refactor: 用 Eino 框架重构编排层 #133
@@ -2,7 +2,7 @@ name: Deploy
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on:
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push:
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branches: [v2]
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branches: [main, v2]
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jobs:
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deploy:
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116
CamTalk-演讲稿.md
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116
CamTalk-演讲稿.md
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@@ -0,0 +1,116 @@
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## CamTalk — 多模态实时 AI 视觉对话助手 · 演讲稿
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> 面向面试官，预计 10-15 分钟。建议配合架构图或项目文档做演示。
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---
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### 开场（约 1 分钟）
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各位好，今天我想和大家分享一个我主导设计和开发的项目——**CamTalk**，一个多模态实时 AI 视觉对话助手。
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简单来说，用户打开浏览器，对着摄像头，用语音提问，AI 就能同时"看到"画面、"听到"语音，然后用文字和语音自然地回应。整个过程不需要打字，就像一个面对面的助手。
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做这个项目的初衷其实很直接——现在的大模型已经具备多模态能力，但大多数产品还是"上传一张图、输入一段文字"的交互方式。我认为真正的多模态交互应该是**无感的**——用户只需要说话，AI 自己去理解视觉场景，就像两个人面对面聊天一样。
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---
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### 系统架构（约 3 分钟）
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CamTalk 采用三层架构：**前端做轻量预处理，后端做智能编排，云端 AI 服务按需调用**。
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**前端**是 React 18 加 TypeScript，跑在浏览器里。它负责三件事：摄像头和麦克风的采集，边缘侧的预处理——比如语音活动检测和关键帧过滤，以及 UI 渲染。通过 WebSocket 与后端通信。
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**后端**是 Go 写的网关服务，用 Gin 框架做 HTTP 路由，gorilla/websocket 处理长连接。它是整个系统的"大脑"，负责会话管理、AI 编排，以及和各家 AI 服务的对接。每个 WebSocket 连接对应一个 goroutine，天然适合这种长连接场景。
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**AI 服务层**是可插拔的。LLM 默认用 GPT-4o，通过 OpenAI 兼容接口可以随时切换成通义千问等国产模型。语音识别默认 Deepgram，语音合成默认 OpenAI TTS，同时也支持小米的 MiMo 系列作为备选。
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有人可能会问：为什么不直接让前端调用 AI API？这里有三点考虑。第一是**安全性**，API Key 不应该暴露在客户端。第二是**统一管控**，速率限制、成本监控、模型路由这些逻辑集中在网关层更好维护。第三是**可扩展性**，未来加缓存、做负载均衡、多实例部署，都在网关层解决。
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部署方面，我们设计了 Nginx 做同源反向代理，前端静态资源和后端 API 在同一个域名下，天然解决跨域问题。Go 网关可以水平扩展，通过 Redis 共享会话状态。目前也已经配置好了 Docker Compose 一键部署方案，包含前端、后端和 PostgreSQL 三个容器。
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---
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### 核心交互流程（约 3 分钟）
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我想重点讲一下一次完整的交互流程，因为它串起了整个系统最核心的技术挑战。
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用户对着摄像头说了一句话，比如"这是什么花"。首先是**前端**的 VAD——语音活动检测模块——在浏览器端实时检测用户何时开始说话、何时说完。这一步完全在端侧完成，用的是 @ricky0123/vad-web，基于 WebRTC VAD 算法。好处是：用户不说话时不需要上传任何音频，节省约 70% 的无效带宽。
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VAD 检测到语音结束后，前端会同时做两件事：把音频编码成 PCM 格式，以及从摄像头捕获当前画面，一起通过 WebSocket 发给后端。
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后端收到后，启动一个 **AI 编排管道**（我们叫它 Orchestrator）。第一步，把音频发给 STT 服务做语音识别，拿到文字结果。第二步，把识别出的文字、摄像头画面以及对话历史，一起打包发给多模态 LLM 做推理。LLM 以流式方式逐 token 输出。第三步，也是最关键的优化——我们不等待 LLM 输出完再调用 TTS，而是做**句子级切分**：LLM 每输出一个完整句子，就立即送入 TTS 合成并推送给客户端。
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所以客户端的体验是这样的：文字一个 token 一个 token 地出现，几乎同时语音就开始播放了。用户**先看到文字、紧接着听到语音**，感知延迟可以控制在 0.5 秒以内。整个端到端的目标延迟是 1.5 到 2 秒。
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这个"LLM 文本流和 TTS 音频流并行推送"的设计，是我们降低感知延迟最关键的手段。
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---
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### 成本控制（约 2 分钟）
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做实时多模态应用，成本是最容易失控的地方。我在设计之初就把成本控制作为架构级别的考量。
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最直观的例子是视觉链路：如果按 1fps 全量发送画面给 LLM，一个用户每天用 10 分钟，一天就是 60 万帧的 token 消耗，1000 个用户时成本完全不可控。
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我们的核心策略叫**端云协同**——把适合的计算前置到客户端。
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在视觉侧，我们做了三个优化：一是降低采样频率，空闲时 5 秒一帧，用户说话时 1 秒一帧；二是关键帧过滤，通过 Canvas 像素比较计算帧间相似度，画面没有显著变化就不发送；三是只在用户提问时捕获画面，而不是持续上传视频流。
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在语音侧，VAD 在浏览器端检测，只上传有效语音片段，环境噪音和静默时段完全不消耗带宽。
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在推理侧，我们规划了模型分级策略——简单识别类问题走 GPT-4o-mini，深度分析走 GPT-4o，复杂推理走 o1。同时对话历史做了裁剪，前端保留最近 10 轮，后端保留 20 轮，限制每轮的固定 token 开销。
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这些策略综合下来，预估月成本可以从无优化的约 5000 美元降到 300 到 500 美元，降幅大约 90%。
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---
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### 工程设计与取舍（约 2 分钟）
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除了技术实现，我想分享几个设计上的取舍。
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**存储方案的分阶段设计**。MVP 阶段我们用进程内存存会话状态，快速验证核心功能。但代码层面我们已经通过 Repository 接口模式做了抽象——HistoryRepository、UsageRepository 这些接口定义好了，底层实现可以是 Memory、Redis 或 PostgreSQL，通过配置切换。目前 Redis 实现已经就绪，PostgreSQL 的 schema 也设计好了，包括 sessions、messages、usage_daily 三张表。这种渐进式设计让我们既能快速交付，又为后续扩展留好了空间。
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**文档驱动开发**。项目里有一套完整的设计文档，涵盖架构、接口协议、技术选型、成本控制等。我们遵循"文档优先"原则——实现功能前先写设计文档，实现和文档不一致时优先更新文档。这在团队协作中特别重要，接口契约清晰，前后端可以并行开发。
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**WebSocket 协议的可靠性设计**。客户端每 30 秒发心跳，服务端 60 秒没收到心跳就断开。断线后用指数退避加抖动重连——1 秒、2 秒、4 秒、8 秒，最大 30 秒。消息用统一信封格式，所有消息都带 type 字段做类型分发。
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---
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### 用户故事与产品规划（约 2 分钟）
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最后讲一下产品层面的思考。用户故事我按 P0 到 P2 分了三个优先级。
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P0 是 MVP 必做的四个场景：AI 识别画面中的物体、语音对话无需打字、AI 能看到摄像头画面、AI 用语音回答。这四个跑通了，核心价值就成立了。
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P1 是体验增强：AI 主动观察画面变化并提示重要事件、识别画面中的文字做 OCR、以及多轮对话的上下文记忆。
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P2 是进阶探索：比如视障用户的无障碍辅助——AI 实时描述周围环境并提示障碍物，画面中外语内容的实时翻译，以及"观察模式"和"对话模式"的切换。
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优先级判断用两个维度交叉评估：用户价值和实现成本。P0 是高价值且成本合理的，P1 是高价值但成本较高的，P2 是探索性的，验证后再投入。
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目前还有几个功能创意在规划中，包括视频录制、对话翻译、对话总结、手动对话输入，以及对话情景选择——比如面试官模式、英语老师模式、辩论赛模式等。
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---
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### 总结（约 1 分钟）
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总结一下，CamTalk 这个项目有几个我比较满意的设计点。
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第一是**架构清晰**：三层分离，每层职责明确，前端做轻量预处理，后端做智能编排，AI 服务可插拔。
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第二是**体验导向**：从用户感知延迟倒推技术方案，流式并行推送、句子级切分、端侧 VAD 这些手段都是围绕"让对话像真人一样自然"这个目标设计的。
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第三是**成本意识**：从架构层面就融入了成本控制，端云协同、智能采样、模型分级，不是等功能做完再去优化成本。
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第四是**工程成熟度**：接口抽象、文档驱动、渐进式存储升级，为项目的长期演进留好了空间。
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以上就是 CamTalk 项目的整体介绍。谢谢大家，有什么问题我们可以一起讨论。
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---
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> **附：讲解提示**
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>
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> - 如果面试官追问技术深度，可以展开讲 Orchestrator 的管道实现细节（goroutine 并发、context 取消、句子切分算法）或 VAD 参数调优。
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> - 如果追问产品思维，可以展开讲用户故事的优先级判断逻辑，以及观察模式和对话模式的差异设计。
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> - 如果追问可扩展性，可以讲 Redis 共享会话、多 Gateway 水平扩展、模型路由器的规划。
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> - 如果追问成本数据，可以给出具体的 token 消耗计算过程和各种优化手段的量化效果。
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@@ -14,12 +14,11 @@ import (
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"github.com/hhs/camtalk/internal/api"
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"github.com/hhs/camtalk/internal/auth"
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"github.com/hhs/camtalk/internal/ai/llm"
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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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eino "github.com/hhs/camtalk/internal/eino"
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"github.com/hhs/camtalk/internal/logger"
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"github.com/hhs/camtalk/internal/orchestrator"
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"github.com/hhs/camtalk/internal/session"
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"github.com/hhs/camtalk/internal/store"
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"github.com/hhs/camtalk/internal/ws"
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@@ -167,9 +166,6 @@ func main() {
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sttService = stt.NewDeepgramService(cfg.AI.STT.APIKey, cfg.AI.STT.Model, cfg.AI.STT.Endpoint, cfg.AI.STT.Timeout, logger.Log)
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logger.Log.Infow("STT service initialized", "provider", "deepgram", "model", cfg.AI.STT.Model)
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}
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llmService := llm.NewOpenAIService(cfg.AI.LLM.APIKey, cfg.AI.LLM.Model, cfg.AI.LLM.Endpoint, cfg.AI.LLM.Timeout, cfg.AI.LLM.HTTPClientTimeout, logger.Log)
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logger.Log.Infow("LLM service initialized", "provider", cfg.AI.LLM.Provider, "model", cfg.AI.LLM.Model, "endpoint", cfg.AI.LLM.Endpoint, "timeout", cfg.AI.LLM.Timeout)
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var ttsService tts.Service
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switch strings.ToLower(cfg.AI.TTS.Provider) {
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case "mimo", "xiaomi":
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@@ -180,8 +176,12 @@ func main() {
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logger.Log.Infow("TTS service initialized", "provider", "openai", "model", cfg.AI.TTS.Model, "voice", cfg.AI.TTS.Voice, "speed", cfg.AI.TTS.Speed)
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}
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// 初始化 Orchestrator
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orch := orchestrator.New(sttService, llmService, ttsService, sessionMgr, cfg)
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// 初始化 Eino Graph + Orchestrator
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pipelineGraph, err := eino.NewPipelineGraph(ctx, cfg, sttService, ttsService, sessionMgr)
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if err != nil {
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logger.Log.Fatalw("failed to create eino pipeline graph", "error", err)
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}
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orch := eino.NewEinoOrchestrator(pipelineGraph, sessionMgr, cfg.AI.LLM.Model)
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// 初始化认证服务
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tokenMgr := auth.NewTokenManager(
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@@ -3,6 +3,8 @@ module github.com/hhs/camtalk
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go 1.25.0
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require (
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github.com/cloudwego/eino v0.9.9
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github.com/cloudwego/eino-ext/components/model/openai v0.1.13
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github.com/gin-gonic/gin v1.10.0
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github.com/golang-jwt/jwt/v5 v5.3.1
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github.com/google/uuid v1.6.0
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@@ -13,16 +15,22 @@ require (
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github.com/spf13/viper v1.21.0
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github.com/stretchr/testify v1.11.1
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go.uber.org/zap v1.28.0
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golang.org/x/crypto v0.23.0
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golang.org/x/crypto v0.31.0
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)
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require (
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github.com/bytedance/sonic v1.11.6 // indirect
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github.com/bytedance/sonic/loader v0.1.1 // indirect
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github.com/bahlo/generic-list-go v0.2.0 // indirect
