feat: 实现 Eino Graph 构建与 Orchestrator 适配器，切换 main.go

- 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>

backend/internal/eino/graph.go

@@ -0,0 +1,113 @@
+package eino
+
+import (
+	"context"
+	"time"
+
+	openaiImpl "github.com/cloudwego/eino-ext/components/model/openai"
+	"github.com/cloudwego/eino/compose"
+
+	"github.com/hhs/camtalk/internal/ai/stt"
+	"github.com/hhs/camtalk/internal/ai/tts"
+	"github.com/hhs/camtalk/internal/config"
+	"github.com/hhs/camtalk/internal/logger"
+	"github.com/hhs/camtalk/internal/models"
+	"github.com/hhs/camtalk/internal/session"
+)
+
+const (
+	nodeSTT      = "stt"
+	nodeHistory  = "history"
+	nodeLLM      = "llm"
+	nodeSplitter = "splitter"
+	nodeTTS      = "tts"
+	nodeDone     = "done"
+)
+
+// PipelineGraph 封装编译后的 Eino Graph。
+type PipelineGraph struct {
+	Runnable compose.Runnable[PipelineInput, PipelineOutput]
+}
+
+// NewPipelineGraph 构建 CamTalk AI 编排 Graph。
+//
+// 拓扑：START → STT → History → ChatModel → Splitter → TTS → Done → END
+//
+// Graph 使用 Stream 模式调用，ChatModel 实现真正的 token 级流式输出。
+// LLM token 通过 Callback 的 OnEndWithStreamOutput 实时推送到客户端。
+func NewPipelineGraph(
+	ctx context.Context,
+	cfg *config.Config,
+	sttService stt.Service,
+	ttsService tts.Service,
+	sessionMgr session.Manager,
+) (*PipelineGraph, error) {
+	log := logger.Log
+
+	// 1. 创建 eino-ext ChatModel（对接 DashScope OpenAI 兼容接口）
+	chatModel, err := openaiImpl.NewChatModel(ctx, &openaiImpl.ChatModelConfig{
+		APIKey:  cfg.AI.LLM.APIKey,
+		Model:   cfg.AI.LLM.Model,
+		BaseURL: cfg.AI.LLM.Endpoint,
+		Timeout: time.Duration(cfg.AI.LLM.Timeout) * time.Second,
+	})
+	if err != nil {
+		return nil, err
+	}
+	log.Infow("Eino ChatModel 初始化成功",
+		"model", cfg.AI.LLM.Model,
+		"endpoint", cfg.AI.LLM.Endpoint)
+
+	// 2. 构建 Graph（值类型，非指针）
+	g := compose.NewGraph[PipelineInput, PipelineOutput](
+		compose.WithGenLocalState(genLocalState),
+	)
+
+	// 3. 添加节点
+	maxHistory := cfg.Session.MaxHistory
+
+	_ = g.AddLambdaNode(nodeSTT, NewSTTLambda(sttService))
+	_ = g.AddLambdaNode(nodeHistory, NewHistoryLambda(sessionMgr.GetHistory, maxHistory))
+	_ = g.AddChatModelNode(nodeLLM, chatModel)
+	_ = g.AddLambdaNode(nodeSplitter, NewSplitterLambda())
+	_ = g.AddLambdaNode(nodeTTS, NewTTSLambda(
+		ttsService,
+		cfg.AI.TTS.Voice,
+		cfg.AI.TTS.Speed,
+		cfg.AI.TTS.OutputFormat,
+		cfg.AI.TTS.SampleRate,
+	))
+	_ = g.AddLambdaNode(nodeDone, NewDoneLambda(cfg.AI.LLM.Model))
+
+	// 4. 连接边
+	_ = g.AddEdge(compose.START, nodeSTT)
+	_ = g.AddEdge(nodeSTT, nodeHistory)
+	_ = g.AddEdge(nodeHistory, nodeLLM)
+	_ = g.AddEdge(nodeLLM, nodeSplitter)
+	_ = g.AddEdge(nodeSplitter, nodeTTS)
+	_ = g.AddEdge(nodeTTS, nodeDone)
+	_ = g.AddEdge(nodeDone, compose.END)
+
+	// 5. 编译（回调在运行时通过 Stream option 传入）
+	runnable, err := g.Compile(ctx)
+	if err != nil {
+		return nil, err
+	}
+
+	log.Infow("Eino Graph 编译成功", "nodes", 6)
+	return &PipelineGraph{Runnable: runnable}, nil
+}
+
+// buildPipelineInput 从 WebSocket 请求和会话配置构建 Graph 输入。
+func buildPipelineInput(req models.WsQuery, sessionID string, sess *models.Session, audioData, imageData []byte) PipelineInput {
+	return PipelineInput{
+		AudioData:  audioData,
+		ImageData:  imageData,
+		Text:       req.Text,
+		SessionID:  sessionID,
+		RequestID:  req.RequestID,
+		Language:   sess.Config.Language,
+		Scenario:   sess.Config.Scenario,
+		TTSEnabled: sess.Config.TTSEnabled,
+	}
+}