325 lines
12 KiB
Go
325 lines
12 KiB
Go
package app
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// 文本生成、Agent 工具循环和文本流解析。
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import (
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"context"
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"errors"
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"fmt"
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"net/http"
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"strings"
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"yingce/backend/internal/model"
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"yingce/backend/internal/protocol"
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)
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func runTextTask(ctx context.Context, input canvasGenerationInput) (map[string]interface{}, error) {
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if _, ok := declarativeProtocolAdapterForContext(ctx, input.Config.InterfaceType); ok {
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return runDeclarativeProtocolTask(ctx, input)
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}
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switch input.Config.InterfaceType {
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case "chat-completion":
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return runChatCompletionsTextTask(ctx, input)
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case "openai-response":
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return runResponsesTextTask(ctx, input)
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case string(model.ChannelInterfaceClaudeAPI):
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return runClaudeTextTask(ctx, input)
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}
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return runLegacyTextTask(ctx, input)
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}
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// Known text protocols keep the plugin's request mapping and host transport,
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// while sharing the SSE parser used by text generation and Agent requests.
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func executeProtocolCreateRequest(ctx context.Context, input canvasGenerationInput, spec protocol.RequestSpec) ([]byte, *protocol.Result, error) {
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wire := input.Config.InterfaceType
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if wire == string(model.ChannelInterfaceOpenAIResponse) {
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wire = "responses"
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}
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if input.Mode != "text" || !input.StreamText || (wire != "chat-completion" && wire != "responses" && wire != "claude-api") {
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data, err := executeProtocolRequest(ctx, input.Config, spec)
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return data, nil, err
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}
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body := protocolBodyObject(spec.Body)
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if body == nil {
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return nil, nil, errors.New("声明式流式文本请求体必须是 JSON 对象")
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}
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body["stream"] = true
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if wire == "chat-completion" {
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if err := ensureChatCompletionStreamUsage(body); err != nil {
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return nil, nil, err
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}
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}
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spec.Body = body
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parser := newStreamingAgentParser(wire, input.OnTextDelta)
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parser.emitReasoning = input.OnReasoningDelta
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data, mimeType, err := executeProtocolBinaryRequestWithConsumer(ctx, input.Config, spec, parser.consume)
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if err != nil || !strings.Contains(strings.ToLower(mimeType), "event-stream") {
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return data, nil, err
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}
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parser.flush()
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parsed, err := parser.result()
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if err != nil {
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return nil, nil, err
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}
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text := stringField(parsed, "text")
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if text == "" {
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return nil, nil, errors.New("流式文本接口没有返回内容")
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}
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return data, &protocol.Result{Text: text, Reasoning: stringField(parsed, "reasoning")}, nil
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}
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func runLegacyTextTask(ctx context.Context, input canvasGenerationInput) (map[string]interface{}, error) {
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responseInput, err := textResponseInput(input)
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if err != nil {
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return nil, err
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}
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body := map[string]interface{}{"model": input.Config.Model, "input": responseInput}
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applyTextThinking(body, input, "responses")
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applyTextOutputLimit(body, input.MaxOutputTokens, "max_output_tokens")
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result, err := requestTextProvider(ctx, input.Config, "/responses", body, "responses", input.StreamText, input.OnTextDelta)
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if err != nil {
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if !shouldFallbackTextToChat(err) {
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return nil, err
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}
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result, chatErr := runChatCompletionsTextTask(ctx, input)
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if chatErr == nil {
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return result, nil
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}
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return nil, fmt.Errorf("文本接口请求失败：Responses API %v；Chat Completions %v", err, chatErr)
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}
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return providerTextTaskResult(result), nil
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}
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func runResponsesTextTask(ctx context.Context, input canvasGenerationInput) (map[string]interface{}, error) {
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responseInput, err := textResponseInput(input)
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if err != nil {
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return nil, err
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}
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body := map[string]interface{}{"model": input.Config.Model, "input": responseInput}
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applyTextThinking(body, input, "responses")
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applyTextOutputLimit(body, input.MaxOutputTokens, "max_output_tokens")
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result, err := requestTextProvider(ctx, input.Config, "/responses", body, "responses", input.StreamText, input.OnTextDelta)
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if err != nil {
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return nil, err
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}
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return providerTextTaskResult(result), nil
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}
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func runChatCompletionsTextTask(ctx context.Context, input canvasGenerationInput) (map[string]interface{}, error) {
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messages := []map[string]interface{}{}
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if systemPrompt := strings.TrimSpace(input.Config.SystemPrompt); systemPrompt != "" {
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messages = append(messages, map[string]interface{}{"role": "system", "content": systemPrompt})
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}
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messages = append(messages, validatedTextHistory(input.TextHistory)...)