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||||
github.com/buger/jsonparser v1.1.1 // indirect
|
||||
github.com/bytedance/gopkg v0.1.3 // indirect
|
||||
github.com/bytedance/sonic v1.15.0 // indirect
|
||||
github.com/bytedance/sonic/loader v0.5.0 // indirect
|
||||
github.com/cespare/xxhash/v2 v2.3.0 // indirect
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||||
github.com/cloudwego/base64x v0.1.4 // indirect
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||||
github.com/cloudwego/iasm v0.2.0 // indirect
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||||
github.com/cloudwego/base64x v0.1.6 // indirect
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||||
github.com/cloudwego/eino-ext/libs/acl/openai v0.1.17 // indirect
|
||||
github.com/davecgh/go-spew v1.1.1 // indirect
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||||
github.com/dustin/go-humanize v1.0.1 // indirect
|
||||
github.com/eino-contrib/jsonschema v1.0.3 // indirect
|
||||
github.com/evanphx/json-patch v0.5.2 // indirect
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github.com/fsnotify/fsnotify v1.9.0 // indirect
|
||||
github.com/gabriel-vasile/mimetype v1.4.3 // indirect
|
||||
github.com/gin-contrib/sse v0.1.0 // indirect
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||||
@@ -31,18 +39,25 @@ require (
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||||
github.com/go-playground/validator/v10 v10.20.0 // indirect
|
||||
github.com/go-viper/mapstructure/v2 v2.4.0 // indirect
|
||||
github.com/goccy/go-json v0.10.2 // indirect
|
||||
github.com/goph/emperror v0.17.2 // indirect
|
||||
github.com/jackc/pgpassfile v1.0.0 // indirect
|
||||
github.com/jackc/pgservicefile v0.0.0-20240606120523-5a60cdf6a761 // indirect
|
||||
github.com/jackc/puddle/v2 v2.2.2 // indirect
|
||||
github.com/json-iterator/go v1.1.12 // indirect
|
||||
github.com/klauspost/cpuid/v2 v2.2.10 // indirect
|
||||
github.com/leodido/go-urn v1.4.0 // indirect
|
||||
github.com/mailru/easyjson v0.7.7 // indirect
|
||||
github.com/mattn/go-isatty v0.0.20 // indirect
|
||||
github.com/meguminnnnnnnnn/go-openai v0.1.2 // indirect
|
||||
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
|
||||
github.com/modern-go/reflect2 v1.0.2 // indirect
|
||||
github.com/nikolalohinski/gonja v1.5.3 // indirect
|
||||
github.com/pelletier/go-toml/v2 v2.2.4 // indirect
|
||||
github.com/pkg/errors v0.9.1 // indirect
|
||||
github.com/pmezard/go-difflib v1.0.0 // indirect
|
||||
github.com/sagikazarmark/locafero v0.11.0 // indirect
|
||||
github.com/sirupsen/logrus v1.9.3 // indirect
|
||||
github.com/slongfield/pyfmt v0.0.0-20220222012616-ea85ff4c361f // indirect
|
||||
github.com/sourcegraph/conc v0.3.1-0.20240121214520-5f936abd7ae8 // indirect
|
||||
github.com/spf13/afero v1.15.0 // indirect
|
||||
github.com/spf13/cast v1.10.0 // indirect
|
||||
@@ -51,10 +66,13 @@ require (
|
||||
github.com/subosito/gotenv v1.6.0 // indirect
|
||||
github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
|
||||
github.com/ugorji/go/codec v1.2.12 // indirect
|
||||
github.com/wk8/go-ordered-map/v2 v2.1.8 // indirect
|
||||
github.com/yargevad/filepathx v1.0.0 // indirect
|
||||
go.uber.org/atomic v1.11.0 // indirect
|
||||
go.uber.org/multierr v1.10.0 // indirect
|
||||
go.yaml.in/yaml/v3 v3.0.4 // indirect
|
||||
golang.org/x/arch v0.8.0 // indirect
|
||||
golang.org/x/arch v0.11.0 // indirect
|
||||
golang.org/x/exp v0.0.0-20230713183714-613f0c0eb8a1 // indirect
|
||||
golang.org/x/net v0.25.0 // indirect
|
||||
golang.org/x/sync v0.17.0 // indirect
|
||||
golang.org/x/sys v0.30.0 // indirect
|
||||
|
||||
126
backend/go.sum
126
backend/go.sum
@@ -1,30 +1,58 @@
|
||||
github.com/airbrake/gobrake v3.6.1+incompatible/go.mod h1:wM4gu3Cn0W0K7GUuVWnlXZU11AGBXMILnrdOU8Kn00o=
|
||||
github.com/bahlo/generic-list-go v0.2.0 h1:5sz/EEAK+ls5wF+NeqDpk5+iNdMDXrh3z3nPnH1Wvgk=
|
||||
github.com/bahlo/generic-list-go v0.2.0/go.mod h1:2KvAjgMlE5NNynlg/5iLrrCCZ2+5xWbdbCW3pNTGyYg=
|
||||
github.com/bitly/go-simplejson v0.5.0/go.mod h1:cXHtHw4XUPsvGaxgjIAn8PhEWG9NfngEKAMDJEczWVA=
|
||||
github.com/bmizerany/assert v0.0.0-20160611221934-b7ed37b82869/go.mod h1:Ekp36dRnpXw/yCqJaO+ZrUyxD+3VXMFFr56k5XYrpB4=
|
||||
github.com/bsm/ginkgo/v2 v2.12.0 h1:Ny8MWAHyOepLGlLKYmXG4IEkioBysk6GpaRTLC8zwWs=
|
||||
github.com/bsm/ginkgo/v2 v2.12.0/go.mod h1:SwYbGRRDovPVboqFv0tPTcG1sN61LM1Z4ARdbAV9g4c=
|
||||
github.com/bsm/gomega v1.27.10 h1:yeMWxP2pV2fG3FgAODIY8EiRE3dy0aeFYt4l7wh6yKA=
|
||||
github.com/bsm/gomega v1.27.10/go.mod h1:JyEr/xRbxbtgWNi8tIEVPUYZ5Dzef52k01W3YH0H+O0=
|
||||
github.com/bytedance/sonic v1.11.6 h1:oUp34TzMlL+OY1OUWxHqsdkgC/Zfc85zGqw9siXjrc0=
|
||||
github.com/bytedance/sonic v1.11.6/go.mod h1:LysEHSvpvDySVdC2f87zGWf6CIKJcAvqab1ZaiQtds4=
|
||||
github.com/bytedance/sonic/loader v0.1.1 h1:c+e5Pt1k/cy5wMveRDyk2X4B9hF4g7an8N3zCYjJFNM=
|
||||
github.com/bytedance/sonic/loader v0.1.1/go.mod h1:ncP89zfokxS5LZrJxl5z0UJcsk4M4yY2JpfqGeCtNLU=
|
||||
github.com/buger/jsonparser v1.1.1 h1:2PnMjfWD7wBILjqQbt530v576A/cAbQvEW9gGIpYMUs=
|
||||
github.com/buger/jsonparser v1.1.1/go.mod h1:6RYKKt7H4d4+iWqouImQ9R2FZql3VbhNgx27UK13J/0=
|
||||
github.com/bugsnag/bugsnag-go v1.4.0/go.mod h1:2oa8nejYd4cQ/b0hMIopN0lCRxU0bueqREvZLWFrtK8=
|
||||
github.com/bugsnag/panicwrap v1.2.0/go.mod h1:D/8v3kj0zr8ZAKg1AQ6crr+5VwKN5eIywRkfhyM/+dE=
|
||||
github.com/bytedance/gopkg v0.1.3 h1:TPBSwH8RsouGCBcMBktLt1AymVo2TVsBVCY4b6TnZ/M=
|
||||
github.com/bytedance/gopkg v0.1.3/go.mod h1:576VvJ+eJgyCzdjS+c4+77QF3p7ubbtiKARP3TxducM=
|
||||
github.com/bytedance/mockey v1.3.0 h1:ONLRdvhqmCfr9rTasUB8ZKCfvbdD2tohOg4u+4Q/ed0=
|
||||
github.com/bytedance/mockey v1.3.0/go.mod h1:1BPHF9sol5R1ud/+0VEHGQq/+i2lN+GTsr3O2Q9IENY=
|
||||
github.com/bytedance/sonic v1.15.0 h1:/PXeWFaR5ElNcVE84U0dOHjiMHQOwNIx3K4ymzh/uSE=
|
||||
github.com/bytedance/sonic v1.15.0/go.mod h1:tFkWrPz0/CUCLEF4ri4UkHekCIcdnkqXw9VduqpJh0k=
|
||||
github.com/bytedance/sonic/loader v0.5.0 h1:gXH3KVnatgY7loH5/TkeVyXPfESoqSBSBEiDd5VjlgE=
|
||||
github.com/bytedance/sonic/loader v0.5.0/go.mod h1:AR4NYCk5DdzZizZ5djGqQ92eEhCCcdf5x77udYiSJRo=
|
||||
github.com/certifi/gocertifi v0.0.0-20190105021004-abcd57078448/go.mod h1:GJKEexRPVJrBSOjoqN5VNOIKJ5Q3RViH6eu3puDRwx4=
|
||||
github.com/cespare/xxhash/v2 v2.3.0 h1:UL815xU9SqsFlibzuggzjXhog7bL6oX9BbNZnL2UFvs=
|
||||
github.com/cespare/xxhash/v2 v2.3.0/go.mod h1:VGX0DQ3Q6kWi7AoAeZDth3/j3BFtOZR5XLFGgcrjCOs=
|
||||
github.com/cloudwego/base64x v0.1.4 h1:jwCgWpFanWmN8xoIUHa2rtzmkd5J2plF/dnLS6Xd/0Y=
|
||||
github.com/cloudwego/base64x v0.1.4/go.mod h1:0zlkT4Wn5C6NdauXdJRhSKRlJvmclQ1hhJgA0rcu/8w=
|
||||
github.com/cloudwego/iasm v0.2.0 h1:1KNIy1I1H9hNNFEEH3DVnI4UujN+1zjpuk6gwHLTssg=
|
||||
github.com/cloudwego/iasm v0.2.0/go.mod h1:8rXZaNYT2n95jn+zTI1sDr+IgcD2GVs0nlbbQPiEFhY=
|
||||
github.com/cloudwego/base64x v0.1.6 h1:t11wG9AECkCDk5fMSoxmufanudBtJ+/HemLstXDLI2M=
|
||||
github.com/cloudwego/base64x v0.1.6/go.mod h1:OFcloc187FXDaYHvrNIjxSe8ncn0OOM8gEHfghB2IPU=
|
||||
github.com/cloudwego/eino v0.9.9 h1:x63hvRif6ANPh9YEPoTIrp1potEeoLQFAjOclKaX/Kg=
|
||||
github.com/cloudwego/eino v0.9.9/go.mod h1:OBD1mrkfkt/pJa4rkg1P0VnaMeOVl7l8IAdEqY//3IQ=
|
||||
github.com/cloudwego/eino-ext/components/model/openai v0.1.13 h1:5XHRTiTD5bt9KQrMHcfvuWNklEC3tpm3XHejdozt9vM=
|
||||
github.com/cloudwego/eino-ext/components/model/openai v0.1.13/go.mod h1:mgIoqYYOc0eECCqvLbEYpOJrQNTNxkwXzSJzFU+v5sQ=
|
||||
github.com/cloudwego/eino-ext/libs/acl/openai v0.1.17 h1:EeVcR1TslRA2IdNW1h/2LaGbPlffwGhQm99jM3zWZiI=
|
||||
github.com/cloudwego/eino-ext/libs/acl/openai v0.1.17/go.mod h1:Zkcx6DPTR2NfWmtSXbhItswGw6hqUezNPhNcke0pOG8=
|
||||
github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
|
||||
github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
|
||||
github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
|
||||
github.com/dustin/go-humanize v1.0.1 h1:GzkhY7T5VNhEkwH0PVJgjz+fX1rhBrR7pRT3mDkpeCY=
|
||||
github.com/dustin/go-humanize v1.0.1/go.mod h1:Mu1zIs6XwVuF/gI1OepvI0qD18qycQx+mFykh5fBlto=
|
||||
github.com/eino-contrib/jsonschema v1.0.3 h1:2Kfsm1xlMV0ssY2nuxshS4AwbLFuqmPmzIjLVJ1Fsp0=
|
||||
github.com/eino-contrib/jsonschema v1.0.3/go.mod h1:cpnX4SyKjWjGC7iN2EbhxaTdLqGjCi0e9DxpLYxddD4=
|
||||
github.com/evanphx/json-patch v0.5.2 h1:xVCHIVMUu1wtM/VkR9jVZ45N3FhZfYMMYGorLCR8P3k=
|
||||
github.com/evanphx/json-patch v0.5.2/go.mod h1:ZWS5hhDbVDyob71nXKNL0+PWn6ToqBHMikGIFbs31qQ=
|
||||
github.com/frankban/quicktest v1.14.6 h1:7Xjx+VpznH+oBnejlPUj8oUpdxnVs4f8XU8WnHkI4W8=
|
||||
github.com/frankban/quicktest v1.14.6/go.mod h1:4ptaffx2x8+WTWXmUCuVU6aPUX1/Mz7zb5vbUoiM6w0=
|
||||
github.com/fsnotify/fsnotify v1.4.7/go.mod h1:jwhsz4b93w/PPRr/qN1Yymfu8t87LnFCMoQvtojpjFo=
|
||||
github.com/fsnotify/fsnotify v1.9.0 h1:2Ml+OJNzbYCTzsxtv8vKSFD9PbJjmhYF14k/jKC7S9k=
|
||||
github.com/fsnotify/fsnotify v1.9.0/go.mod h1:8jBTzvmWwFyi3Pb8djgCCO5IBqzKJ/Jwo8TRcHyHii0=
|
||||
github.com/gabriel-vasile/mimetype v1.4.3 h1:in2uUcidCuFcDKtdcBxlR0rJ1+fsokWf+uqxgUFjbI0=
|
||||
github.com/gabriel-vasile/mimetype v1.4.3/go.mod h1:d8uq/6HKRL6CGdk+aubisF/M5GcPfT7nKyLpA0lbSSk=
|
||||
github.com/getsentry/raven-go v0.2.0/go.mod h1:KungGk8q33+aIAZUIVWZDr2OfAEBsO49PX4NzFV5kcQ=
|
||||
github.com/gin-contrib/sse v0.1.0 h1:Y/yl/+YNO8GZSjAhjMsSuLt29uWRFHdHYUb5lYOV9qE=
|
||||
github.com/gin-contrib/sse v0.1.0/go.mod h1:RHrZQHXnP2xjPF+u1gW/2HnVO7nvIa9PG3Gm+fLHvGI=
|
||||
github.com/gin-gonic/gin v1.10.0 h1:nTuyha1TYqgedzytsKYqna+DfLos46nTv2ygFy86HFU=
|
||||
github.com/gin-gonic/gin v1.10.0/go.mod h1:4PMNQiOhvDRa013RKVbsiNwoyezlm2rm0uX/T7kzp5Y=
|
||||
github.com/go-check/check v0.0.0-20180628173108-788fd7840127 h1:0gkP6mzaMqkmpcJYCFOLkIBwI7xFExG03bbkOkCvUPI=
|
||||
github.com/go-check/check v0.0.0-20180628173108-788fd7840127/go.mod h1:9ES+weclKsC9YodN5RgxqK/VD9HM9JsCSh7rNhMZE98=
|
||||
github.com/go-playground/assert/v2 v2.2.0 h1:JvknZsQTYeFEAhQwI4qEt9cyV5ONwRHC+lYKSsYSR8s=
|
||||
github.com/go-playground/assert/v2 v2.2.0/go.mod h1:VDjEfimB/XKnb+ZQfWdccd7VUvScMdVu0Titje2rxJ4=
|
||||
github.com/go-playground/locales v0.14.1 h1:EWaQ/wswjilfKLTECiXz7Rh+3BjFhfDFKv/oXslEjJA=
|
||||
@@ -37,15 +65,22 @@ github.com/go-viper/mapstructure/v2 v2.4.0 h1:EBsztssimR/CONLSZZ04E8qAkxNYq4Qp9L
|
||||
github.com/go-viper/mapstructure/v2 v2.4.0/go.mod h1:oJDH3BJKyqBA2TXFhDsKDGDTlndYOZ6rGS0BRZIxGhM=
|
||||
github.com/goccy/go-json v0.10.2 h1:CrxCmQqYDkv1z7lO7Wbh2HN93uovUHgrECaO5ZrCXAU=
|
||||
github.com/goccy/go-json v0.10.2/go.mod h1:6MelG93GURQebXPDq3khkgXZkazVtN9CRI+MGFi0w8I=
|
||||
github.com/gofrs/uuid v3.2.0+incompatible/go.mod h1:b2aQJv3Z4Fp6yNu3cdSllBxTCLRxnplIgP/c0N/04lM=
|
||||
github.com/golang-jwt/jwt/v5 v5.3.1 h1:kYf81DTWFe7t+1VvL7eS+jKFVWaUnK9cB1qbwn63YCY=
|
||||
github.com/golang-jwt/jwt/v5 v5.3.1/go.mod h1:fxCRLWMO43lRc8nhHWY6LGqRcf+1gQWArsqaEUEa5bE=
|
||||
github.com/golang/protobuf v1.2.0/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
|
||||
github.com/google/go-cmp v0.6.0 h1:ofyhxvXcZhMsU5ulbFiLKl/XBFqE1GSq7atu8tAmTRI=
|
||||
github.com/google/go-cmp v0.6.0/go.mod h1:17dUlkBOakJ0+DkrSSNjCkIjxS6bF9zb3elmeNGIjoY=
|
||||
github.com/google/gofuzz v1.0.0/go.mod h1:dBl0BpW6vV/+mYPU4Po3pmUjxk6FQPldtuIdl/M65Eg=
|
||||
github.com/google/uuid v1.6.0 h1:NIvaJDMOsjHA8n1jAhLSgzrAzy1Hgr+hNrb57e+94F0=
|
||||
github.com/google/uuid v1.6.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
|
||||
github.com/goph/emperror v0.17.2 h1:yLapQcmEsO0ipe9p5TaN22djm3OFV/TfM/fcYP0/J18=
|
||||
github.com/goph/emperror v0.17.2/go.mod h1:+ZbQ+fUNO/6FNiUo0ujtMjhgad9Xa6fQL9KhH4LNHic=
|
||||
github.com/gopherjs/gopherjs v1.17.2 h1:fQnZVsXk8uxXIStYb0N4bGk7jeyTalG/wsZjQ25dO0g=
|
||||
github.com/gopherjs/gopherjs v1.17.2/go.mod h1:pRRIvn/QzFLrKfvEz3qUuEhtE/zLCWfreZ6J5gM2i+k=
|
||||
github.com/gorilla/websocket v1.5.3 h1:saDtZ6Pbx/0u+bgYQ3q96pZgCzfhKXGPqt7kZ72aNNg=
|
||||
github.com/gorilla/websocket v1.5.3/go.mod h1:YR8l580nyteQvAITg2hZ9XVh4b55+EU/adAjf1fMHhE=
|
||||
github.com/hpcloud/tail v1.0.0/go.mod h1:ab1qPbhIpdTxEkNHXyeSf5vhxWSCs/tWer42PpOxQnU=
|
||||
github.com/jackc/pgpassfile v1.0.0 h1:/6Hmqy13Ss2zCq62VdNG8tM1wchn8zjSGOBJ6icpsIM=
|
||||
github.com/jackc/pgpassfile v1.0.0/go.mod h1:CEx0iS5ambNFdcRtxPj5JhEz+xB6uRky5eyVu/W2HEg=
|
||||
github.com/jackc/pgservicefile v0.0.0-20240606120523-5a60cdf6a761 h1:iCEnooe7UlwOQYpKFhBabPMi4aNAfoODPEFNiAnClxo=
|
||||
@@ -54,37 +89,70 @@ github.com/jackc/pgx/v5 v5.10.0 h1:VhSvgU2jSli8o3AqIEOTJr7rZwAEUVo4E4XhR94Zfr0=
|
||||
github.com/jackc/pgx/v5 v5.10.0/go.mod h1:mal1tBGAFfLHvZzaYh77YS/eC6IX9OWbRV1QIIM0Jn4=
|
||||
github.com/jackc/puddle/v2 v2.2.2 h1:PR8nw+E/1w0GLuRFSmiioY6UooMp6KJv0/61nB7icHo=
|
||||
github.com/jackc/puddle/v2 v2.2.2/go.mod h1:vriiEXHvEE654aYKXXjOvZM39qJ0q+azkZFrfEOc3H4=
|
||||
github.com/jessevdk/go-flags v1.4.0/go.mod h1:4FA24M0QyGHXBuZZK/XkWh8h0e1EYbRYJSGM75WSRxI=
|
||||
