropicResponse struct {
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} `json:"error,omitempty"`
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}
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// 流式事件类型
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type anthropicStreamEvent struct {
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Type string `json:"type"`
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Delta *struct {
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Text string `json:"text"`
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Type string `json:"type"`
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Index int `json:"index,omitempty"`
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ContentBlock *anthropicContentBlock `json:"content_block,omitempty"`
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Delta *struct {
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Type string `json:"type,omitempty"`
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Text string `json:"text,omitempty"`
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PartialJSON string `json:"partial_json,omitempty"`
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} `json:"delta,omitempty"`
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}
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// --- Chat 非流式 ---
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func (p *AnthropicProvider) Chat(ctx context.Context, req ai.ChatRequest) (*ai.ChatResponse, error) {
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if err := p.Validate(); err != nil {
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return nil, err
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@@ -163,6 +245,7 @@ func (p *AnthropicProvider) Chat(ctx context.Context, req ai.ChatRequest) (*ai.C
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System: systemMsg,
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MaxTokens: maxTokens,
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Temperature: temperature,
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Tools: convertToolsToAnthropic(req.Tools),
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}
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respBody, err := p.doRequest(ctx, body)
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@@ -182,8 +265,35 @@ func (p *AnthropicProvider) Chat(ctx context.Context, req ai.ChatRequest) (*ai.C
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return nil, fmt.Errorf("Anthropic 返回空响应")
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}
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// 解析响应中的 text 和 tool_use content blocks
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var textContent string
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var toolCalls []ai.ToolCall
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for _, block := range result.Content {
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switch block.Type {
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case "text":
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textContent += block.Text
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case "tool_use":
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argsStr := "{}"
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if len(block.Input) > 0 {
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argsStr = string(block.Input)
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}
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toolCalls = append(toolCalls, ai.ToolCall{
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ID: block.ID,
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Type: "function",
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Function: struct {
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Name string `json:"name"`
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Arguments string `json:"arguments"`
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}{
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Name: block.Name,
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Arguments: argsStr,
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},
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})
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}
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}
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return &ai.ChatResponse{
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Content: result.Content[0].Text,
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Content: textContent,
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ToolCalls: toolCalls,
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TokensUsed: ai.TokenUsage{
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PromptTokens: result.Usage.InputTokens,
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CompletionTokens: result.Usage.OutputTokens,
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@@ -192,6 +302,8 @@ func (p *AnthropicProvider) Chat(ctx context.Context, req ai.ChatRequest) (*ai.C
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}, nil
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}
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// --- ChatStream 流式 ---
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func (p *AnthropicProvider) ChatStream(ctx context.Context, req ai.ChatRequest, callback func(ai.StreamChunk)) error {
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if err := p.Validate(); err != nil {
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return err
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@@ -216,6 +328,7 @@ func (p *AnthropicProvider) ChatStream(ctx context.Context, req ai.ChatRequest,
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MaxTokens: maxTokens,
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Temperature: temperature,
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Stream: true,
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Tools: convertToolsToAnthropic(req.Tools),
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}
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respBody, err := p.doRequest(ctx, body)
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@@ -224,6 +337,14 @@ func (p *AnthropicProvider) ChatStream(ctx context.Context, req ai.ChatRequest,
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}
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defer respBody.Close()
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// 跟踪当前活跃的 tool_use blocks
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type activeToolUse struct {
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id string
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name string
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argsJSON strings.Builder
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}
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activeBlocks := make(map[int]*activeToolUse) // index -> block
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scanner := bufio.NewScanner(respBody)
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for scanner.Scan() {
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line := scanner.Text()
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@@ -238,10 +359,54 @@ func (p *AnthropicProvider) ChatStream(ctx context.Context, req ai.ChatRequest,
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}
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switch event.Type {
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case "content_block_delta":
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if event.Delta != nil && event.Delta.Text != "" {
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callback(ai.StreamChunk{Content: event.Delta.Text})
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case "content_block_start":
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if event.ContentBlock != nil && event.ContentBlock.Type == "tool_use" {
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activeBlocks[event.Index] = &activeToolUse{
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id: event.ContentBlock.ID,
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name: event.ContentBlock.Name,
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}
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}
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case "content_block_delta":
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if event.Delta == nil {
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continue
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}
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switch event.Delta.Type {
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case "text_delta":
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if event.Delta.Text != "" {
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callback(ai.StreamChunk{Content: event.Delta.Text})
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}
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case "input_json_delta":
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if block, ok := activeBlocks[event.Index]; ok {
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block.argsJSON.WriteString(event.Delta.PartialJSON)
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}
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}
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case "content_block_stop":
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if block, ok := activeBlocks[event.Index]; ok {
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argsStr := block.argsJSON.String()
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if argsStr == "" {
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argsStr = "{}"
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}
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// 产出完整的 tool call
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callback(ai.StreamChunk{
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ToolCalls: []ai.ToolCall{
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{
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ID: block.id,
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Type: "function",
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Function: struct {
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Name string `json:"name"`
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Arguments string `json:"arguments"`
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}{
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Name: block.name,
|
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Arguments: argsStr,
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},
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},
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},
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})
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delete(activeBlocks, event.Index)
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}
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case "message_stop":
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callback(ai.StreamChunk{Done: true})
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return nil
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@@ -252,6 +417,8 @@ func (p *AnthropicProvider) ChatStream(ctx context.Context, req ai.ChatRequest,
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return scanner.Err()
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}
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// --- HTTP 请求 ---
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func (p *AnthropicProvider) doRequest(ctx context.Context, body interface{}) (io.ReadCloser, error) {
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jsonBody, err := json.Marshal(body)
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if err != nil {
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@@ -263,15 +430,6 @@ func (p *AnthropicProvider) doRequest(ctx context.Context, body interface{}) (io
|
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url = p.baseURL + "/messages"
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}
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// 调试日志:打印实际请求信息
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bodyStr := string(jsonBody)
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if len(bodyStr) > 500 {
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bodyStr = bodyStr[:500] + "..."