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userContent, err := textChatContent(input)
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if err != nil {
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return nil, err
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}
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messages = append(messages, map[string]interface{}{"role": "user", "content": userContent})
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body := map[string]interface{}{"model": input.Config.Model, "messages": messages}
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applyTextThinking(body, input, "chat-completion")
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applyTextOutputLimit(body, input.MaxOutputTokens, "max_tokens")
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result, err := requestTextProvider(ctx, input.Config, "/chat/completions", body, "chat-completion", input.StreamText, input.OnTextDelta)
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if err != nil {
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return nil, err
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}
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return providerTextTaskResult(result), nil
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}
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func runClaudeTextTask(ctx context.Context, input canvasGenerationInput) (map[string]interface{}, error) {
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if len(input.ReferenceVideos) > 0 {
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return nil, errors.New("Claude API 当前不支持视频参考输入")
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}
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messages := make([]map[string]interface{}, 0, len(input.TextHistory)+1)
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for _, message := range validatedTextHistory(input.TextHistory) {
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messages = append(messages, message)
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}
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content, err := claudeTextContent(input)
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if err != nil {
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return nil, err
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}
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messages = append(messages, map[string]interface{}{"role": "user", "content": content})
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maxTokens := 4096
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if input.MaxOutputTokens > 0 {
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maxTokens = input.MaxOutputTokens
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}
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body := map[string]interface{}{"model": input.Config.Model, "max_tokens": maxTokens, "messages": messages}
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applyTextThinking(body, input, "claude-api")
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if systemPrompt := strings.TrimSpace(input.Config.SystemPrompt); systemPrompt != "" {
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body["system"] = systemPrompt
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}
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result, err := requestTextProvider(ctx, input.Config, "/messages", body, "claude-api", input.StreamText, input.OnTextDelta)
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if err != nil {
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return nil, err
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}
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return providerTextTaskResult(result), nil
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}
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func applyTextThinking(body map[string]interface{}, input canvasGenerationInput, protocol string) {
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if !input.TextOptions.Thinking {
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return
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}
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switch protocol {
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case "responses":
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body["reasoning"] = map[string]interface{}{"effort": "medium", "summary": "auto"}
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case "chat-completion":
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body["reasoning_effort"] = "medium"
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case "claude-api":
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body["thinking"] = map[string]interface{}{"type": "enabled", "budget_tokens": 1024}
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}
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}
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// Chat Completion defaults to automatic tool selection when tools are present,
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// so an explicit "auto" only reduces compatibility. Reasoning endpoints also
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// disagree on forced choices. Normalize before the first network request while
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// preserving required/named choices for non-reasoning structured tasks.