github.com/joho/godotenv v1.5.1 h1:7eLL/+HRGLY0ldzfGMeQkb7vMd0as4CfYvUVzLqw0N0=
|
||||
github.com/joho/godotenv v1.5.1/go.mod h1:f4LDr5Voq0i2e/R5DDNOoa2zzDfwtkZa6DnEwAbqwq4=
|
||||
github.com/josharian/intern v1.0.0/go.mod h1:5DoeVV0s6jJacbCEi61lwdGj/aVlrQvzHFFd8Hwg//Y=
|
||||
github.com/json-iterator/go v1.1.12 h1:PV8peI4a0ysnczrg+LtxykD8LfKY9ML6u2jnxaEnrnM=
|
||||
github.com/json-iterator/go v1.1.12/go.mod h1:e30LSqwooZae/UwlEbR2852Gd8hjQvJoHmT4TnhNGBo=
|
||||
github.com/klauspost/cpuid/v2 v2.0.9/go.mod h1:FInQzS24/EEf25PyTYn52gqo7WaD8xa0213Md/qVLRg=
|
||||
github.com/jtolds/gls v4.20.0+incompatible h1:xdiiI2gbIgH/gLH7ADydsJ1uDOEzR8yvV7C0MuV77Wo=
|
||||
github.com/jtolds/gls v4.20.0+incompatible/go.mod h1:QJZ7F/aHp+rZTRtaJ1ow/lLfFfVYBRgL+9YlvaHOwJU=
|
||||
github.com/kardianos/osext v0.0.0-20190222173326-2bc1f35cddc0/go.mod h1:1NbS8ALrpOvjt0rHPNLyCIeMtbizbir8U//inJ+zuB8=
|
||||
github.com/klauspost/cpuid/v2 v2.2.10 h1:tBs3QSyvjDyFTq3uoc/9xFpCuOsJQFNPiAhYdw2skhE=
|
||||
github.com/klauspost/cpuid/v2 v2.2.10/go.mod h1:hqwkgyIinND0mEev00jJYCxPNVRVXFQeu1XKlok6oO0=
|
||||
github.com/knz/go-libedit v1.10.1/go.mod h1:MZTVkCWyz0oBc7JOWP3wNAzd002ZbM/5hgShxwh4x8M=
|
||||
github.com/konsorten/go-windows-terminal-sequences v1.0.1/go.mod h1:T0+1ngSBFLxvqU3pZ+m/2kptfBszLMUkC4ZK/EgS/cQ=
|
||||
github.com/kr/pretty v0.1.0/go.mod h1:dAy3ld7l9f0ibDNOQOHHMYYIIbhfbHSm3C4ZsoJORNo=
|
||||
github.com/kr/pretty v0.3.1 h1:flRD4NNwYAUpkphVc1HcthR4KEIFJ65n8Mw5qdRn3LE=
|
||||
github.com/kr/pretty v0.3.1/go.mod h1:hoEshYVHaxMs3cyo3Yncou5ZscifuDolrwPKZanG3xk=
|
||||
github.com/kr/pty v1.1.1/go.mod h1:pFQYn66WHrOpPYNljwOMqo10TkYh1fy3cYio2l3bCsQ=
|
||||
github.com/kr/text v0.1.0/go.mod h1:4Jbv+DJW3UT/LiOwJeYQe1efqtUx/iVham/4vfdArNI=
|
||||
github.com/kr/text v0.2.0 h1:5Nx0Ya0ZqY2ygV366QzturHI13Jq95ApcVaJBhpS+AY=
|
||||
github.com/kr/text v0.2.0/go.mod h1:eLer722TekiGuMkidMxC/pM04lWEeraHUUmBw8l2grE=
|
||||
github.com/leodido/go-urn v1.4.0 h1:WT9HwE9SGECu3lg4d/dIA+jxlljEa1/ffXKmRjqdmIQ=
|
||||
github.com/leodido/go-urn v1.4.0/go.mod h1:bvxc+MVxLKB4z00jd1z+Dvzr47oO32F/QSNjSBOlFxI=
|
||||
github.com/mailru/easyjson v0.7.7 h1:UGYAvKxe3sBsEDzO8ZeWOSlIQfWFlxbzLZe7hwFURr0=
|
||||
github.com/mailru/easyjson v0.7.7/go.mod h1:xzfreul335JAWq5oZzymOObrkdz5UnU4kGfJJLY9Nlc=
|
||||
github.com/mattn/go-colorable v0.1.2 h1:/bC9yWikZXAL9uJdulbSfyVNIR3n3trXl+v8+1sx8mU=
|
||||
github.com/mattn/go-colorable v0.1.2/go.mod h1:U0ppj6V5qS13XJ6of8GYAs25YV2eR4EVcfRqFIhoBtE=
|
||||
github.com/mattn/go-isatty v0.0.20 h1:xfD0iDuEKnDkl03q4limB+vH+GxLEtL/jb4xVJSWWEY=
|
||||
github.com/mattn/go-isatty v0.0.20/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
|
||||
github.com/meguminnnnnnnnn/go-openai v0.1.2 h1:iXombGGjqjBrmE9WaSidUhhi3YQhf42QTHvHLMkgvCA=
|
||||
github.com/meguminnnnnnnnn/go-openai v0.1.2/go.mod h1:qs96ysDmxhE4BZoU45I43zcyfnaYxU3X+aRzLko/htY=
|
||||
github.com/mgutz/ansi v0.0.0-20170206155736-9520e82c474b h1:j7+1HpAFS1zy5+Q4qx1fWh90gTKwiN4QCGoY9TWyyO4=
|
||||
github.com/mgutz/ansi v0.0.0-20170206155736-9520e82c474b/go.mod h1:01TrycV0kFyexm33Z7vhZRXopbI8J3TDReVlkTgMUxE=
|
||||
github.com/modern-go/concurrent v0.0.0-20180228061459-e0a39a4cb421/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=
|
||||
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd h1:TRLaZ9cD/w8PVh93nsPXa1VrQ6jlwL5oN8l14QlcNfg=
|
||||
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=
|
||||
github.com/modern-go/reflect2 v1.0.2 h1:xBagoLtFs94CBntxluKeaWgTMpvLxC4ur3nMaC9Gz0M=
|
||||
github.com/modern-go/reflect2 v1.0.2/go.mod h1:yWuevngMOJpCy52FWWMvUC8ws7m/LJsjYzDa0/r8luk=
|
||||
github.com/nikolalohinski/gonja v1.5.3 h1:GsA+EEaZDZPGJ8JtpeGN78jidhOlxeJROpqMT9fTj9c=
|
||||
github.com/nikolalohinski/gonja v1.5.3/go.mod h1:RmjwxNiXAEqcq1HeK5SSMmqFJvKOfTfXhkJv6YBtPa4=
|
||||
github.com/onsi/ginkgo v1.6.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE=
|
||||
github.com/onsi/ginkgo v1.8.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE=
|
||||
github.com/onsi/gomega v1.5.0/go.mod h1:ex+gbHU/CVuBBDIJjb2X0qEXbFg53c61hWP/1CpauHY=
|
||||
github.com/pelletier/go-toml/v2 v2.2.4 h1:mye9XuhQ6gvn5h28+VilKrrPoQVanw5PMw/TB0t5Ec4=
|
||||
github.com/pelletier/go-toml/v2 v2.2.4/go.mod h1:2gIqNv+qfxSVS7cM2xJQKtLSTLUE9V8t9Stt+h56mCY=
|
||||
github.com/pkg/errors v0.8.0/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
|
||||
github.com/pkg/errors v0.9.1 h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=
|
||||
github.com/pkg/errors v0.9.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
|
||||
github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
|
||||
github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
|
||||
github.com/redis/go-redis/v9 v9.20.1 h1:sfCU6A8P3dXbKyWes02uxA2baehGux9dZHfEKtsTB1w=
|
||||
github.com/redis/go-redis/v9 v9.20.1/go.mod h1:v/M13XI1PVCDcm01VtPFOADfZtHf8YW3baQf57KlIkA=
|
||||
github.com/rogpeppe/go-internal v1.9.0 h1:73kH8U+JUqXU8lRuOHeVHaa/SZPifC7BkcraZVejAe8=
|
||||
github.com/rogpeppe/go-internal v1.9.0/go.mod h1:WtVeX8xhTBvf0smdhujwtBcq4Qrzq/fJaraNFVN+nFs=
|
||||
github.com/rollbar/rollbar-go v1.0.2/go.mod h1:AcFs5f0I+c71bpHlXNNDbOWJiKwjFDtISeXco0L5PKQ=
|
||||
github.com/sagikazarmark/locafero v0.11.0 h1:1iurJgmM9G3PA/I+wWYIOw/5SyBtxapeHDcg+AAIFXc=
|
||||
github.com/sagikazarmark/locafero v0.11.0/go.mod h1:nVIGvgyzw595SUSUE6tvCp3YYTeHs15MvlmU87WwIik=
|
||||
github.com/sirupsen/logrus v1.2.0/go.mod h1:LxeOpSwHxABJmUn/MG1IvRgCAasNZTLOkJPxbbu5VWo=
|
||||
github.com/sirupsen/logrus v1.9.3 h1:dueUQJ1C2q9oE3F7wvmSGAaVtTmUizReu6fjN8uqzbQ=
|
||||
github.com/sirupsen/logrus v1.9.3/go.mod h1:naHLuLoDiP4jHNo9R0sCBMtWGeIprob74mVsIT4qYEQ=
|
||||
github.com/slongfield/pyfmt v0.0.0-20220222012616-ea85ff4c361f h1:Z2cODYsUxQPofhpYRMQVwWz4yUVpHF+vPi+eUdruUYI=
|
||||
github.com/slongfield/pyfmt v0.0.0-20220222012616-ea85ff4c361f/go.mod h1:JqzWyvTuI2X4+9wOHmKSQCYxybB/8j6Ko43qVmXDuZg=
|
||||
github.com/smarty/assertions v1.15.0 h1:cR//PqUBUiQRakZWqBiFFQ9wb8emQGDb0HeGdqGByCY=
|
||||
github.com/smarty/assertions v1.15.0/go.mod h1:yABtdzeQs6l1brC900WlRNwj6ZR55d7B+E8C6HtKdec=
|
||||
github.com/smartystreets/goconvey v1.8.1 h1:qGjIddxOk4grTu9JPOU31tVfq3cNdBlNa5sSznIX1xY=
|
||||
github.com/smartystreets/goconvey v1.8.1/go.mod h1:+/u4qLyY6x1jReYOp7GOM2FSt8aP9CzCZL03bI28W60=
|
||||
github.com/sourcegraph/conc v0.3.1-0.20240121214520-5f936abd7ae8 h1:+jumHNA0Wrelhe64i8F6HNlS8pkoyMv5sreGx2Ry5Rw=
|
||||
github.com/sourcegraph/conc v0.3.1-0.20240121214520-5f936abd7ae8/go.mod h1:3n1Cwaq1E1/1lhQhtRK2ts/ZwZEhjcQeJQ1RuC6Q/8U=
|
||||
github.com/spf13/afero v1.15.0 h1:b/YBCLWAJdFWJTN9cLhiXXcD7mzKn9Dm86dNnfyQw1I=
|
||||
@@ -96,15 +164,18 @@ github.com/spf13/pflag v1.0.10/go.mod h1:McXfInJRrz4CZXVZOBLb0bTZqETkiAhM9Iw0y3A
|
||||
github.com/spf13/viper v1.21.0 h1:x5S+0EU27Lbphp4UKm1C+1oQO+rKx36vfCoaVebLFSU=
|
||||
github.com/spf13/viper v1.21.0/go.mod h1:P0lhsswPGWD/1lZJ9ny3fYnVqxiegrlNrEmgLjbTCAY=
|
||||
github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
|
||||
github.com/stretchr/objx v0.1.1/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
|
||||
github.com/stretchr/objx v0.4.0/go.mod h1:YvHI0jy2hoMjB+UWwv71VJQ9isScKT/TqJzVSSt89Yw=
|
||||
github.com/stretchr/objx v0.5.0/go.mod h1:Yh+to48EsGEfYuaHDzXPcE3xhTkx73EhmCGUpEOglKo=
|
||||
github.com/stretchr/objx v0.5.2 h1:xuMeJ0Sdp5ZMRXx/aWO6RZxdr3beISkG5/G/aIRr3pY=
|
||||
github.com/stretchr/objx v0.5.2/go.mod h1:FRsXN1f5AsAjCGJKqEizvkpNtU+EGNCLh3NxZ/8L+MA=
|
||||
github.com/stretchr/testify v1.2.2/go.mod h1:a8OnRcib4nhh0OaRAV+Yts87kKdq0PP7pXfy6kDkUVs=
|
||||
github.com/stretchr/testify v1.3.0/go.mod h1:M5WIy9Dh21IEIfnGCwXGc5bZfKNJtfHm1UVUgZn+9EI=
|
||||
github.com/stretchr/testify v1.7.0/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
|
||||
github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
|
||||
github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
|
||||
github.com/stretchr/testify v1.8.1/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4=
|
||||
github.com/stretchr/testify v1.8.4/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
|
||||
github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
|
||||
github.com/stretchr/testify v1.11.1 h1:7s2iGBzp5EwR7/aIZr8ao5+dra3wiQyKjjFuvgVKu7U=
|
||||
github.com/stretchr/testify v1.11.1/go.mod h1:wZwfW3scLgRK+23gO65QZefKpKQRnfz6sD981Nm4B6U=
|
||||
github.com/subosito/gotenv v1.6.0 h1:9NlTDc1FTs4qu0DDq7AEtTPNw6SVm7uBMsUCUjABIf8=
|
||||
@@ -113,30 +184,48 @@ github.com/twitchyliquid64/golang-asm v0.15.1 h1:SU5vSMR7hnwNxj24w34ZyCi/FmDZTkS
|
||||
github.com/twitchyliquid64/golang-asm v0.15.1/go.mod h1:a1lVb/DtPvCB8fslRZhAngC2+aY1QWCk3Cedj/Gdt08=
|
||||
github.com/ugorji/go/codec v1.2.12 h1:9LC83zGrHhuUA9l16C9AHXAqEV/2wBQ4nkvumAE65EE=
|
||||
github.com/ugorji/go/codec v1.2.12/go.mod h1:UNopzCgEMSXjBc6AOMqYvWC1ktqTAfzJZUZgYf6w6lg=
|
||||
github.com/wk8/go-ordered-map/v2 v2.1.8 h1:5h/BUHu93oj4gIdvHHHGsScSTMijfx5PeYkE/fJgbpc=
|
||||
github.com/wk8/go-ordered-map/v2 v2.1.8/go.mod h1:5nJHM5DyteebpVlHnWMV0rPz6Zp7+xBAnxjb1X5vnTw=
|
||||
github.com/x-cray/logrus-prefixed-formatter v0.5.2 h1:00txxvfBM9muc0jiLIEAkAcIMJzfthRT6usrui8uGmg=
|
||||
github.com/x-cray/logrus-prefixed-formatter v0.5.2/go.mod h1:2duySbKsL6M18s5GU7VPsoEPHyzalCE06qoARUCeBBE=
|
||||
github.com/yargevad/filepathx v1.0.0 h1:SYcT+N3tYGi+NvazubCNlvgIPbzAk7i7y2dwg3I5FYc=
|
||||
github.com/yargevad/filepathx v1.0.0/go.mod h1:BprfX/gpYNJHJfc35GjRRpVcwWXS89gGulUIU5tK3tA=
|
||||
github.com/zeebo/xxh3 v1.1.0 h1:s7DLGDK45Dyfg7++yxI0khrfwq9661w9EN78eP/UZVs=
|
||||
github.com/zeebo/xxh3 v1.1.0/go.mod h1:IisAie1LELR4xhVinxWS5+zf1lA4p0MW4T+w+W07F5s=
|
||||
go.uber.org/atomic v1.11.0 h1:ZvwS0R+56ePWxUNi+Atn9dWONBPp/AUETXlHW0DxSjE=
|
||||
go.uber.org/atomic v1.11.0/go.mod h1:LUxbIzbOniOlMKjJjyPfpl4v+PKK2cNJn91OQbhoJI0=
|
||||
go.uber.org/goleak v1.3.0 h1:2K3zAYmnTNqV73imy9J1T3WC+gmCePx2hEGkimedGto=
|
||||
go.uber.org/goleak v1.3.0/go.mod h1:CoHD4mav9JJNrW/WLlf7HGZPjdw8EucARQHekz1X6bE=
|
||||
go.uber.org/mock v0.4.0 h1:VcM4ZOtdbR4f6VXfiOpwpVJDL6lCReaZ6mw31wqh7KU=
|
||||
go.uber.org/mock v0.4.0/go.mod h1:a6FSlNadKUHUa9IP5Vyt1zh4fC7uAwxMutEAscFbkZc=
|
||||
go.uber.org/multierr v1.10.0 h1:S0h4aNzvfcFsC3dRF1jLoaov7oRaKqRGC/pUEJ2yvPQ=
|
||||
go.uber.org/multierr v1.10.0/go.mod h1:20+QtiLqy0Nd6FdQB9TLXag12DsQkrbs3htMFfDN80Y=
|
||||
go.uber.org/zap v1.28.0 h1:IZzaP1Fv73/T/pBMLk4VutPl36uNC+OSUh3JLG3FIjo=
|
||||
go.uber.org/zap v1.28.0/go.mod h1:rDLpOi171uODNm/mxFcuYWxDsqWSAVkFdX4XojSKg/Q=
|
||||
go.yaml.in/yaml/v3 v3.0.4 h1:tfq32ie2Jv2UxXFdLJdh3jXuOzWiL1fo0bu/FbuKpbc=
|
||||
go.yaml.in/yaml/v3 v3.0.4/go.mod h1:DhzuOOF2ATzADvBadXxruRBLzYTpT36CKvDb3+aBEFg=
|
||||
golang.org/x/arch v0.0.0-20210923205945-b76863e36670/go.mod h1:5om86z9Hs0C8fWVUuoMHwpExlXzs5Tkyp9hOrfG7pp8=
|
||||
golang.org/x/arch v0.8.0 h1:3wRIsP3pM4yUptoR96otTUOXI367OS0+c9eeRi9doIc=
|
||||
golang.org/x/arch v0.8.0/go.mod h1:FEVrYAQjsQXMVJ1nsMoVVXPZg6p2JE2mx8psSWTDQys=
|
||||
golang.org/x/crypto v0.23.0 h1:dIJU/v2J8Mdglj/8rJ6UUOM3Zc9zLZxVZwwxMooUSAI=
|
||||
golang.org/x/crypto v0.23.0/go.mod h1:CKFgDieR+mRhux2Lsu27y0fO304Db0wZe70UKqHu0v8=
|
||||
golang.org/x/arch v0.11.0 h1:KXV8WWKCXm6tRpLirl2szsO5j/oOODwZf4hATmGVNs4=
|
||||
golang.org/x/arch v0.11.0/go.mod h1:FEVrYAQjsQXMVJ1nsMoVVXPZg6p2JE2mx8psSWTDQys=
|
||||
golang.org/x/crypto v0.0.0-20180904163835-0709b304e793/go.mod h1:6SG95UA2DQfeDnfUPMdvaQW0Q7yPrPDi9nlGo2tz2b4=
|
||||
golang.org/x/crypto v0.31.0 h1:ihbySMvVjLAeSH1IbfcRTkD/iNscyz8rGzjF/E5hV6U=
|
||||
golang.org/x/crypto v0.31.0/go.mod h1:kDsLvtWBEx7MV9tJOj9bnXsPbxwJQ6csT/x4KIN4Ssk=
|
||||
golang.org/x/exp v0.0.0-20230713183714-613f0c0eb8a1 h1:MGwJjxBy0HJshjDNfLsYO8xppfqWlA5ZT9OhtUUhTNw=
|
||||
golang.org/x/exp v0.0.0-20230713183714-613f0c0eb8a1/go.mod h1:FXUEEKJgO7OQYeo8N01OfiKP8RXMtf6e8aTskBGqWdc=
|
||||
golang.org/x/net v0.0.0-20180906233101-161cd47e91fd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
|
||||
golang.org/x/net v0.25.0 h1:d/OCCoBEUq33pjydKrGQhw7IlUPI2Oylr+8qLx49kac=
|
||||
golang.org/x/net v0.25.0/go.mod h1:JkAGAh7GEvH74S6FOH42FLoXpXbE/aqXSrIQjXgsiwM=
|
||||
golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
|
||||
golang.org/x/sync v0.17.0 h1:l60nONMj9l5drqw6jlhIELNv9I0A4OFgRsG9k2oT9Ug=
|
||||
golang.org/x/sync v0.17.0/go.mod h1:9KTHXmSnoGruLpwFjVSX0lNNA75CykiMECbovNTZqGI=
|
||||
golang.org/x/sys v0.0.0-20180905080454-ebe1bf3edb33/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
|
||||
golang.org/x/sys v0.0.0-20180909124046-d0be0721c37e/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
|
||||
golang.org/x/sys v0.0.0-20220715151400-c0bba94af5f8/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
|
||||
golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
|
||||
golang.org/x/sys v0.30.0 h1:QjkSwP/36a20jFYWkSue1YwXzLmsV5Gfq7Eiy72C1uc=
|
||||
golang.org/x/sys v0.30.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
|
||||
