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>
89 lines
2.4 KiB
Go
89 lines
2.4 KiB
Go
package eino
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import (
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"context"
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"time"
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"github.com/cloudwego/eino/compose"
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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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// ctxKeyStartTime 请求开始时间的 context key。
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type ctxKeyStartTime struct{}
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// WithStartTime 将请求开始时间注入 context。
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func WithStartTime(ctx context.Context, t time.Time) context.Context {
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return context.WithValue(ctx, ctxKeyStartTime{}, t)
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}
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// latencyFromCtx 从 context 获取开始时间并计算延迟（毫秒）。
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func latencyFromCtx(ctx context.Context) int64 {
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if startTime, ok := ctx.Value(ctxKeyStartTime{}).(time.Time); ok {
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return time.Since(startTime).Milliseconds()
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}
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return 0
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}
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// NewDoneLambda 创建 Done Lambda 节点。
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// 输入: struct{}（TTS 完成信号）→ 输出: *PipelineOutput
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//
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// 从 PipelineState 读取完整回复和 token 用量，发送 llm_done 到客户端。
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// 历史消息追加由适配器负责（避免重复写入）。
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func NewDoneLambda(defaultModel string) *compose.Lambda {
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return compose.InvokableLambda(func(ctx context.Context, _ struct{}) (PipelineOutput, error) {
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log := logger.Log
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sender := senderFromCtx(ctx)
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state := stateFromCtx(ctx)
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if state == nil {
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return PipelineOutput{}, nil
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}
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state.mu.Lock()
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fullResponse := state.FullResponse.String()
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transcribedText := state.TranscribedText
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tokenUsage := state.TokenUsage
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requestID := state.RequestID
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modelName := defaultModel
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state.mu.Unlock()
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// 发送 llm_done
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if sender != nil && requestID != "" {
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done := models.WsLLMDone{
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Type: "llm_done",
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RequestID: requestID,
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FullText: fullResponse,
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Model: modelName,
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LatencyMs: latencyFromCtx(ctx),
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}
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if tokenUsage != nil {
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done.TokensUsed = struct {
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Prompt int `json:"prompt"`
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Completion int `json:"completion"`
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Total int `json:"total"`
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}{
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Prompt: tokenUsage.Prompt,
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Completion: tokenUsage.Completion,
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Total: tokenUsage.Total,
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}
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}
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if err := sender.SendLLMDone(done); err != nil {
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log.Errorw("发送 llm_done 失败", "error", err)
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}
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}
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log.Infow("查询处理完成",
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"request_id", requestID,
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"response_length", len(fullResponse))
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return PipelineOutput{
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TranscribedText: transcribedText,
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FullResponse: fullResponse,
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Model: modelName,
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TokenUsage: tokenUsage,
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}, nil
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})
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}
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