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func normalizeAgentToolChoice(body map[string]interface{}, input canvasGenerationInput, protocol string) {
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if protocol == "chat-completion" && (input.TextOptions.Thinking || isAutoAgentToolChoice(body["tool_choice"])) {
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delete(body, "tool_choice")
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}
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}
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type providerTextResult struct {
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Text string
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Reasoning string
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}
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func providerTextTaskResult(result providerTextResult) map[string]interface{} {
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payload := map[string]interface{}{"mode": "text", "text": result.Text}
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if strings.TrimSpace(result.Reasoning) != "" {
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payload["reasoning"] = result.Reasoning
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}
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return payload
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}
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func applyTextOutputLimit(body map[string]interface{}, limit int, field string) {
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if limit > 0 {
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body[field] = limit
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}
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}
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func claudeTextContent(input canvasGenerationInput) (interface{}, error) {
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if len(input.ReferenceImages) == 0 {
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return input.Prompt, nil
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}
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content := []map[string]interface{}{{"type": "text", "text": input.Prompt}}
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for _, image := range input.ReferenceImages {
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value, err := openAIImageInputURL(image)
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if err != nil {
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return nil, err
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}
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if strings.HasPrefix(value, "data:") {
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mimeType, data, ok := splitDataURL(value)
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if !ok {
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return nil, errors.New("Claude 参考图片 data URL 无效")
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}
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content = append(content, map[string]interface{}{"type": "image", "source": map[string]interface{}{"type": "base64", "media_type": mimeType, "data": data}})
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} else {
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content = append(content, map[string]interface{}{"type": "image", "source": map[string]interface{}{"type": "url", "url": value}})
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}
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}
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return content, nil
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}
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func splitDataURL(value string) (string, string, bool) {
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if !strings.HasPrefix(value, "data:") {
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return "", "", false
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}
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separator := strings.Index(value, ",")
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if separator <= len("data:") {
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return "", "", false
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}
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header := strings.TrimPrefix(value[:separator], "data:")
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if !strings.HasSuffix(header, ";base64") {
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return "", "", false
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}
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return strings.TrimSuffix(header, ";base64"), value[separator+1:], value[separator+1:] != ""
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}
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func textResponseInput(input canvasGenerationInput) (interface{}, error) {
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systemPrompt := strings.TrimSpace(input.Config.SystemPrompt)
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if len(input.TextHistory) == 0 && len(input.ReferenceImages) == 0 && len(input.ReferenceVideos) == 0 {
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return withSystemPrompt(input.Config, input.Prompt), nil
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}
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messages := make([]map[string]interface{}, 0, len(input.TextHistory)+2)
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if systemPrompt != "" {
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messages = append(messages, map[string]interface{}{"role": "system", "content": systemPrompt})
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}
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messages = append(messages, validatedTextHistory(input.TextHistory)...)
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content, err := textResponseContent(input)
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if err != nil {
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return nil, err
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}
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messages = append(messages, map[string]interface{}{"role": "user", "content": content})
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return messages, nil
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}
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func validatedTextHistory(history []providerTextMessage) []map[string]interface{} {
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result := make([]map[string]interface{}, 0, len(history))
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for _, message := range history {
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role := strings.ToLower(strings.TrimSpace(message.Role))
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content := strings.TrimSpace(message.Content)
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if (role != "user" && role != "assistant") || content == "" {
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continue
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}
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result = append(result, map[string]interface{}{"role": role, "content": content})
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}
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return result
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}
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func textResponseContent(input canvasGenerationInput) ([]map[string]interface{}, error) {
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content := []map[string]interface{}{{"type": "input_text", "text": input.Prompt}}
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for _, image := range input.ReferenceImages {
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url, err := openAIImageInputURL(image)
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if err != nil {
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return nil, err
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}
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content = append(content, map[string]interface{}{"type": "input_image", "image_url": url})
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}
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for _, video := range input.ReferenceVideos {
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url, err := openAIVideoInputURL(video)
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if err != nil {
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return nil, err
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}
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content = append(content, map[string]interface{}{"type": "input_video", "video_url": url})
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}
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return content, nil
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}
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func textChatContent(input canvasGenerationInput) (interface{}, error) {
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if len(input.ReferenceImages) == 0 && len(input.ReferenceVideos) == 0 {
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return input.Prompt, nil
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}
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content := []map[string]interface{}{{"type": "text", "text": input.Prompt}}
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for _, image := range input.ReferenceImages {
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url, err := openAIImageInputURL(image)
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if err != nil {
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return nil, err
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}
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content = append(content, map[string]interface{}{"type": "image_url", "image_url": map[string]interface{}{"url": url}})
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}
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for _, video := range input.ReferenceVideos {
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url, err := openAIVideoInputURL(video)
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if err != nil {
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return nil, err
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}
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content = append(content, map[string]interface{}{"type": "video_url", "video_url": map[string]interface{}{"url": url}})
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}
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return content, nil
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}
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func shouldFallbackTextToChat(err error) bool {
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var httpErr providerHTTPError
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if !errors.As(err, &httpErr) {
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return false
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}
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// 只在上游明确说这个路径不存在/不允许时，才把 Responses 换成 Chat Completions。
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// 502/503/504 是瞬时故障，换协议不会修好，还会把真正的上游故障伪装成“能力缺失后的第二次失败”。
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switch httpErr.StatusCode {
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case http.StatusNotFound, http.StatusMethodNotAllowed, http.StatusNotImplemented:
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return true
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default:
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return false
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}
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}
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