golang.org/x/term v0.28.0 h1:/Ts8HFuMR2E6IP/jlo7QVLZHggjKQbhu/7H0LJFr3Gg=
|
||||
golang.org/x/term v0.28.0/go.mod h1:Sw/lC2IAUZ92udQNf3WodGtn4k/XoLyZoh8v/8uiwek=
|
||||
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
|
||||
golang.org/x/text v0.29.0 h1:1neNs90w9YzJ9BocxfsQNHKuAT4pkghyXc4nhZ6sJvk=
|
||||
golang.org/x/text v0.29.0/go.mod h1:7MhJOA9CD2qZyOKYazxdYMF85OwPdEr9jTtBpO7ydH4=
|
||||
google.golang.org/protobuf v1.34.1 h1:9ddQBjfCyZPOHPUiPxpYESBLc+T8P3E+Vo4IbKZgFWg=
|
||||
@@ -144,8 +233,9 @@ google.golang.org/protobuf v1.34.1/go.mod h1:c6P6GXX6sHbq/GpV6MGZEdwhWPcYBgnhAHh
|
||||
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
|
||||
gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
|
||||
gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c/go.mod h1:JHkPIbrfpd72SG/EVd6muEfDQjcINNoR0C8j2r3qZ4Q=
|
||||
gopkg.in/fsnotify.v1 v1.4.7/go.mod h1:Tz8NjZHkW78fSQdbUxIjBTcgA1z1m8ZHf0WmKUhAMys=
|
||||
gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7/go.mod h1:dt/ZhP58zS4L8KSrWDmTeBkI65Dw0HsyUHuEVlX15mw=
|
||||
gopkg.in/yaml.v2 v2.2.1/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
|
||||
gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
|
||||
gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
|
||||
gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
|
||||
nullprogram.com/x/optparse v1.0.0/go.mod h1:KdyPE+Igbe0jQUrVfMqDMeJQIJZEuyV7pjYmp6pbG50=
|
||||
rsc.io/pdf v0.1.1/go.mod h1:n8OzWcQ6Sp37PL01nO98y4iUCRdTGarVfzxY20ICaU4=
|
||||
|
||||
@@ -1,239 +0,0 @@
|
||||
package llm
|
||||
|
||||
import (
|
||||
"bufio"
|
||||
"bytes"
|
||||
"context"
|
||||
"encoding/base64"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"net/http"
|
||||
"strings"
|
||||
"time"
|
||||
|
||||
"go.uber.org/zap"
|
||||
)
|
||||
|
||||
// OpenAIService 基于 OpenAI Chat Completions API 的 LLM 实现。
|
||||
type OpenAIService struct {
|
||||
apiKey string
|
||||
model string
|
||||
endpoint string
|
||||
timeout time.Duration
|
||||
logger *zap.SugaredLogger
|
||||
client *http.Client
|
||||
}
|
||||
|
||||
// NewOpenAIService 创建 OpenAI LLM 服务。
|
||||
// model、endpoint 由 config 层保证非空。
|
||||
func NewOpenAIService(apiKey, model, endpoint string, timeoutSec, httpClientTimeoutSec int, logger *zap.SugaredLogger) *OpenAIService {
|
||||
timeout := time.Duration(timeoutSec) * time.Second
|
||||
if timeout <= 0 {
|
||||
timeout = 10 * time.Second
|
||||
}
|
||||
httpClientTimeout := time.Duration(httpClientTimeoutSec) * time.Second
|
||||
if httpClientTimeout <= 0 {
|
||||
httpClientTimeout = 60 * time.Second
|
||||
}
|
||||
return &OpenAIService{
|
||||
apiKey: apiKey,
|
||||
model: model,
|
||||
endpoint: endpoint,
|
||||
timeout: timeout,
|
||||
logger: logger,
|
||||
client: &http.Client{Timeout: httpClientTimeout},
|
||||
}
|
||||
}
|
||||
|
||||
// --- OpenAI API 请求/响应结构 ---
|
||||
|
||||
type chatRequest struct {
|
||||
Model string `json:"model"`
|
||||
Messages []chatMessage `json:"messages"`
|
||||
Stream bool `json:"stream"`
|
||||
}
|
||||
|
||||
type chatMessage struct {
|
||||
Role string `json:"role"`
|
||||
Content []contentPart `json:"content"`
|
||||
}
|
||||
|
||||
type contentPart struct {
|
||||
Type string `json:"type"`
|
||||
Text string `json:"text"`
|
||||
ImageURL *imageURL `json:"image_url,omitempty"`
|
||||
}
|
||||
|
||||
type imageURL struct {
|
||||
URL string `json:"url"`
|
||||
}
|
||||
|
||||
// streamDelta SSE 流式响应的单个 delta。
|
||||
type streamDelta struct {
|
||||
Choices []struct {
|
||||
Delta struct {
|
||||
Content string `json:"content"`
|
||||
} `json:"delta"`
|
||||
FinishReason *string `json:"finish_reason"`
|
||||
} `json:"choices"`
|
||||
Usage *struct {
|
||||
PromptTokens int `json:"prompt_tokens"`
|
||||
CompletionTokens int `json:"completion_tokens"`
|
||||
TotalTokens int `json:"total_tokens"`
|
||||
} `json:"usage"`
|
||||
Model string `json:"model"`
|
||||
}
|
||||
|
||||
// ChatStream 实现 llm.Service。调用 OpenAI Chat Completions API 流式推理。
|
||||
func (o *OpenAIService) ChatStream(ctx context.Context, req Request) (<-chan Chunk, error) {
|
||||
// 构建请求
|
||||
messages := o.buildMessages(req)
|
||||
|
||||
body := chatRequest{
|
||||
Model: o.model,
|
||||
Messages: messages,
|
||||
Stream: true,
|
||||
}
|
||||
|
||||
payload, err := json.Marshal(body)
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("llm: marshal request: %w", err)
|
||||
}
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("llm: marshal request: %w", err)
|
||||
}
|
||||
|
||||
// 创建带超时的 context
|
||||
ctx, cancel := context.WithTimeout(ctx, o.timeout)
|
||||
|
||||
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, o.endpoint+"/chat/completions", bytes.NewReader(payload))
|
||||
if err != nil {
|
||||
cancel()
|
||||
return nil, fmt.Errorf("llm: create request: %w", err)
|
||||
}
|
||||
httpReq.Header.Set("Content-Type", "application/json")
|
||||
httpReq.Header.Set("Authorization", "Bearer "+o.apiKey)
|
||||
|
||||
resp, err := o.client.Do(httpReq)
|
||||
if err != nil {
|
||||
cancel()
|
||||
return nil, fmt.Errorf("llm: send request: %w", err)
|
||||
}
|
||||
|
||||
if resp.StatusCode != http.StatusOK {
|
||||
cancel()
|
||||
bodyBytes, _ := io.ReadAll(resp.Body)
|
||||
resp.Body.Close()
|
||||
return nil, fmt.Errorf("llm: api error (status %d): %s", resp.StatusCode, string(bodyBytes))
|
||||
}
|
||||
|
||||
// 启动 goroutine 解析 SSE 流
|
||||
ch := make(chan Chunk, 64)
|
||||
go func() {
|
||||
defer close(ch)
|
||||
defer cancel()
|
||||
defer resp.Body.Close()
|
||||
|
||||
o.parseSSEStream(resp.Body, ch)
|
||||
}()
|
||||
|
||||
return ch, nil
|
||||
}
|
||||
|
||||
// parseSSEStream 解析 SSE 流，将 delta 发送到 channel。
|
||||
func (o *OpenAIService) parseSSEStream(body io.Reader, ch chan<- Chunk) {
|
||||
scanner := bufio.NewScanner(body)
|
||||
scanner.Buffer(make([]byte, 0, 64*1024), 256*1024)
|
||||
|
||||
var fullText strings.Builder
|
||||
var lastModel string
|
||||
|
||||
for scanner.Scan() {
|
||||
line := scanner.Text()
|
||||
|
||||
// SSE 格式：data: {...}
|
||||
if !strings.HasPrefix(line, "data: ") {
|
||||
continue
|
||||
}
|
||||
data := strings.TrimPrefix(line, "data: ")
|
||||
if data == "[DONE]" {
|
||||
// 流结束，发送最终 chunk
|
||||
ch <- Chunk{Delta: "", Done: true, Model: lastModel}
|
||||
return
|
||||
}
|
||||
|
||||
var delta streamDelta
|
||||
if err := json.Unmarshal([]byte(data), &delta); err != nil {
|
||||
o.logger.Warnw("llm: unmarshal delta failed", "error", err, "data", data)
|
||||
continue
|
||||
}
|
||||
|
||||
if delta.Model != "" {
|
||||
lastModel = delta.Model
|
||||
}
|
||||
|
||||
// 提取增量文本
|
||||
if len(delta.Choices) > 0 {
|
||||
content := delta.Choices[0].Delta.Content
|
||||
if content != "" {
|
||||
fullText.WriteString(content)
|
||||
ch <- Chunk{Delta: content, Done: false, Model: lastModel}
|
||||
}
|
||||
|
||||
// 某些模型在最后一个 choice 中携带 usage
|
||||
if delta.Choices[0].FinishReason != nil && delta.Usage != nil {
|
||||
ch <- Chunk{
|
||||
Delta: "",
|
||||
Done: true,
|
||||
Model: lastModel,
|
||||
TokensUsed: &TokenUsage{
|
||||
Prompt: delta.Usage.PromptTokens,
|
||||
Completion: delta.Usage.CompletionTokens,
|
||||
Total: delta.Usage.TotalTokens,
|
||||
},
|
||||
}
|
||||
return
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// scanner 结束但没收到 [DONE]
|
||||
if err := scanner.Err(); err != nil {
|
||||
o.logger.Warnw("llm: scan error", "error", err)
|
||||
}
|
||||
ch <- Chunk{Delta: "", Done: true, Model: lastModel}
|
||||
}
|
||||
|
||||
// buildMessages 构建 OpenAI Chat API 的 messages 数组。
|
||||
func (o *OpenAIService) buildMessages(req Request) []chatMessage {
|
||||
var messages []chatMessage
|
||||
|
||||
// System prompt（情景覆盖优先）
|
||||
messages = append(messages, chatMessage{
|
||||
Role: "system",
|
||||
Content: []contentPart{{Type: "text", Text: BuildSystemPrompt(req.Language, "", req.SystemPrompt)}},
|
||||
})
|
||||
|
||||
// 历史消息
|
||||
for _, msg := range req.History {
|
||||
messages = append(messages, chatMessage{
|
||||
Role: msg.Role,
|
||||
Content: []contentPart{{Type: "text", Text: msg.Content}},
|
||||
})
|
||||
}
|
||||
|
||||
// 当前用户消息（图像 + 文本）
|
||||
var parts []contentPart
|
||||
if len(req.Image) > 0 {
|
||||
b64 := base64.StdEncoding.EncodeToString(req.Image)
|
||||
parts = append(parts, contentPart{
|
||||
Type: "image_url",
|
||||
ImageURL: &imageURL{URL: "data:image/jpeg;base64," + b64},
|
||||
})
|
||||
}
|
||||
parts = append(parts, contentPart{Type: "text", Text: req.Text})
|
||||
messages = append(messages, chatMessage{Role: "user", Content: parts})
|
||||
|
||||
return messages
|
||||
}
|
||||
@@ -1,251 +0,0 @@
|
||||
package llm
|
||||
|
||||
import (
|
||||
"context"
|
||||
"fmt"
|
||||
"net/http"
|
||||
"net/http/httptest"
|
||||
"strings"
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
"go.uber.org/zap"
|
||||
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
)
|
||||
|
||||
// mockLLMServer 创建模拟 OpenAI SSE 流式响应的 HTTP 服务器。
|
||||
func mockLLMServer(t *testing.T, handler http.HandlerFunc) *httptest.Server {
|
||||
t.Helper()
|
||||
return httptest.NewServer(handler)
|
||||
}
|
||||
|
||||
func TestOpenAIService_ChatStream_Success(t *testing.T) {
|
||||
srv := mockLLMServer(t, func(w http.ResponseWriter, r *http.Request) {
|
||||
// 验证请求
|
||||
if r.Method != http.MethodPost {
|
||||
t.Errorf("method = %s, want POST", r.Method)
|
||||
}
|
||||
if !strings.Contains(r.URL.Path, "/chat/completions") {
|
||||
t.Errorf("path = %s, should contain /chat/completions", r.URL.Path)
|
||||
}
|
||||
auth := r.Header.Get("Authorization")
|
||||
if auth != "Bearer test-key" {
|
||||
t.Errorf("Authorization = %q, want %q", auth, "Bearer test-key")
|
||||
}
|
||||
|
||||
w.Header().Set("Content-Type", "text/event-stream")
|
||||
flusher, ok := w.(http.Flusher)
|
||||
if !ok {
|
||||
t.Fatal("ResponseWriter does not support Flusher")
|
||||
}
|
||||
|
||||
// 发送几个 delta
|
||||
deltas := []string{"你好", "世界", "！"}
|
||||
for _, d := range deltas {
|
||||
fmt.Fprintf(w, "data: {\"choices\":[{\"delta\":{\"content\":\"%s\"}}],\"model\":\"gpt-4o\"}\n\n", d)
|
||||
flusher.Flush()
|
||||
}
|
||||
|
||||
// 发送 [DONE]
|
||||
fmt.Fprintf(w, "data: [DONE]\n\n")
|
||||
flusher.Flush()
|
||||
})
|
||||
defer srv.Close()
|
||||
|
||||
svc := NewOpenAIService("test-key", "gpt-4o", srv.URL, 10, 60, zap.NewNop().Sugar())
|
||||
|
||||
ch, err := svc.ChatStream(context.Background(), Request{
|
||||
Text: "这是什么？",
|
||||
Language: "zh-CN",
|
||||
})
|
||||
if err != nil {
|
||||
t.Fatalf("ChatStream() error: %v", err)
|
||||
}
|
||||
|
||||
var chunks []Chunk
|
||||
for c := range ch {
|
||||
chunks = append(chunks, c)
|
||||
}
|
||||
|
||||
// 应该有 3 个文本 chunk + 1 个 Done chunk
|
||||
if len(chunks) != 4 {
|
||||
t.Fatalf("got %d chunks, want 4", len(chunks))
|
||||
}
|
||||
|
||||
// 验证文本内容
|
||||
if chunks[0].Delta != "你好" {
|
||||
t.Errorf("chunk[0].Delta = %q, want %q", chunks[0].Delta, "你好")
|
||||
}
|
||||
if chunks[1].Delta != "世界" {
|
||||
t.Errorf("chunk[1].Delta = %q, want %q", chunks[1].Delta, "世界")
|
||||
}
|
||||
|
||||
// 验证最后一个 chunk 是 Done
|
||||
last := chunks[len(chunks)-1]
|
||||
if !last.Done {
|
||||
t.Error("last chunk should be Done")
|
||||
}
|
||||
if last.Model != "gpt-4o" {
|
||||
t.Errorf("last chunk Model = %q, want %q", last.Model, "gpt-4o")
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenAIService_ChatStream_WithImage(t *testing.T) {
|
||||
srv := mockLLMServer(t, func(w http.ResponseWriter, r *http.Request) {
|
||||
w.Header().Set("Content-Type", "text/event-stream")
|
||||
fmt.Fprintf(w, "data: {\"choices\":[{\"delta\":{\"content\":\"ok\"}}],\"model\":\"gpt-4o\"}\n\n")
|
||||
fmt.Fprintf(w, "data: [DONE]\n\n")
|
||||
})
|
||||
defer srv.Close()
|
||||
|
||||
svc := NewOpenAIService("test-key", "gpt-4o", srv.URL, 10, 60, zap.NewNop().Sugar())
|
||||
|
||||
ch, err := svc.ChatStream(context.Background(), Request{
|
||||
Image: []byte("fake-jpeg-data"),
|
||||
Text: "描述图片",
|
||||
Language: "zh-CN",
|
||||
})
|
||||
if err != nil {
|
||||
t.Fatalf("ChatStream() error: %v", err)
|
||||
}
|
||||
|
||||
// 消费 channel
|
||||
for range ch {
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenAIService_ChatStream_WithHistory(t *testing.T) {
|
||||
srv := mockLLMServer(t, func(w http.ResponseWriter, r *http.Request) {
|
||||
w.Header().Set("Content-Type", "text/event-stream")
|
||||
fmt.Fprintf(w, "data: {\"choices\":[{\"delta\":{\"content\":\"ok\"}}],\"model\":\"gpt-4o\"}\n\n")
|
||||
fmt.Fprintf(w, "data: [DONE]\n\n")
|
||||
})
|
||||
defer srv.Close()
|
||||
|
||||
svc := NewOpenAIService("test-key", "gpt-4o", srv.URL, 10, 60, zap.NewNop().Sugar())
|
||||
|
||||
ch, err := svc.ChatStream(context.Background(), Request{
|
||||
Text: "继续",
|
||||
Language: "zh-CN",
|
||||
History: []models.Message{
|
||||
{Role: "user", Content: "你好"},
|
||||
{Role: "assistant", Content: "你好！有什么可以帮助你的吗？"},