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}
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fmt.Printf("[Anthropic DEBUG] URL: %s\n", url)
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fmt.Printf("[Anthropic DEBUG] BaseURL: %s\n", p.baseURL)
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fmt.Printf("[Anthropic DEBUG] Body: %s\n", bodyStr)
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httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(jsonBody))
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if err != nil {
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return nil, fmt.Errorf("创建 HTTP 请求失败: %w", err)
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@@ -209,7 +209,17 @@ func (p *OpenAIProvider) Chat(ctx context.Context, req ai.ChatRequest) (*ai.Chat
|
||||
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||||
respBody, err := p.doRequest(ctx, body)
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if err != nil {
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return nil, err
|
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// 当带 tools 的请求返回 400 时,自动降级为不带 tools 的纯文本请求
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if len(req.Tools) > 0 && isHTTP400Error(err) {
|
||||
fmt.Println("[OpenAI] 模型不支持 Function Calling,自动降级为纯文本模式")
|
||||
body.Tools = nil
|
||||
respBody, err = p.doRequest(ctx, body)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
} else {
|
||||
return nil, err
|
||||
}
|
||||
}
|
||||
defer respBody.Close()
|
||||
|
||||
@@ -257,7 +267,17 @@ func (p *OpenAIProvider) ChatStream(ctx context.Context, req ai.ChatRequest, cal
|
||||
|
||||
respBody, err := p.doRequest(ctx, body)
|
||||
if err != nil {
|
||||
return err
|
||||
// 当带 tools 的请求返回 400 时,自动降级为不带 tools 的纯文本请求
|
||||
if len(req.Tools) > 0 && isHTTP400Error(err) {
|
||||
fmt.Println("[OpenAI] 模型不支持 Function Calling,自动降级为纯文本模式")
|
||||
body.Tools = nil
|
||||
respBody, err = p.doRequest(ctx, body)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
} else {
|
||||
return err
|
||||
}
|
||||
}
|
||||
defer respBody.Close()
|
||||
|
||||
@@ -366,14 +386,7 @@ func (p *OpenAIProvider) doRequest(ctx context.Context, body interface{}) (io.Re
|
||||
|
||||
url := p.baseURL + "/chat/completions"
|
||||
|
||||
// 调试日志
|
||||
bodyStr := string(jsonBody)
|
||||
if len(bodyStr) > 500 {
|
||||
bodyStr = bodyStr[:500] + "..."
|
||||
}
|
||||
fmt.Printf("[OpenAI DEBUG] URL: %s\n", url)
|
||||
fmt.Printf("[OpenAI DEBUG] BaseURL: %s\n", p.baseURL)
|
||||
fmt.Printf("[OpenAI DEBUG] Body: %s\n", bodyStr)
|
||||
|
||||
|
||||
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(jsonBody))
|
||||
if err != nil {
|
||||
@@ -408,3 +421,15 @@ func (p *OpenAIProvider) doRequest(ctx context.Context, body interface{}) (io.Re
|
||||
|
||||
return resp.Body, nil
|
||||
}
|
||||
|
||||
// isHTTP400Error 检查错误是否为 HTTP 4xx 客户端错误(400/422 等),
|
||||
// 通常表示模型不支持请求中的某些参数(如 tools/functions)。
|
||||
func isHTTP400Error(err error) bool {
|
||||
if err == nil {
|
||||
return false
|
||||
}
|
||||
msg := err.Error()
|
||||
return strings.Contains(msg, "(HTTP 400)") ||
|
||||
strings.Contains(msg, "(HTTP 422)") ||
|
||||
strings.Contains(msg, "(HTTP 404)")
|
||||
}
|
||||
|
||||
@@ -8,6 +8,9 @@ package app
|
||||
#import <Cocoa/Cocoa.h>
|
||||
#import <dispatch/dispatch.h>
|
||||
|
||||
static inline BOOL gonaviBoolYES() { return YES; }
|
||||
static inline BOOL gonaviBoolNO() { return NO; }
|
||||
|
||||
static void gonaviSetWindowButtonsVisible(NSWindow *window, BOOL visible) {
|
||||
if (window == nil) {
|
||||
return;
|
||||
@@ -62,9 +65,9 @@ import "C"
|
||||
|
||||
func setMacNativeWindowControls(enabled bool) {
|
||||
state := resolveMacNativeWindowControlState(enabled)
|
||||
flag := C.BOOL(false)
|
||||
if state.ShowNativeButtons {
|
||||
flag = C.BOOL(true)
|
||||
C.gonaviApplyMacWindowStyle(C.gonaviBoolYES())
|
||||
} else {
|
||||
C.gonaviApplyMacWindowStyle(C.gonaviBoolNO())
|
||||
}
|
||||
C.gonaviApplyMacWindowStyle(flag)
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user