cfy666
4b731b5ac0
- graph.go: 构建 Graph 拓扑 START→STT→History→ChatModel→Splitter→TTS→Done→END - 创建 eino-ext ChatModel 对接 DashScope OpenAI 兼容接口 - 统一使用值类型（PipelineInput/PipelineOutput） - Callback 在运行时通过 Stream option 传入 - adapter.go: EinoOrchestrator 实现 orchestrator.Orchestrator 接口 - 解码 base64 音频/图片，注入 context 值 - 调用 Graph.Stream() 触发惰性执行并消费输出 - 追加用户/助手消息到历史 - main.go: 移除旧 llmService + orchestrator.New() 替换为 eino.NewPipelineGraph() + eino.NewEinoOrchestrator() - 各节点统一使用值类型，State 传递请求元数据 Co-Authored-By: Claude <noreply@anthropic.com>
133 lines
3.4 KiB
Go
133 lines
3.4 KiB
Go
package eino
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import (
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"context"
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"fmt"
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"strings"
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"github.com/cloudwego/eino/compose"
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"github.com/hhs/camtalk/internal/ai/stt"
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"github.com/hhs/camtalk/internal/logger"
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"github.com/hhs/camtalk/internal/models"
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)
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// NewSTTLambda 创建 STT Lambda 节点。
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// 输入: PipelineInput → 输出: STTOutput
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//
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// 文本输入模式：跳过 STT，直接返回用户输入文本。
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// 语音模式：调用 sttService.Recognize() 进行语音识别。
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// 识别结果通过 Sender 发送 stt_result 到客户端。
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func NewSTTLambda(sttService stt.Service) *compose.Lambda {
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return compose.InvokableLambda(func(ctx context.Context, input PipelineInput) (STTOutput, error) {
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log := logger.Log
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sender := senderFromCtx(ctx)
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requestID := requestIDFromCtx(ctx)
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// 将输入元数据写入 State，供下游节点（History、Done）读取
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if state := stateFromCtx(ctx); state != nil {
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state.mu.Lock()
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state.SessionID = input.SessionID
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state.RequestID = input.RequestID
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state.ImageData = input.ImageData
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state.Scenario = input.Scenario
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state.DetailLevel = "low"
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state.Language = input.Language
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state.TTSEnabled = input.TTSEnabled
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state.mu.Unlock()
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}
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// 文本输入模式：跳过 STT
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if input.Text != "" {
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log.Infow("使用文本输入，跳过 STT",
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"request_id", requestID, "text", input.Text)
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// 发送 stt_result 保持前端消息流一致性
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if sender != nil {
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if err := sender.SendSTTResult(models.WsSTTResult{
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Type: "stt_result",
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RequestID: requestID,
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Text: input.Text,
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IsFinal: true,
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}); err != nil {
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log.Errorw("发送 stt_result 失败", "error", err)
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}
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}
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// 写入 State
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if state := stateFromCtx(ctx); state != nil {
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state.mu.Lock()
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state.TranscribedText = input.Text
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state.mu.Unlock()
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}
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return STTOutput{
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Text: input.Text,
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Language: input.Language,
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IsSkipped: true,
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}, nil
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}
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// 语音模式：解码音频
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if len(input.AudioData) == 0 {
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return STTOutput{}, fmt.Errorf("stt: no audio data provided")
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}
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log.Infow("开始语音识别",
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"request_id", requestID, "audio_bytes", len(input.AudioData))
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// 调用 STT 服务
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text, err := sttService.Recognize(ctx, input.AudioData, stt.Options{
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Encoding: "pcm_s16le",
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SampleRate: 16000,
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Language: input.Language,
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})
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if err != nil {
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log.Errorw("语音识别失败", "error", err, "request_id", requestID)
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if sender != nil {
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sender.SendError(models.WsError{
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Type: "error",
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RequestID: requestID,
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Code: "STT_ERROR",
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Message: "语音识别失败: " + err.Error(),
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})
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}
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return STTOutput{}, fmt.Errorf("stt: recognize: %w", err)
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}
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// STT 返回空文本
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if strings.TrimSpace(text) == "" {
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log.Infow("语音识别结果为空", "request_id", requestID)
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text = "（未识别到语音）"
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}
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log.Infow("语音识别完成", "request_id", requestID, "text", text)
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// 发送 stt_result
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if sender != nil {
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if err := sender.SendSTTResult(models.WsSTTResult{
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Type: "stt_result",
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RequestID: requestID,
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Text: text,
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IsFinal: true,
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}); err != nil {
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log.Errorw("发送 stt_result 失败", "error", err)
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}
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}
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// 写入 State
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if state := stateFromCtx(ctx); state != nil {
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state.mu.Lock()
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state.TranscribedText = text
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state.mu.Unlock()
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}
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return STTOutput{
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Text: text,
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Language: input.Language,
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IsSkipped: false,
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}, nil
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})
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}
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