|
||||
},
|
||||
})
|
||||
if err != nil {
|
||||
t.Fatalf("ChatStream() error: %v", err)
|
||||
}
|
||||
|
||||
for range ch {
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenAIService_ChatStream_APIError(t *testing.T) {
|
||||
srv := mockLLMServer(t, func(w http.ResponseWriter, r *http.Request) {
|
||||
w.Header().Set("Content-Type", "application/json")
|
||||
w.WriteHeader(http.StatusUnauthorized)
|
||||
fmt.Fprintf(w, `{"error":{"message":"Invalid API key"}}`)
|
||||
})
|
||||
defer srv.Close()
|
||||
|
||||
svc := NewOpenAIService("bad-key", "gpt-4o", srv.URL, 10, 60, zap.NewNop().Sugar())
|
||||
|
||||
_, err := svc.ChatStream(context.Background(), Request{
|
||||
Text: "test",
|
||||
})
|
||||
if err == nil {
|
||||
t.Fatal("ChatStream() should return error for 401")
|
||||
}
|
||||
if !strings.Contains(err.Error(), "401") {
|
||||
t.Errorf("error should mention 401, got: %v", err)
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenAIService_ChatStream_Timeout(t *testing.T) {
|
||||
srv := mockLLMServer(t, func(w http.ResponseWriter, r *http.Request) {
|
||||
// 模拟慢响应
|
||||
time.Sleep(5 * time.Second)
|
||||
w.Header().Set("Content-Type", "text/event-stream")
|
||||
fmt.Fprintf(w, "data: {\"choices\":[{\"delta\":{\"content\":\"late\"}}]}\n\n")
|
||||
fmt.Fprintf(w, "data: [DONE]\n\n")
|
||||
})
|
||||
defer srv.Close()
|
||||
|
||||
svc := NewOpenAIService("test-key", "gpt-4o", srv.URL, 1, 60, zap.NewNop().Sugar()) // 1s timeout
|
||||
|
||||
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Second)
|
||||
defer cancel()
|
||||
|
||||
ch, err := svc.ChatStream(ctx, Request{Text: "test"})
|
||||
if err != nil {
|
||||
// 超时可能在建立连接时或读取时发生
|
||||
return
|
||||
}
|
||||
|
||||
// 如果连接成功，消费 channel 应该超时
|
||||
var gotContent bool
|
||||
for c := range ch {
|
||||
if c.Delta != "" {
|
||||
gotContent = true
|
||||
}
|
||||
}
|
||||
if gotContent {
|
||||
t.Error("should not receive content before timeout")
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenAIService_ChatStream_UsageInResponse(t *testing.T) {
|
||||
srv := mockLLMServer(t, func(w http.ResponseWriter, r *http.Request) {
|
||||
w.Header().Set("Content-Type", "text/event-stream")
|
||||
// 带 usage 的最后一个 chunk
|
||||
fmt.Fprintf(w, "data: {\"choices\":[{\"delta\":{\"content\":\"hi\"},\"finish_reason\":\"stop\"}],\"model\":\"gpt-4o\",\"usage\":{\"prompt_tokens\":10,\"completion_tokens\":5,\"total_tokens\":15}}\n\n")
|
||||
fmt.Fprintf(w, "data: [DONE]\n\n")
|
||||
})
|
||||
defer srv.Close()
|
||||
|
||||
svc := NewOpenAIService("test-key", "gpt-4o", srv.URL, 10, 60, zap.NewNop().Sugar())
|
||||
|
||||
ch, err := svc.ChatStream(context.Background(), Request{Text: "test"})
|
||||
if err != nil {
|
||||
t.Fatalf("ChatStream() error: %v", err)
|
||||
}
|
||||
|
||||
var last Chunk
|
||||
for c := range ch {
|
||||
last = c
|
||||
}
|
||||
|
||||
if !last.Done {
|
||||
t.Error("last chunk should be Done")
|
||||
}
|
||||
if last.TokensUsed == nil {
|
||||
t.Fatal("last chunk should have TokensUsed")
|
||||
}
|
||||
if last.TokensUsed.Total != 15 {
|
||||
t.Errorf("TokensUsed.Total = %d, want 15", last.TokensUsed.Total)
|
||||
}
|
||||
}
|
||||
|
||||
func TestBuildSystemPrompt(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
language string
|
||||
detailLevel string
|
||||
wantContain string
|
||||
}{
|
||||
{"chinese default", "zh-CN", "", "视觉助手"},
|
||||
{"chinese high", "zh-CN", "high", "更详细"},
|
||||
{"english default", "en", "", "visual assistant"},
|
||||
{"english high", "en", "high", "detailed"},
|
||||
}
|
||||
|
||||
for _, tt := range tests {
|
||||
t.Run(tt.name, func(t *testing.T) {
|
||||
got := BuildSystemPrompt(tt.language, tt.detailLevel, "")
|
||||
if !strings.Contains(got, tt.wantContain) {
|
||||
t.Errorf("BuildSystemPrompt(%q, %q, \"\") should contain %q", tt.language, tt.detailLevel, tt.wantContain)
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
165
backend/internal/eino/adapter.go
Normal file
165
backend/internal/eino/adapter.go
Normal file
@@ -0,0 +1,165 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"encoding/base64"
|
||||
"io"
|
||||
"time"
|
||||
|
||||
"github.com/cloudwego/eino/compose"
|
||||
|
||||
"github.com/hhs/camtalk/internal/logger"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
"github.com/hhs/camtalk/internal/orchestrator"
|
||||
"github.com/hhs/camtalk/internal/session"
|
||||
)
|
||||
|
||||
// ctxKeySessionID sessionID 的 context key。
|
||||
type ctxKeySessionID struct{}
|
||||
|
||||
// WithSessionID 将 sessionID 注入 context。
|
||||
func WithSessionID(ctx context.Context, sessionID string) context.Context {
|
||||
return context.WithValue(ctx, ctxKeySessionID{}, sessionID)
|
||||
}
|
||||
|
||||
// EinoOrchestrator 实现 orchestrator.Orchestrator 接口。
|
||||
// 将 Eino Graph 包装为现有接口，WS Handler 几乎不用改。
|
||||
type EinoOrchestrator struct {
|
||||
graph *PipelineGraph
|
||||
sessionMgr session.Manager
|
||||
model string
|
||||
callbacks compose.Option // 运行时 Callback option
|
||||
}
|
||||
|
||||
// NewEinoOrchestrator 创建 Eino 编排器适配器。
|
||||
func NewEinoOrchestrator(graph *PipelineGraph, sessionMgr session.Manager, model string) *EinoOrchestrator {
|
||||
return &EinoOrchestrator{
|
||||
graph: graph,
|
||||
sessionMgr: sessionMgr,
|
||||
model: model,
|
||||
callbacks: compose.WithCallbacks(BuildCallbackHandler()),
|
||||
}
|
||||
}
|
||||
|
||||
// ProcessQuery 实现 orchestrator.Orchestrator 接口。
|
||||
func (e *EinoOrchestrator) ProcessQuery(
|
||||
ctx context.Context,
|
||||
sessionID string,
|
||||
req models.WsQuery,
|
||||
history []models.Message,
|
||||
sender orchestrator.Sender,
|
||||
) error {
|
||||
log := logger.Log
|
||||
startTime := time.Now()
|
||||
|
||||
// 1. 设置活跃请求
|
||||
if err := e.sessionMgr.SetActiveRequest(ctx, sessionID, req.RequestID); err != nil {
|
||||
log.Errorw("设置活跃请求失败", "error", err)
|
||||
}
|
||||
defer e.sessionMgr.ClearActiveRequest(ctx, sessionID)
|
||||
|
||||
// 2. 获取会话配置
|
||||
sess, err := e.sessionMgr.Get(ctx, sessionID)
|
||||
if err != nil {
|
||||
log.Errorw("获取会话失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "SESSION_NOT_FOUND",
|
||||
Message: "会话不存在",
|
||||
})
|
||||
return err
|
||||
}
|
||||
|
||||
// 3. 解码音频和图片
|
||||
var audioData []byte
|
||||
if req.Text == "" && req.Audio != "" {
|
||||
audioData, err = base64.StdEncoding.DecodeString(req.Audio)
|
||||
if err != nil {
|
||||
log.Errorw("音频解码失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "INVALID_MESSAGE",
|
||||
Message: "音频数据解码失败",
|
||||
})
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
var imageData []byte
|
||||
if req.Image != "" {
|
||||
imageData, err = base64.StdEncoding.DecodeString(req.Image)
|
||||
if err != nil {
|
||||
log.Errorw("图片解码失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "INVALID_MESSAGE",
|
||||
Message: "图片数据解码失败",
|
||||
})
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
// 4. 构建 Graph 输入
|
||||
input := buildPipelineInput(req, sessionID, sess, audioData, imageData)
|
||||
|
||||
// 5. 注入 context 值（供 Callback 和 Lambda 节点使用）
|
||||
ctx = WithSender(ctx, sender)
|
||||
ctx = WithRequestID(ctx, req.RequestID)
|
||||
ctx = WithSessionID(ctx, sessionID)
|
||||
ctx = WithStartTime(ctx, startTime)
|
||||
ctx = WithPipelineState(ctx, genLocalState(ctx))
|
||||
|
||||
// 6. 追加用户消息到历史
|
||||
if req.Text != "" {
|
||||
_ = e.sessionMgr.AppendMessage(ctx, sessionID, models.Message{
|
||||
Role: "user",
|
||||
Content: req.Text,
|
||||
})
|
||||
}
|
||||
|
||||
// 7. 调用 Graph（Stream 模式 + 运行时 Callback）
|
||||
streamReader, err := e.graph.Runnable.Stream(ctx, input, e.callbacks)
|
||||
if err != nil {
|
||||
log.Errorw("Graph Stream 启动失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "INTERNAL_ERROR",
|
||||
Message: "编排器启动失败",
|
||||
})
|
||||
return err
|
||||
}
|
||||
|
||||
// 8. 消费 StreamReader（触发整条链路执行，side effects 推送消息到客户端）
|
||||
var output PipelineOutput
|
||||
for {
|
||||
o, err := streamReader.Recv()
|
||||
if err != nil {
|
||||
if err == io.EOF {
|
||||
break
|
||||
}
|
||||
log.Errorw("Graph Stream 消费错误", "error", err)
|
||||
break
|
||||
}
|
||||
output = o
|
||||
}
|
||||
|
||||
// 9. 追加助手消息到历史
|
||||
if output.FullResponse != "" {
|
||||
_ = e.sessionMgr.AppendMessage(ctx, sessionID, models.Message{
|
||||
Role: "assistant",
|
||||
Content: output.FullResponse,
|
||||
})
|
||||
}
|
||||
|
||||
latency := time.Since(startTime).Milliseconds()
|
||||
log.Infow("Eino 编排完成",
|
||||
"request_id", req.RequestID,
|
||||
"latency_ms", latency,
|
||||
"session_id", sessionID)
|
||||
|
||||
return nil
|
||||
}
|
||||
133
backend/internal/eino/callback.go
Normal file
133
backend/internal/eino/callback.go
Normal file
@@ -0,0 +1,133 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"io"
|
||||
|
||||
"github.com/cloudwego/eino/callbacks"
|
||||
"github.com/cloudwego/eino/components/model"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
callbacksHelper "github.com/cloudwego/eino/utils/callbacks"
|
||||
|
||||
"github.com/hhs/camtalk/internal/logger"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
"github.com/hhs/camtalk/internal/orchestrator"
|
||||
)
|
||||
|
||||
// context key 类型，避免与其他包冲突。
|
||||
type ctxKeySender struct{}
|
||||
type ctxKeyRequestID struct{}
|
||||
type ctxKeyState struct{}
|
||||
|
||||
// WithSender 将 Sender 注入 context。
|
||||
func WithSender(ctx context.Context, sender orchestrator.Sender) context.Context {
|
||||
return context.WithValue(ctx, ctxKeySender{}, sender)
|
||||
}
|
||||
|
||||
// WithRequestID 将 requestID 注入 context。
|
||||
func WithRequestID(ctx context.Context, requestID string) context.Context {
|
||||
return context.WithValue(ctx, ctxKeyRequestID{}, requestID)
|
||||
}
|
||||
|
||||
// WithPipelineState 将 PipelineState 注入 context。
|
||||
func WithPipelineState(ctx context.Context, state *PipelineState) context.Context {
|
||||
return context.WithValue(ctx, ctxKeyState{}, state)
|
||||
}
|
||||
|
||||
// senderFromCtx 从 context 获取 Sender。
|
||||
func senderFromCtx(ctx context.Context) orchestrator.Sender {
|
||||
s, _ := ctx.Value(ctxKeySender{}).(orchestrator.Sender)
|
||||
return s
|
||||
}
|
||||
|
||||
// requestIDFromCtx 从 context 获取 requestID。
|
||||
func requestIDFromCtx(ctx context.Context) string {
|
||||
s, _ := ctx.Value(ctxKeyRequestID{}).(string)
|
||||
return s
|
||||
}
|
||||
|
||||
// stateFromCtx 从 context 获取 PipelineState。
|
||||
func stateFromCtx(ctx context.Context) *PipelineState {
|
||||
s, _ := ctx.Value(ctxKeyState{}).(*PipelineState)
|
||||
return s
|
||||
}
|
||||
|
||||
// BuildCallbackHandler 构建 Eino Callback Handler。
|
||||
//
|
||||
// 核心职责：ChatModel 节点通过 OnEndWithStreamOutput 逐 token 推送 llm_chunk 到客户端，
|
||||
// 同时累积完整文本到 PipelineState。
|
||||
//
|
||||
// 其他节点的消息推送（stt_result、tts_audio、llm_done）由各 Lambda 内部直接调用 Sender。
|
||||
func BuildCallbackHandler() callbacks.Handler {
|
||||
return callbacksHelper.NewHandlerHelper().
|
||||
ChatModel(&callbacksHelper.ModelCallbackHandler{
|
||||
OnEndWithStreamOutput: func(ctx context.Context, info *callbacks.RunInfo, output *schema.StreamReader[*model.CallbackOutput]) context.Context {
|
||||
log := logger.Log
|
||||
sender := senderFromCtx(ctx)
|
||||
requestID := requestIDFromCtx(ctx)
|
||||
state := stateFromCtx(ctx)
|
||||
|
||||
if sender == nil || requestID == "" {
|
||||
log.Warnw("ModelCallback: missing sender or request_id in context",
|
||||
"node", info.Name)
|
||||
return ctx
|
||||
}
|
||||
|
||||
// 异步消费流，避免阻塞框架的下游处理。
|
||||
// 框架对流做了内部拷贝，此 goroutine 读取独立副本。
|
||||
go func() {
|
||||
defer output.Close()
|
||||
|
||||
for {
|
||||
chunk, err := output.Recv()
|
||||
if err != nil {
|
||||
if err == io.EOF {
|
||||
return
|
||||
}
|
||||
log.Errorw("ModelCallback: stream recv error",
|
||||
"node", info.Name, "error", err)
|
||||
return
|
||||
}
|
||||
|
||||
if chunk == nil || chunk.Message == nil {
|
||||
continue
|
||||
}
|
||||
|
||||
delta := chunk.Message.Content
|
||||
if delta == "" {
|
||||
continue
|
||||
}
|
||||
|
||||
// 推送 llm_chunk 到客户端
|
||||
if err := sender.SendLLMChunk(models.WsLLMChunk{
|
||||
Type: "llm_chunk",
|
||||
RequestID: requestID,
|
||||
Delta: delta,
|
||||
Role: "assistant",
|
||||
}); err != nil {
|
||||
log.Errorw("ModelCallback: send llm_chunk failed", "error", err)
|
||||
}
|
||||
|
||||
// 累积完整文本到 State
|
||||
if state != nil {
|
||||
state.AppendText(delta)
|
||||
}
|
||||
|
||||
// 记录 token 用量（流的最后一帧携带）
|
||||
if chunk.TokenUsage != nil && state != nil {
|
||||
state.mu.Lock()
|
||||
state.TokenUsage = &TokenUsage{
|
||||
Prompt: chunk.TokenUsage.PromptTokens,
|
||||
Completion: chunk.TokenUsage.CompletionTokens,
|
||||
Total: chunk.TokenUsage.TotalTokens,
|
||||
}
|
||||
state.mu.Unlock()
|
||||
}
|
||||
}
|
||||
}()
|
||||
|
||||
return ctx
|
||||
},
|
||||
}).
