hhs
032de796c8
## 主要变更 ### 文档重构（减少 1199 行，-23%） - 01-架构设计.md: 503→369 行 (-27%)，删除 DDL/配置示例，精简鉴权/存储描述 - 02-接口文档.md: 1313→570 行 (-57%)，删除 Go 接口/Orchestrator 实现/配置管理 - 07-成本控制.md: 65→59 行 (-9%)，代码块替换为文件引用 ### 文档编号规范化 - 08-功能创意.md → 删除（内容整合到 README.md "功能扩展方向"） - 10-Eino框架与编排设计.md → 08-Eino框架与编排设计.md - 情景切换.md → 09-情景切换.md - 12-鉴权体系.md → 10-鉴权体系.md - 13-令牌桶限流.md → 11-令牌桶限流.md ### 交叉引用更新 - 01-架构设计.md: 更新对鉴权体系/令牌桶限流的引用为新编号 - README.md: 更新文档索引表、推荐阅读顺序、新增功能扩展方向 ### 删除过时文档 - 09-技术名词解释.md（内容已整合到 03-技术选型.md） - 10-Eino重构方案.md（历史记录，已完成） - 11-Eino框架技术文档.md（已合并到 08） - 情景切换功能完整文档.md（已规范化为 09） ## 重构原则 - 架构文档聚焦系统结构，移除实现细节 - 接口文档保留纯契约，删除内部实现 - 编号连续（01-11），语义清晰 - 通过交叉引用连接相关文档，避免重复
117 lines
3.4 KiB
Go
117 lines
3.4 KiB
Go
package eino
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import (
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"context"
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"time"
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openaiImpl "github.com/cloudwego/eino-ext/components/model/openai"
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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/ai/tts"
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"github.com/hhs/camtalk/internal/config"
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"github.com/hhs/camtalk/internal/logger"
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"github.com/hhs/camtalk/internal/models"
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"github.com/hhs/camtalk/internal/session"
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)
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const (
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nodeSTT = "stt"
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nodeHistory = "history"
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nodeLLM = "llm"
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nodeMessageToString = "msg2str"
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nodeSplitter = "splitter"
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nodeTTS = "tts"
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nodeDone = "done"
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)
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// PipelineGraph 封装编译后的 Eino Graph。
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type PipelineGraph struct {
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Runnable compose.Runnable[PipelineInput, PipelineOutput]
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}
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// NewPipelineGraph 构建 CamTalk AI 编排 Graph。
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//
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// 拓扑：START → STT → History → ChatModel → Splitter → TTS → Done → END
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//
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// Graph 使用 Stream 模式调用，ChatModel 实现真正的 token 级流式输出。
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// LLM token 通过 Callback 的 OnEndWithStreamOutput 实时推送到客户端。
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func NewPipelineGraph(
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ctx context.Context,
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cfg *config.Config,
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sttService stt.Service,
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ttsService tts.Service,
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sessionMgr session.Manager,
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) (*PipelineGraph, error) {
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log := logger.Log
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// 1. 创建 eino-ext ChatModel（对接 DashScope OpenAI 兼容接口）
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chatModel, err := openaiImpl.NewChatModel(ctx, &openaiImpl.ChatModelConfig{
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APIKey: cfg.AI.LLM.APIKey,
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Model: cfg.AI.LLM.Model,
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BaseURL: cfg.AI.LLM.Endpoint,
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Timeout: time.Duration(cfg.AI.LLM.Timeout) * time.Second,
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})
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if err != nil {
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return nil, err
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}
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log.Infow("Eino ChatModel 初始化成功",
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"model", cfg.AI.LLM.Model,
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"endpoint", cfg.AI.LLM.Endpoint)
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// 2. 构建 Graph（值类型，非指针）
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g := compose.NewGraph[PipelineInput, PipelineOutput](
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compose.WithGenLocalState(genLocalState),
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)
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// 3. 添加节点
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maxHistory := cfg.Session.MaxHistory
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_ = g.AddLambdaNode(nodeSTT, NewSTTLambda(sttService))
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_ = g.AddLambdaNode(nodeHistory, NewHistoryLambda(sessionMgr.GetHistory, maxHistory))
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_ = g.AddChatModelNode(nodeLLM, chatModel)
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_ = g.AddLambdaNode(nodeMessageToString, NewMessageToStringLambda())
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_ = g.AddLambdaNode(nodeSplitter, NewSplitterLambda())
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_ = g.AddLambdaNode(nodeTTS, NewTTSLambda(
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ttsService,
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cfg.AI.TTS.Voice,
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cfg.AI.TTS.Speed,
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cfg.AI.TTS.OutputFormat,
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cfg.AI.TTS.SampleRate,
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))
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_ = g.AddLambdaNode(nodeDone, NewDoneLambda(cfg.AI.LLM.Model))
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// 4. 连接边
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_ = g.AddEdge(compose.START, nodeSTT)
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_ = g.AddEdge(nodeSTT, nodeHistory)
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_ = g.AddEdge(nodeHistory, nodeLLM)
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_ = g.AddEdge(nodeLLM, nodeMessageToString)
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_ = g.AddEdge(nodeMessageToString, nodeSplitter)
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_ = g.AddEdge(nodeSplitter, nodeTTS)
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_ = g.AddEdge(nodeTTS, nodeDone)
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_ = g.AddEdge(nodeDone, compose.END)
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// 5. 编译（回调在运行时通过 Stream option 传入）
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runnable, err := g.Compile(ctx)
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if err != nil {
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return nil, err
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}
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log.Infow("Eino Graph 编译成功", "nodes", 7)
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return &PipelineGraph{Runnable: runnable}, nil
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}
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// buildPipelineInput 从 WebSocket 请求和会话配置构建 Graph 输入。
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func buildPipelineInput(req models.WsQuery, sessionID string, sess *models.Session, audioData, imageData []byte) PipelineInput {
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return PipelineInput{
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AudioData: audioData,
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ImageData: imageData,
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Text: req.Text,
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SessionID: sessionID,
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RequestID: req.RequestID,
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Language: sess.Config.Language,
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Scenario: sess.Config.Scenario,
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TTSEnabled: sess.Config.TTSEnabled,
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}
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}
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