|
||||
Handler()
|
||||
}
|
||||
116
backend/internal/eino/graph.go
Normal file
116
backend/internal/eino/graph.go
Normal file
@@ -0,0 +1,116 @@
|
||||
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"
|
||||
nodeMessageToString = "msg2str"
|
||||
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(nodeMessageToString, NewMessageToStringLambda())
|
||||
_ = 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, nodeMessageToString)
|
||||
_ = g.AddEdge(nodeMessageToString, 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,
|
||||
}
|
||||
}
|
||||
235
backend/internal/eino/graph_test.go
Normal file
235
backend/internal/eino/graph_test.go
Normal file
@@ -0,0 +1,235 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
"github.com/stretchr/testify/assert"
|
||||
"github.com/stretchr/testify/mock"
|
||||
"github.com/stretchr/testify/require"
|
||||
|
||||
"github.com/hhs/camtalk/internal/ai/stt"
|
||||
"github.com/hhs/camtalk/internal/ai/tts"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
"github.com/hhs/camtalk/internal/orchestrator"
|
||||
)
|
||||
|
||||
// --- Mock STT Service ---
|
||||
|
||||
type mockSTTService struct {
|
||||
mock.Mock
|
||||
}
|
||||
|
||||
func (m *mockSTTService) Recognize(ctx context.Context, audio []byte, opts stt.Options) (string, error) {
|
||||
args := m.Called(ctx, audio, opts)
|
||||
return args.String(0), args.Error(1)
|
||||
}
|
||||
|
||||
// --- Mock TTS Service ---
|
||||
|
||||
type mockTTSService struct {
|
||||
mock.Mock
|
||||
}
|
||||
|
||||
func (m *mockTTSService) SynthesizeStream(ctx context.Context, textStream <-chan string, opts tts.Options) (<-chan tts.Chunk, error) {
|
||||
args := m.Called(ctx, textStream, opts)
|
||||
return args.Get(0).(<-chan tts.Chunk), args.Error(1)
|
||||
}
|
||||
|
||||
// --- Mock Sender ---
|
||||
|
||||
type mockSender struct {
|
||||
mock.Mock
|
||||
STTResults []models.WsSTTResult
|
||||
LLMChunks []models.WsLLMChunk
|
||||
LLMDones []models.WsLLMDone
|
||||
TTSAudios []models.WsTTSAudio
|
||||
Errors []models.WsError
|
||||
}
|
||||
|
||||
func (m *mockSender) SendSTTResult(result models.WsSTTResult) error {
|
||||
m.STTResults = append(m.STTResults, result)
|
||||
return m.Called(result).Error(0)
|
||||
}
|
||||
|
||||
func (m *mockSender) SendLLMChunk(chunk models.WsLLMChunk) error {
|
||||
m.LLMChunks = append(m.LLMChunks, chunk)
|
||||
return m.Called(chunk).Error(0)
|
||||
}
|
||||
|
||||
func (m *mockSender) SendLLMDone(done models.WsLLMDone) error {
|
||||
m.LLMDones = append(m.LLMDones, done)
|
||||
return m.Called(done).Error(0)
|
||||
}
|
||||
|
||||
func (m *mockSender) SendTTSAudio(audio models.WsTTSAudio) error {
|
||||
m.TTSAudios = append(m.TTSAudios, audio)
|
||||
return m.Called(audio).Error(0)
|
||||
}
|
||||
|
||||
func (m *mockSender) SendError(err models.WsError) error {
|
||||
m.Errors = append(m.Errors, err)
|
||||
return m.Called(err).Error(0)
|
||||
}
|
||||
|
||||
// --- Tests ---
|
||||
|
||||
func TestDetectImageMimeType(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
data []byte
|
||||
expected string
|
||||
}{
|
||||
{"JPEG", []byte{0xFF, 0xD8, 0xFF, 0xE0}, "image/jpeg"},
|
||||
{"PNG", []byte{0x89, 0x50, 0x4E, 0x47}, "image/png"},
|
||||
{"GIF", []byte{0x47, 0x49, 0x46, 0x38}, "image/gif"},
|
||||
{"WebP", []byte{0x52, 0x49, 0x46, 0x46}, "image/webp"},
|
||||
{"Unknown", []byte{0x00, 0x00, 0x00}, "image/jpeg"},
|
||||
{"Short", []byte{0xFF}, "image/jpeg"},
|
||||
}
|
||||
|
||||
for _, tt := range tests {
|
||||
t.Run(tt.name, func(t *testing.T) {
|
||||
result := detectImageMimeType(tt.data)
|
||||
assert.Equal(t, tt.expected, result)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestBuildPipelineInput(t *testing.T) {
|
||||
req := models.WsQuery{
|
||||
Text: "你好",
|
||||
RequestID: "req-1",
|
||||
}
|
||||
sess := &models.Session{
|
||||
Config: models.SessionConfig{
|
||||
Language: "zh-CN",
|
||||
Scenario: "free_chat",
|
||||
TTSEnabled: true,
|
||||
},
|
||||
}
|
||||
|
||||
input := buildPipelineInput(req, "sess-1", sess, nil, nil)
|
||||
require.Equal(t, "你好", input.Text)
|
||||
require.Equal(t, "sess-1", input.SessionID)
|
||||
require.Equal(t, "req-1", input.RequestID)
|
||||
require.Equal(t, "zh-CN", input.Language)
|
||||
require.Equal(t, "free_chat", input.Scenario)
|
||||
require.True(t, input.TTSEnabled)
|
||||
}
|
||||
|
||||
func TestBuildPipelineInput_WithAudioData(t *testing.T) {
|
||||
req := models.WsQuery{
|
||||
Audio: "base64audio",
|
||||
RequestID: "req-2",
|
||||
}
|
||||
sess := &models.Session{
|
||||
Config: models.SessionConfig{
|
||||
Language: "en",
|
||||
Scenario: "free_chat",
|
||||
TTSEnabled: false,
|
||||
},
|
||||
}
|
||||
|
||||
audioData := []byte("fake-audio-bytes")
|
||||
imageData := []byte("fake-image-bytes")
|
||||
|
||||
input := buildPipelineInput(req, "sess-2", sess, audioData, imageData)
|
||||
require.Equal(t, audioData, input.AudioData)
|
||||
require.Equal(t, imageData, input.ImageData)
|
||||
require.False(t, input.TTSEnabled)
|
||||
require.Equal(t, "en", input.Language)
|
||||
}
|
||||
|
||||
func TestPipelineState_AppendAndGet(t *testing.T) {
|
||||
state := genLocalState(context.Background())
|
||||
|
||||
state.AppendText("Hello ")
|
||||
state.AppendText("World")
|
||||
|
||||
require.Equal(t, "Hello World", state.GetFullResponse())
|
||||
}
|
||||
|
||||
func TestPipelineState_ConcurrentAccess(t *testing.T) {
|
||||
state := genLocalState(context.Background())
|
||||
|
||||
done := make(chan struct{})
|
||||
go func() {
|
||||
for i := 0; i < 100; i++ {
|
||||
state.AppendText("a")
|
||||
}
|
||||
close(done)
|
||||
}()
|
||||
|
||||
for i := 0; i < 100; i++ {
|
||||
_ = state.GetFullResponse()
|
||||
}
|
||||
|
||||
<-done
|
||||
require.Equal(t, 100, len(state.GetFullResponse()))
|
||||
}
|
||||
|
||||
func TestContextInjection(t *testing.T) {
|
||||
ctx := context.Background()
|
||||
|
||||
sender := &mockSender{}
|
||||
ctx = WithSender(ctx, sender)
|
||||
ctx = WithRequestID(ctx, "req-123")
|
||||
ctx = WithSessionID(ctx, "sess-456")
|
||||
ctx = WithStartTime(ctx, time.Now())
|
||||
ctx = WithPipelineState(ctx, genLocalState(ctx))
|
||||
|
||||
require.NotNil(t, senderFromCtx(ctx))
|
||||
require.Equal(t, "req-123", requestIDFromCtx(ctx))
|
||||
require.NotNil(t, stateFromCtx(ctx))
|
||||
}
|
||||
|
||||
func TestLatencyFromCtx(t *testing.T) {
|
||||
ctx := context.Background()
|
||||
|
||||
// No start time set
|
||||
require.Equal(t, int64(0), latencyFromCtx(ctx))
|
||||
|
||||
// With start time
|
||||
start := time.Now().Add(-100 * time.Millisecond)
|
||||
ctx = WithStartTime(ctx, start)
|
||||
latency := latencyFromCtx(ctx)
|
||||
require.Greater(t, latency, int64(0))
|
||||
require.Less(t, latency, int64(1000)) // should be < 1 second
|
||||
}
|
||||
|
||||
func TestEinoOrchestrator_ImplementsInterface(t *testing.T) {
|
||||
// Compile-time check that EinoOrchestrator implements orchestrator.Orchestrator
|
||||
var _ orchestrator.Orchestrator = (*EinoOrchestrator)(nil)
|
||||
}
|
||||
|
||||
func TestNewSTTLambda_ReturnsNonNil(t *testing.T) {
|
||||
mockSTT := &mockSTTService{}
|
||||
lambda := NewSTTLambda(mockSTT)
|
||||
require.NotNil(t, lambda)
|
||||
}
|
||||
|
||||
func TestNewHistoryLambda_ReturnsNonNil(t *testing.T) {
|
||||
fetcher := func(ctx context.Context, sessionID string, limit int) ([]models.Message, error) {
|
||||
return nil, nil
|
||||
}
|
||||
lambda := NewHistoryLambda(fetcher, 10)
|
||||
require.NotNil(t, lambda)
|
||||
}
|
||||
|
||||
func TestNewSplitterLambda_ReturnsNonNil(t *testing.T) {
|
||||
lambda := NewSplitterLambda()
|
||||
require.NotNil(t, lambda)
|
||||
}
|
||||
|
||||
func TestNewTTSLambda_ReturnsNonNil(t *testing.T) {
|
||||
mockTTS := &mockTTSService{}
|
||||
lambda := NewTTSLambda(mockTTS, "alloy", 1.0, "mp3", 24000)
|
||||
require.NotNil(t, lambda)
|
||||
}
|
||||
|
||||
func TestNewDoneLambda_ReturnsNonNil(t *testing.T) {
|
||||
lambda := NewDoneLambda("test-model")
|
||||
require.NotNil(t, lambda)
|
||||
}
|
||||
88
backend/internal/eino/nodes_done.go
Normal file
88
backend/internal/eino/nodes_done.go
Normal file
@@ -0,0 +1,88 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"time"
|
||||
|
||||
"github.com/cloudwego/eino/compose"
|
||||
|
||||
"github.com/hhs/camtalk/internal/logger"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
)
|
||||
|
||||
// ctxKeyStartTime 请求开始时间的 context key。
|
||||
type ctxKeyStartTime struct{}
|
||||
|
||||
// WithStartTime 将请求开始时间注入 context。
|
||||
func WithStartTime(ctx context.Context, t time.Time) context.Context {
|
||||
return context.WithValue(ctx, ctxKeyStartTime{}, t)
|
||||
}
|
||||
|
||||
// latencyFromCtx 从 context 获取开始时间并计算延迟（毫秒）。
|
||||
func latencyFromCtx(ctx context.Context) int64 {
|
||||
if startTime, ok := ctx.Value(ctxKeyStartTime{}).(time.Time); ok {
|
||||
return time.Since(startTime).Milliseconds()
|
||||
}
|
||||
return 0
|
||||
}
|
||||
|
||||
// NewDoneLambda 创建 Done Lambda 节点。
|
||||
// 输入: struct{}（TTS 完成信号）→ 输出: *PipelineOutput
|
||||
//
|
||||
// 从 PipelineState 读取完整回复和 token 用量，发送 llm_done 到客户端。
|
||||
// 历史消息追加由适配器负责（避免重复写入）。
|
||||
func NewDoneLambda(defaultModel string) *compose.Lambda {
|
||||
return compose.InvokableLambda(func(ctx context.Context, _ struct{}) (PipelineOutput, error) {
|
||||
log := logger.Log
|
||||
sender := senderFromCtx(ctx)
|
||||
state := stateFromCtx(ctx)
|
||||
|
||||
if state == nil {
|
||||
return PipelineOutput{}, nil
|
||||
}
|
||||
|
||||
state.mu.Lock()
|
||||
fullResponse := state.FullResponse.String()
|
||||
transcribedText := state.TranscribedText
|
||||
tokenUsage := state.TokenUsage
|
||||
requestID := state.RequestID
|
||||
modelName := defaultModel
|
||||
state.mu.Unlock()
|
||||
|
||||
// 发送 llm_done
|
||||
if sender != nil && requestID != "" {
|
||||
done := models.WsLLMDone{
|
||||
Type: "llm_done",
|
||||
RequestID: requestID,
|
||||
FullText: fullResponse,
|
||||
Model: modelName,
|
||||
LatencyMs: latencyFromCtx(ctx),
|
||||
}
|
||||
if tokenUsage != nil {
|
||||
done.TokensUsed = struct {
|
||||
Prompt int `json:"prompt"`
|
||||
Completion int `json:"completion"`
|
||||
Total int `json:"total"`
|
||||
}{
|
||||
Prompt: tokenUsage.Prompt,
|
||||
Completion: tokenUsage.Completion,
|
||||
Total: tokenUsage.Total,
|
||||
}
|
||||
}
|
||||
if err := sender.SendLLMDone(done); err != nil {
|
||||
log.Errorw("发送 llm_done 失败", "error", err)
|
||||
}
|
||||
}
|
||||
|
||||
log.Infow("查询处理完成",
|
||||
"request_id", requestID,
|
||||
"response_length", len(fullResponse))
|
||||
|
||||
return PipelineOutput{
|
||||
TranscribedText: transcribedText,
|
||||
FullResponse: fullResponse,
|
||||
Model: modelName,
|
||||
TokenUsage: tokenUsage,
|
||||
}, nil
|
||||
})
|
||||
}
|
||||
126
backend/internal/eino/nodes_history.go
Normal file
126
backend/internal/eino/nodes_history.go
Normal file
@@ -0,0 +1,126 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"encoding/base64"
|
||||
|
||||
"github.com/cloudwego/eino/compose"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
|
||||
"github.com/hhs/camtalk/internal/ai/llm"
|
||||
"github.com/hhs/camtalk/internal/logger"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
)
|
||||
|
||||
// NewHistoryLambda 创建历史组装 Lambda 节点。
|
||||
// 输入: *STTOutput → 输出: []*schema.Message
|
||||
//
|
||||
// 从 PipelineState 读取请求元数据（SessionID、Scenario、ImageData 等），
|
||||
// 构建系统提示词，组装历史消息和当前用户输入（含多模态图片）。
|
||||
func NewHistoryLambda(historyFetcher func(ctx context.Context, sessionID string, limit int) ([]models.Message, error), maxHistory int) *compose.Lambda {
|
||||
return compose.InvokableLambda(func(ctx context.Context, sttOut STTOutput) ([]*schema.Message, error) {
|
||||
log := logger.Log
|
||||
|
||||
// 从 State 读取请求元数据
|
||||
state := stateFromCtx(ctx)
|
||||
if state == nil {
|
||||
return []*schema.Message{}, nil
|
||||
}
|
||||
|
||||
state.mu.Lock()
|
||||
sessionID := state.SessionID
|
||||
requestID := state.RequestID
|
||||
imageData := state.ImageData
|
||||
scenario := state.Scenario
|
||||
detailLevel := state.DetailLevel
|
||||
language := sttOut.Language
|
||||
state.mu.Unlock()
|
||||
|
||||
// 构建系统提示词
|
||||
scenarioPrompt := llm.GetScenarioPrompt(scenario, language)
|
||||
systemPrompt := llm.BuildSystemPrompt(language, detailLevel, scenarioPrompt)
|
||||
|
||||
// 构建 system message（仅文本，多模态内容只能放在 user 角色）
|
||||
systemMsg := &schema.Message{
|
||||
Role: schema.System,
|
||||
Content: systemPrompt,
|
||||
}
|
||||
|
||||
messages := []*schema.Message{systemMsg}
|
||||
|
||||
// 获取并追加历史消息
|
||||
if historyFetcher != nil && sessionID != "" {
|
||||
history, err := historyFetcher(ctx, sessionID, maxHistory)
|
||||
if err != nil {
|
||||
log.Warnw("获取历史消息失败，继续处理", "error", err, "request_id", requestID)
|
||||
} else {
|
||||
for _, msg := range history {
|
||||
messages = append(messages, &schema.Message{
|
||||
Role: schema.RoleType(msg.Role),
|
||||
Content: msg.Content,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 追加当前用户输入（含图片，多模态内容只能放在 user 角色）
|
||||
// 注意：不能同时设置 Content 和 UserInputMultiContent，需要统一放到 MultiContent 中
|
||||
if len(imageData) > 0 {
|
||||
base64Str := base64.StdEncoding.EncodeToString(imageData)
|
||||
mimeType := detectImageMimeType(imageData)
|
||||
parts := []schema.MessageInputPart{
|
||||
{
|
||||
Type: schema.ChatMessagePartTypeText,
|
||||
Text: sttOut.Text,
|
||||
},
|
||||
{
|
||||
Type: schema.ChatMessagePartTypeImageURL,
|
||||
Image: &schema.MessageInputImage{
|
||||
MessagePartCommon: schema.MessagePartCommon{
|
||||
Base64Data: &base64Str,
|
||||
MIMEType: mimeType,
|
||||
},
|
||||
Detail: schema.ImageURLDetailAuto,
|
||||
},
|
||||
},
|
||||
}
|
||||
messages = append(messages, &schema.Message{
|
||||
Role: schema.User,
|
||||
UserInputMultiContent: parts,
|
||||
})
|
||||
} else {
|
||||
messages = append(messages, &schema.Message{
|
||||
Role: schema.User,
|
||||
Content: sttOut.Text,
|
||||
})
|
||||
}
|
||||
|
||||
log.Infow("历史组装完成",
|
||||
"request_id", requestID,
|
||||
"message_count", len(messages),
|
||||
"has_image", len(imageData) > 0,
|
||||
"scenario", scenario)
|
||||
|
||||
return messages, nil
|
||||
})
|
||||
}
|
||||
|
||||
// detectImageMimeType 简单检测图片 MIME 类型。
|
||||
func detectImageMimeType(data []byte) string {
|
||||
if len(data) < 4 {
|
||||
return "image/jpeg"
|
||||
}
|
||||
if data[0] == 0xFF && data[1] == 0xD8 && data[2] == 0xFF {
|
||||
return "image/jpeg"
|
||||
}
|
||||
if data[0] == 0x89 && data[1] == 0x50 && data[2] == 0x4E && data[3] == 0x47 {
|
||||
return "image/png"
|
||||
}
|
||||
if data[0] == 0x47 && data[1] == 0x49 && data[2] == 0x46 {
|
||||
return "image/gif"
|
||||
}
|
||||
if data[0] == 0x52 && data[1] == 0x49 && data[2] == 0x46 && data[3] == 0x46 {
|
||||
return "image/webp"
|
||||
}
|
||||
return "image/jpeg"
|
||||
}
|
||||
102
backend/internal/eino/nodes_splitter.go
Normal file
102
backend/internal/eino/nodes_splitter.go
Normal file
@@ -0,0 +1,102 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"io"
|
||||
"strings"
|
||||
|
||||
"github.com/cloudwego/eino/compose"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
)
|
||||
|
||||
// sentenceDelimiters 句子分隔符集合。
|
||||
var sentenceDelimiters = map[rune]bool{
|
||||
'。': true,
|
||||
'！': true,
|
||||
'？': true,
|
||||
'\n': true,
|
||||
'.': true,
|
||||
'!': true,
|
||||
'?': true,
|
||||
}
|
||||
|
||||
// NewMessageToStringLambda 创建 Message → String 转换 Lambda 节点。
|
||||
// 输入: *schema.Message → 输出: string
|
||||
//
|
||||
// 提取 Message.Content 文本，供 Splitter 节点消费。
|
||||
func NewMessageToStringLambda() *compose.Lambda {
|
||||
return compose.TransformableLambda(func(ctx context.Context, input *schema.StreamReader[*schema.Message]) (*schema.StreamReader[string], error) {
|
||||
sr, sw := schema.Pipe[string](8)
|
||||
|
||||
go func() {
|
||||
defer sw.Close()
|
||||
defer input.Close()
|
||||
|
||||
for {
|
||||
msg, err := input.Recv()
|
||||
if err != nil {
|
||||
if err == io.EOF {
|
||||
return
|
||||
}
|
||||
sw.Send("", err)
|
||||
return
|
||||
}
|
||||
if msg != nil && msg.Content != "" {
|
||||
sw.Send(msg.Content, nil)
|
||||
}
|
||||
}
|
||||
}()
|
||||
|
||||
return sr, nil
|
||||
})
|
||||
}
|
||||
|
||||
// NewSplitterLambda 创建句子分割 Transform Lambda 节点。
|
||||
// 输入: StreamReader[string]（LLM token 流）→ 输出: StreamReader[string]（完整句子流）
|
||||
//
|
||||
// 逐字符累积，按句子分隔符切分。每切出一个完整句子就输出一次，
|
||||
// 供下游 TTS 节点实时合成。
|
||||
func NewSplitterLambda() *compose.Lambda {
|
||||
return compose.TransformableLambda(func(ctx context.Context, input *schema.StreamReader[string]) (*schema.StreamReader[string], error) {
|
||||
sr, sw := schema.Pipe[string](8)
|
||||
|
||||
go func() {
|
||||
defer sw.Close()
|
||||
defer input.Close()
|
||||
|
||||
var buffer strings.Builder
|
||||
|
||||
for {
|
||||
chunk, err := input.Recv()
|
||||
if err != nil {
|
||||
if err == io.EOF {
|
||||
// 流结束，flush 剩余缓冲
|
||||
if buffer.Len() > 0 {
|
||||
text := strings.TrimSpace(buffer.String())
|
||||
if text != "" {
|
||||
sw.Send(text, nil)
|
||||
}
|
||||
}
|
||||
return
|
||||
}
|
||||
sw.Send("", err)
|
||||
return
|
||||
}
|
||||
|
||||
// 逐字符累积，按句子分隔符切分
|
||||
for _, r := range chunk {
|
||||
buffer.WriteRune(r)
|
||||
if sentenceDelimiters[r] {
|
||||
text := strings.TrimSpace(buffer.String())
|
||||
if text != "" {
|
||||
sw.Send(text, nil)
|
||||
}
|
||||
buffer.Reset()
|
||||
}
|
||||
}
|
||||
}
|
||||
}()
|
||||
|
||||
return sr, nil
|
||||
})
|
||||
}
|
||||
132
backend/internal/eino/nodes_stt.go
Normal file
132
backend/internal/eino/nodes_stt.go
Normal file
@@ -0,0 +1,132 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"fmt"
|
||||
"strings"
|
||||
|
||||
"github.com/cloudwego/eino/compose"
|
||||
|
||||
"github.com/hhs/camtalk/internal/ai/stt"
|
||||
"github.com/hhs/camtalk/internal/logger"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
)
|
||||
|
||||
// NewSTTLambda 创建 STT Lambda 节点。
|
||||
// 输入: PipelineInput → 输出: STTOutput
|
||||
//
|
||||
// 文本输入模式：跳过 STT，直接返回用户输入文本。
|
||||
// 语音模式：调用 sttService.Recognize() 进行语音识别。
|
||||
// 识别结果通过 Sender 发送 stt_result 到客户端。
|
||||
func NewSTTLambda(sttService stt.Service) *compose.Lambda {
|
||||
return compose.InvokableLambda(func(ctx context.Context, input PipelineInput) (STTOutput, error) {
|
||||
log := logger.Log
|
||||
sender := senderFromCtx(ctx)
|
||||
requestID := requestIDFromCtx(ctx)
|
||||
|
||||
// 将输入元数据写入 State，供下游节点（History、Done）读取
|
||||
if state := stateFromCtx(ctx); state != nil {
|
||||
state.mu.Lock()
|
||||
state.SessionID = input.SessionID
|
||||
state.RequestID = input.RequestID
|
||||
state.ImageData = input.ImageData
|
||||
state.Scenario = input.Scenario
|
||||
state.DetailLevel = "low"
|
||||
state.Language = input.Language
|
||||
state.TTSEnabled = input.TTSEnabled
|
||||
state.mu.Unlock()
|
||||
}
|
||||
|
||||
// 文本输入模式：跳过 STT
|
||||
if input.Text != "" {
|
||||
log.Infow("使用文本输入，跳过 STT",
|
||||
"request_id", requestID, "text", input.Text)
|
||||
|
||||
// 发送 stt_result 保持前端消息流一致性
|
||||
if sender != nil {
|
||||
if err := sender.SendSTTResult(models.WsSTTResult{
|
||||
Type: "stt_result",
|
||||
RequestID: requestID,
|
||||
Text: input.Text,
|
||||
IsFinal: true,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 stt_result 失败", "error", err)
|
||||
}
|
||||
}
|
||||
|
||||
// 写入 State
|
||||
if state := stateFromCtx(ctx); state != nil {
|
||||
state.mu.Lock()
|
||||
state.TranscribedText = input.Text
|
||||
state.mu.Unlock()
|
||||
}
|
||||
|
||||
return STTOutput{
|
||||
Text: input.Text,
|
||||
Language: input.Language,
|
||||
IsSkipped: true,
|
||||
}, nil
|
||||
}
|
||||
|
||||
// 语音模式：解码音频
|
||||
if len(input.AudioData) == 0 {
|
||||
return STTOutput{}, fmt.Errorf("stt: no audio data provided")
|
||||
}
|
||||
|
||||
log.Infow("开始语音识别",
|
||||
"request_id", requestID, "audio_bytes", len(input.AudioData))
|
||||
|
||||
// 调用 STT 服务
|
||||
text, err := sttService.Recognize(ctx, input.AudioData, stt.Options{
|
||||
Encoding: "pcm_s16le",
|
||||
SampleRate: 16000,
|
||||
Language: input.Language,
|
||||
})
|
||||
if err != nil {
|
||||
log.Errorw("语音识别失败", "error", err, "request_id", requestID)
|
||||
if sender != nil {
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: requestID,
|
||||
Code: "STT_ERROR",
|
||||
Message: "语音识别失败: " + err.Error(),
|
||||
})
|
||||
}
|
||||
return STTOutput{}, fmt.Errorf("stt: recognize: %w", err)
|
||||
}
|
||||
|
||||
// STT 返回空文本
|
||||
if strings.TrimSpace(text) == "" {
|
||||
log.Infow("语音识别结果为空", "request_id", requestID)
|
||||
text = "（未识别到语音）"
|
||||
}
|
||||
|
||||
log.Infow("语音识别完成", "request_id", requestID, "text", text)
|
||||
|
||||
// 发送 stt_result
|
||||
if sender != nil {
|
||||
if err := sender.SendSTTResult(models.WsSTTResult{
|
||||
Type: "stt_result",
|
||||
RequestID: requestID,
|
||||
Text: text,
|
||||
IsFinal: true,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 stt_result 失败", "error", err)
|
||||
}
|
||||
}
|
||||
|
||||
// 写入 State
|
||||
if state := stateFromCtx(ctx); state != nil {
|
||||
state.mu.Lock()
|
||||
state.TranscribedText = text
|
||||
state.mu.Unlock()
|
||||
}
|
||||
|
||||
return STTOutput{
|
||||
Text: text,
|
||||
Language: input.Language,
|
||||
IsSkipped: false,
|
||||
}, nil
|
||||
})
|
||||
}
|
||||
|
||||
116
backend/internal/eino/nodes_tts.go
Normal file
116
backend/internal/eino/nodes_tts.go
Normal file
@@ -0,0 +1,116 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"encoding/base64"
|
||||
"io"
|
||||
|
||||
"github.com/cloudwego/eino/compose"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
|
||||
"github.com/hhs/camtalk/internal/ai/tts"
|
||||
"github.com/hhs/camtalk/internal/logger"
|
||||
"github.com/hhs/camtalk/internal/models"
|
||||
)
|
||||
|
||||
// NewTTSLambda 创建 TTS Transform Lambda 节点。
|
||||
// 输入: StreamReader[string]（句子流）→ 输出: StreamReader[struct{}]（结果流）
|
||||
//
|
||||
// 流式消费每个句子，调用 ttsService.SynthesizeStream() 合成，
|
||||
// 逐 chunk 推送 tts_audio 到客户端。TTS 失败静默跳过。
|
||||
func NewTTSLambda(ttsService tts.Service, ttsVoice string, ttsSpeed float64, ttsOutputFmt string, ttsSampleRate int) *compose.Lambda {
|
||||
return compose.TransformableLambda(func(ctx context.Context, input *schema.StreamReader[string]) (*schema.StreamReader[struct{}], error) {
|
||||
sr, sw := schema.Pipe[struct{}](8)
|
||||
|
||||
go func() {
|
||||
defer sw.Close()
|
||||
defer input.Close()
|
||||
|
||||
log := logger.Log
|
||||
sender := senderFromCtx(ctx)
|
||||
requestID := requestIDFromCtx(ctx)
|
||||
|
||||
if sender == nil || requestID == "" {
|
||||
// 消费并丢弃流
|
||||
for {
|
||||
_, err := input.Recv()
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 收集句子，按批次合成 TTS
|
||||
var sentences []string
|
||||
for {
|
||||
sentence, err := input.Recv()
|
||||
if err != nil {
|
||||
if err == io.EOF {
|
||||
break
|
||||
}
|
||||
log.Errorw("TTS: stream recv error", "error", err, "request_id", requestID)
|
||||
break
|
||||
}
|
||||
if sentence != "" {
|
||||
sentences = append(sentences, sentence)
|
||||
}
|
||||
}
|
||||
|
||||
if len(sentences) == 0 {
|
||||
sw.Send(struct{}{}, nil)
|
||||
return
|
||||
}
|
||||
|
||||
log.Infow("开始 TTS 合成", "request_id", requestID, "sentence_count", len(sentences))
|
||||
|
||||
// 将句子数组转为 channel
|
||||
sentenceCh := make(chan string, len(sentences))
|
||||
for _, s := range sentences {
|
||||
sentenceCh <- s
|
||||
}
|
||||
close(sentenceCh)
|
||||
|
||||
// 调用 TTS 服务
|
||||
ttsStream, err := ttsService.SynthesizeStream(ctx, sentenceCh, tts.Options{
|
||||
Voice: ttsVoice,
|
||||
Speed: ttsSpeed,
|
||||
OutputFmt: ttsOutputFmt,
|
||||
SampleRate: ttsSampleRate,
|
||||
})
|
||||
if err != nil {
|
||||
log.Errorw("TTS 合成启动失败（已跳过）", "error", err, "request_id", requestID)
|
||||
sw.Send(struct{}{}, nil)
|
||||
return
|
||||
}
|
||||
|
||||
// 消费 TTS 音频流，推送到客户端
|
||||
for chunk := range ttsStream {
|
||||
select {
|
||||
case <-ctx.Done():
|
||||
log.Infow("TTS 流被中断", "request_id", requestID)
|
||||
sw.Send(struct{}{}, ctx.Err())
|
||||
return
|
||||
default:
|
||||
}
|
||||
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(chunk.Audio)
|
||||
|
||||
if err := sender.SendTTSAudio(models.WsTTSAudio{
|
||||
Type: "tts_audio",
|
||||
RequestID: requestID,
|
||||
Audio: audioBase64,
|
||||
MimeType: "audio/mp3",
|
||||
IsLast: chunk.IsLast,
|
||||
Final: chunk.Final,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 tts_audio 失败", "error", err)
|
||||
}
|
||||
}
|
||||
|
||||
log.Infow("TTS 合成完成", "request_id", requestID)
|
||||
sw.Send(struct{}{}, nil)
|
||||
}()
|
||||
|
||||
return sr, nil
|
||||
})
|
||||
}
|
||||
45
backend/internal/eino/state.go
Normal file
45
backend/internal/eino/state.go
Normal file
@@ -0,0 +1,45 @@
|
||||
package eino
|
||||
|
||||
import (
|
||||
"context"
|
||||
"strings"
|
||||
"sync"
|
||||
)
|
||||
|
||||
// PipelineState Graph 全局状态，用于跨节点收集数据。
|
||||
// 通过 compose.WithGenLocalState 注册，各节点通过 compose.ProcessState 读写。
|
||||
type PipelineState struct {
|
||||
mu sync.Mutex
|
||||
FullResponse strings.Builder // LLM 完整回复（由 Callback 累积）
|
||||
TranscribedText string // STT 识别文本
|
||||
Model string // 实际使用的模型名
|
||||
TokenUsage *TokenUsage // token 用量
|
||||
|
||||
// 从 PipelineInput 复制的元数据，供下游节点（History、Done）读取
|
||||
SessionID string
|
||||
RequestID string
|
||||
ImageData []byte
|
||||
Scenario string
|
||||
DetailLevel string
|
||||
Language string
|
||||
TTSEnabled bool
|
||||
}
|
||||
|
||||
// genLocalState 创建每请求的 PipelineState 实例。
|
||||
func genLocalState(ctx context.Context) *PipelineState {
|
||||
return &PipelineState{}
|
||||
}
|
||||
|
||||
// AppendText 追加文本到 FullResponse（线程安全）。
|
||||
func (s *PipelineState) AppendText(text string) {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
s.FullResponse.WriteString(text)
|
||||
}
|
||||
|
||||
// GetFullResponse 获取完整回复文本（线程安全）。
|
||||
func (s *PipelineState) GetFullResponse() string {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
return s.FullResponse.String()
|
||||
}
|
||||
37
backend/internal/eino/types.go
Normal file
37
backend/internal/eino/types.go
Normal file
@@ -0,0 +1,37 @@
|
||||
// Package eino 基于 CloudWeGo Eino 框架的 AI 编排层。
|
||||
// 使用 Eino Graph 替代手写 goroutine 管道，实现声明式 STT → LLM → TTS 编排。
|
||||
package eino
|
||||
|
||||
// PipelineInput Graph 统一输入。
|
||||
type PipelineInput struct {
|
||||
AudioData []byte // base64 解码后的音频（可选）
|
||||
ImageData []byte // base64 解码后的图像（可选）
|
||||
Text string // 直接文本输入（可选，跳过 STT）
|
||||
SessionID string
|
||||
RequestID string
|
||||
Language string // zh / en
|
||||
Scenario string // free_chat, interviewer, etc.
|
||||
TTSEnabled bool
|
||||
}
|
||||
|
||||
// PipelineOutput Graph 统一输出。
|
||||
type PipelineOutput struct {
|
||||
TranscribedText string // STT 结果
|
||||
FullResponse string // LLM 完整回复
|
||||
Model string // 实际使用的模型名
|
||||
TokenUsage *TokenUsage // token 用量
|
||||
}
|
||||
|
||||
// STTOutput STT 节点输出。
|
||||
type STTOutput struct {
|
||||
Text string
|
||||
Language string
|
||||
IsSkipped bool // 文本输入模式跳过了 STT
|
||||
}
|
||||
|
||||
// TokenUsage token 用量统计。
|
||||
type TokenUsage struct {
|
||||
Prompt int
|
||||
Completion int
|
||||
Total int
|
||||
}
|
||||
@@ -1,403 +0,0 @@
|
||||
package orchestrator
|
||||
|
||||
import (
|
||||
"context"
|
||||
"encoding/base64"
|
||||
"strings"
|
||||
"sync"
|
||||
"time"
|
||||
"unicode/utf8"
|
||||
|
||||
"github.com/hhs/camtalk/internal/ai/llm"
|
||||
"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"
|
||||
)
|
||||
|
||||
// Pipeline 实现 Orchestrator 接口，管理 STT → LLM → TTS 流式管道。
|
||||
type Pipeline struct {
|
||||
sttService stt.Service
|
||||
llmService llm.Service
|
||||
ttsService tts.Service
|
||||
sessionMgr session.Manager
|
||||
model string // LLM 模型名，用于 llm_done 上报
|
||||
ttsVoice string // TTS 音色
|
||||
ttsSpeed float64 // TTS 语速
|
||||
ttsOutputFmt string // TTS 输出格式
|
||||
ttsSampleRate int // TTS 输出采样率
|
||||
}
|
||||
|
||||
// New 创建 Pipeline 实例。
|
||||
func New(
|
||||
sttService stt.Service,
|
||||
llmService llm.Service,
|
||||
ttsService tts.Service,
|
||||
sessionMgr session.Manager,
|
||||
cfg *config.Config,
|
||||
) *Pipeline {
|
||||
return &Pipeline{
|
||||
sttService: sttService,
|
||||
llmService: llmService,
|
||||
ttsService: ttsService,
|
||||
sessionMgr: sessionMgr,
|
||||
model: cfg.AI.LLM.Model,
|
||||
ttsVoice: cfg.AI.TTS.Voice,
|
||||
ttsSpeed: cfg.AI.TTS.Speed,
|
||||
ttsOutputFmt: cfg.AI.TTS.OutputFormat,
|
||||
ttsSampleRate: cfg.AI.TTS.SampleRate,
|
||||
}
|
||||
}
|
||||
|
||||
// ProcessQuery 实现 Orchestrator 接口。
|
||||
func (p *Pipeline) ProcessQuery(
|
||||
ctx context.Context,
|
||||
sessionID string,
|
||||
req models.WsQuery,
|
||||
history []models.Message,
|
||||
sender Sender,
|
||||
) error {
|
||||
log := logger.Log
|
||||
startTime := time.Now()
|
||||
|
||||
// 解码音频数据（文本输入模式可跳过）
|
||||
var audio []byte
|
||||
if req.Text == "" && req.Audio != "" {
|
||||
var err error
|
||||
audio, err = base64.StdEncoding.DecodeString(req.Audio)
|
||||
if err != nil {
|
||||
log.Errorw("音频解码失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "INVALID_MESSAGE",
|
||||
Message: "音频数据解码失败",
|
||||
})
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
// 解码图片数据（可选）
|
||||
var image []byte
|
||||
if req.Image != "" {
|
||||
var err error
|
||||
image, err = base64.StdEncoding.DecodeString(req.Image)
|
||||
if err != nil {
|
||||
log.Errorw("图片解码失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "INVALID_MESSAGE",
|
||||
Message: "图片数据解码失败",
|
||||
})
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
// 设置活跃请求
|
||||
if err := p.sessionMgr.SetActiveRequest(ctx, sessionID, req.RequestID); err != nil {
|
||||
log.Errorw("设置活跃请求失败", "error", err)
|
||||
}
|
||||
defer p.sessionMgr.ClearActiveRequest(ctx, sessionID)
|
||||
|
||||
// 获取会话配置
|
||||
sess, err := p.sessionMgr.Get(ctx, sessionID)
|
||||
if err != nil {
|
||||
log.Errorw("获取会话失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "SESSION_NOT_FOUND",
|
||||
Message: "会话不存在",
|
||||
})
|
||||
return err
|
||||
}
|
||||
|
||||
// Step 1: 获取用户文本（语音识别或直接使用输入文本）
|
||||
var userText string
|
||||
if req.Text != "" {
|
||||
// 文本输入模式：跳过 STT，直接使用用户输入的文本
|
||||
log.Infow("使用文本输入", "request_id", req.RequestID, "text", req.Text)
|
||||
userText = req.Text
|
||||
|
||||
// 发送 stt_result 以保持前端消息流一致性
|
||||
if err := sender.SendSTTResult(models.WsSTTResult{
|
||||
Type: "stt_result",
|
||||
RequestID: req.RequestID,
|
||||
Text: userText,
|
||||
IsFinal: true,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 STT 结果失败", "error", err)
|
||||
}
|
||||
} else {
|
||||
// 语音模式：执行 STT 语音识别
|
||||
log.Infow("开始语音识别", "request_id", req.RequestID, "audio_bytes", len(audio))
|
||||
sttResult, err := p.sttService.Recognize(ctx, audio, stt.Options{
|
||||
Encoding: "pcm_s16le",
|
||||
SampleRate: 16000,
|
||||
Language: sess.Config.Language,
|
||||
})
|
||||
if err != nil {
|
||||
log.Errorw("语音识别失败", "error", err, "audio_bytes", len(audio))
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "STT_ERROR",
|
||||
Message: "语音识别失败: " + err.Error(),
|
||||
})
|
||||
return err
|
||||
}
|
||||
userText = sttResult
|
||||
|
||||
// STT 返回空文本：未识别到语音，发送结果后直接返回（不调 LLM）
|
||||
if strings.TrimSpace(userText) == "" {
|
||||
log.Infow("语音识别结果为空", "request_id", req.RequestID)
|
||||
userText = "（未识别到语音）"
|
||||
if err := sender.SendSTTResult(models.WsSTTResult{
|
||||
Type: "stt_result",
|
||||
RequestID: req.RequestID,
|
||||
Text: userText,
|
||||
IsFinal: true,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 STT 结果失败", "error", err)
|
||||
}
|
||||
// 发送空的 llm_done 以结束本轮处理
|
||||
latency := time.Since(startTime).Milliseconds()
|
||||
_ = sender.SendLLMDone(models.WsLLMDone{
|
||||
Type: "llm_done",
|
||||
RequestID: req.RequestID,
|
||||
FullText: "",
|
||||
Model: p.model,
|
||||
LatencyMs: latency,
|
||||
})
|
||||
return nil
|
||||
}
|
||||
|
||||
// 发送 STT 结果
|
||||
if err := sender.SendSTTResult(models.WsSTTResult{
|
||||
Type: "stt_result",
|
||||
RequestID: req.RequestID,
|
||||
Text: userText,
|
||||
IsFinal: true,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 STT 结果失败", "error", err)
|
||||
}
|
||||
}
|
||||
|
||||
// 追加用户消息到历史
|
||||
p.sessionMgr.AppendMessage(ctx, sessionID, models.Message{
|
||||
Role: "user",
|
||||
Content: userText,
|
||||
})
|
||||
|
||||
// Step 2+3: LLM 流式推理 + TTS 并行合成
|
||||
log.Infow("开始 LLM 推理", "request_id", req.RequestID, "scenario", sess.Config.Scenario)
|
||||
llmReq := llm.Request{
|
||||
Image: image,
|
||||
Text: userText,
|
||||
History: history,
|
||||
Language: sess.Config.Language,
|
||||
SystemPrompt: llm.GetScenarioPrompt(sess.Config.Scenario, sess.Config.Language),
|
||||
}
|
||||
|
||||
llmStream, err := p.llmService.ChatStream(ctx, llmReq)
|
||||
if err != nil {
|
||||
log.Errorw("LLM 流式推理启动失败", "error", err)
|
||||
sender.SendError(models.WsError{
|
||||
Type: "error",
|
||||
RequestID: req.RequestID,
|
||||
Code: "LLM_ERROR",
|
||||
Message: "LLM 推理失败",
|
||||
})
|
||||
return err
|
||||
}
|
||||
|
||||
// 创建句子切分器
|
||||
sentenceCh := make(chan string, 4)
|
||||
splitter := NewSplitter(sentenceCh)
|
||||
|
||||
// 并行：LLM 消费 + TTS 合成
|
||||
var wg sync.WaitGroup
|
||||
var fullText string
|
||||
var ttsErr error
|
||||
|
||||
// goroutine 1: 消费 LLM token + 句子切分
|
||||
var tokenUsage *llm.TokenUsage
|
||||
wg.Add(1)
|
||||
go func() {
|
||||
defer wg.Done()
|
||||
defer close(sentenceCh)
|
||||
fullText, tokenUsage = p.consumeLLMStream(ctx, llmStream, req.RequestID, sender, splitter)
|
||||
}()
|
||||
|
||||
// goroutine 2: TTS 合成（如果启用）
|
||||
if sess.Config.TTSEnabled {
|
||||
wg.Add(1)
|
||||
go func() {
|
||||
defer wg.Done()
|
||||
log.Infow("开始 TTS 合成", "request_id", req.RequestID)
|
||||
ttsErr = p.synthesizeTTS(ctx, sentenceCh, req.RequestID, sender)
|
||||
}()
|
||||
} else {
|
||||
// 如果 TTS 未启用，需要消费 sentenceCh 防止阻塞
|
||||
go func() {
|
||||
for range sentenceCh {
|
||||
}
|
||||
}()
|
||||
}
|
||||
|
||||
// 等待所有 goroutine 完成
|
||||
wg.Wait()
|
||||
|
||||
// TTS 失败静默跳过
|
||||
if ttsErr != nil {
|
||||
log.Warnw("TTS 合成失败（已跳过）", "error", ttsErr)
|
||||
}
|
||||
|
||||
// 追加助手消息到历史
|
||||
p.sessionMgr.AppendMessage(ctx, sessionID, models.Message{
|
||||
Role: "assistant",
|
||||
Content: fullText,
|
||||
})
|
||||
|
||||
// 发送 llm_done
|
||||
latency := time.Since(startTime).Milliseconds()
|
||||
done := models.WsLLMDone{
|
||||
Type: "llm_done",
|
||||
RequestID: req.RequestID,
|
||||
FullText: fullText,
|
||||
Model: p.model,
|
||||
LatencyMs: latency,
|
||||
}
|
||||
if tokenUsage != nil {
|
||||
done.TokensUsed = struct {
|
||||
Prompt int `json:"prompt"`
|
||||
Completion int `json:"completion"`
|
||||
Total int `json:"total"`
|
||||
}{
|
||||
Prompt: tokenUsage.Prompt,
|
||||
Completion: tokenUsage.Completion,
|
||||
Total: tokenUsage.Total,
|
||||
}
|
||||
}
|
||||
if err := sender.SendLLMDone(done); err != nil {
|
||||
log.Errorw("发送 llm_done 失败", "error", err)
|
||||
}
|
||||
|
||||
log.Infow("查询处理完成",
|
||||
"request_id", req.RequestID,
|
||||
"latency_ms", latency,
|
||||
"text_length", utf8.RuneCountInString(fullText),
|
||||
)
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
// consumeLLMStream 消费 LLM 流式输出，发送 llm_chunk 并进行句子切分。
|
||||
// 返回完整文本和 token 用量。
|
||||
func (p *Pipeline) consumeLLMStream(
|
||||
ctx context.Context,
|
||||
stream <-chan llm.Chunk,
|
||||
requestID string,
|
||||
sender Sender,
|
||||
splitter *Splitter,
|
||||
) (string, *llm.TokenUsage) {
|
||||
log := logger.Log
|
||||
var fullText strings.Builder
|
||||
var tokenUsage *llm.TokenUsage
|
||||
|
||||
for chunk := range stream {
|
||||
// 检查上下文是否已取消
|
||||
select {
|
||||
case <-ctx.Done():
|
||||
log.Infow("LLM 流被中断", "request_id", requestID)
|
||||
return fullText.String(), tokenUsage
|
||||
default:
|
||||
}
|
||||
|
||||
if chunk.Done {
|
||||
// 流结束，记录 token 用量
|
||||
if chunk.TokensUsed != nil {
|
||||
tokenUsage = chunk.TokensUsed
|
||||
log.Infow("LLM 用量统计",
|
||||
"request_id", requestID,
|
||||
"prompt_tokens", tokenUsage.Prompt,
|
||||
"completion_tokens", tokenUsage.Completion,
|
||||
"total_tokens", tokenUsage.Total,
|
||||
)
|
||||
}
|
||||
break
|
||||
}
|
||||
|
||||
// 累积全文
|
||||
fullText.WriteString(chunk.Delta)
|
||||
|
||||
// 发送 llm_chunk
|
||||
if err := sender.SendLLMChunk(models.WsLLMChunk{
|
||||
Type: "llm_chunk",
|
||||
RequestID: requestID,
|
||||
Delta: chunk.Delta,
|
||||
Role: "assistant",
|
||||
}); err != nil {
|
||||
log.Errorw("发送 llm_chunk 失败", "error", err)
|
||||
}
|
||||
|
||||
// 句子切分
|
||||
splitter.Feed(chunk.Delta)
|
||||
}
|
||||
|
||||
// 刷新切分器中的剩余文本
|
||||
splitter.Flush()
|
||||
|
||||
return fullText.String(), tokenUsage
|
||||
}
|
||||
|
||||
// synthesizeTTS 从句子 channel 读取文本，进行 TTS 合成并发送音频。
|
||||
func (p *Pipeline) synthesizeTTS(
|
||||
ctx context.Context,
|
||||
sentenceCh <-chan string,
|
||||
requestID string,
|
||||
sender Sender,
|
||||
) error {
|
||||
log := logger.Log
|
||||
|
||||
ttsStream, err := p.ttsService.SynthesizeStream(ctx, sentenceCh, tts.Options{
|
||||
Voice: p.ttsVoice,
|
||||
Speed: p.ttsSpeed,
|
||||
OutputFmt: p.ttsOutputFmt,
|
||||
SampleRate: p.ttsSampleRate,
|
||||
})
|
||||
if err != nil {
|
||||
log.Errorw("TTS 合成启动失败", "error", err)
|
||||
return err
|
||||
}
|
||||
|
||||
// 消费 TTS 音频流
|
||||
for chunk := range ttsStream {
|
||||
// 检查上下文是否已取消
|
||||
select {
|
||||
case <-ctx.Done():
|
||||
log.Infow("TTS 流被中断", "request_id", requestID)
|
||||
return ctx.Err()
|
||||
default:
|
||||
}
|
||||
|
||||
// Base64 编码音频数据
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(chunk.Audio)
|
||||
|
||||
if err := sender.SendTTSAudio(models.WsTTSAudio{
|
||||
Type: "tts_audio",
|
||||
RequestID: requestID,
|
||||
Audio: audioBase64,
|
||||
MimeType: "audio/mp3",
|
||||
IsLast: chunk.IsLast,
|
||||
Final: chunk.Final,
|
||||
}); err != nil {
|
||||
log.Errorw("发送 tts_audio 失败", "error", err)
|
||||
}
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
@@ -1,713 +0,0 @@
|
||||
package orchestrator
|
||||
|
||||
import (
|
||||
"context"
|
||||
"encoding/base64"
|
||||
"errors"
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
"github.com/stretchr/testify/assert"
|
||||
"github.com/stretchr/testify/mock"
|
||||
|
||||
"github.com/hhs/camtalk/internal/ai/llm"
|
||||
"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"
|
||||
)
|
||||
|
||||
func init() {
|
||||
logger.Init("debug", "console")
|
||||
}
|
||||
|
||||
// MockSTTService mock STT 服务
|
||||
type MockSTTService struct {
|
||||
mock.Mock
|
||||
}
|
||||
|
||||
func (m *MockSTTService) Recognize(ctx context.Context, audio []byte, opts stt.Options) (string, error) {
|
||||
args := m.Called(ctx, audio, opts)
|
||||
return args.String(0), args.Error(1)
|
||||
}
|
||||
|
||||
// MockLLMService mock LLM 服务
|
||||
type MockLLMService struct {
|
||||
mock.Mock
|
||||
}
|
||||
|
||||
func (m *MockLLMService) ChatStream(ctx context.Context, req llm.Request) (<-chan llm.Chunk, error) {
|
||||
args := m.Called(ctx, req)
|
||||
if args.Get(0) == nil {
|
||||
return nil, args.Error(1)
|
||||
}
|
||||
return args.Get(0).(<-chan llm.Chunk), args.Error(1)
|
||||
}
|
||||
|
||||
// MockTTSService mock TTS 服务
|
||||
type MockTTSService struct {
|
||||
mock.Mock
|
||||
}
|
||||
|
||||
func (m *MockTTSService) SynthesizeStream(ctx context.Context, textStream <-chan string, opts tts.Options) (<-chan tts.Chunk, error) {
|
||||
args := m.Called(ctx, textStream, opts)
|
||||
if args.Get(0) == nil {
|
||||
return nil, args.Error(1)
|
||||
}
|
||||
return args.Get(0).(<-chan tts.Chunk), args.Error(1)
|
||||
}
|
||||
|
||||
// MockSessionManager mock 会话管理器
|
||||
type MockSessionManager struct {
|
||||
mock.Mock
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) Create(ctx context.Context, userID string, config models.SessionConfig) (string, error) {
|
||||
args := m.Called(ctx, userID, config)
|
||||
return args.String(0), args.Error(1)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) UpdateTitle(ctx context.Context, sessionID string, title string) error {
|
||||
args := m.Called(ctx, sessionID, title)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) ListByUser(ctx context.Context, userID string, page, size int) ([]session.ConversationSummary, int, error) {
|
||||
args := m.Called(ctx, userID, page, size)
|
||||
return args.Get(0).([]session.ConversationSummary), args.Int(1), args.Error(2)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) Get(ctx context.Context, sessionID string) (*models.Session, error) {
|
||||
args := m.Called(ctx, sessionID)
|
||||
if args.Get(0) == nil {
|
||||
return nil, args.Error(1)
|
||||
}
|
||||
return args.Get(0).(*models.Session), args.Error(1)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) UpdateConfig(ctx context.Context, sessionID string, patch models.SessionConfigPatch) error {
|
||||
args := m.Called(ctx, sessionID, patch)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) GetHistory(ctx context.Context, sessionID string, limit int) ([]models.Message, error) {
|
||||
args := m.Called(ctx, sessionID, limit)
|
||||
return args.Get(0).([]models.Message), args.Error(1)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) AppendMessage(ctx context.Context, sessionID string, msg models.Message) error {
|
||||
args := m.Called(ctx, sessionID, msg)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) SetActiveRequest(ctx context.Context, sessionID string, requestID string) error {
|
||||
args := m.Called(ctx, sessionID, requestID)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) GetActiveRequestID(ctx context.Context, sessionID string) (string, error) {
|
||||
args := m.Called(ctx, sessionID)
|
||||
return args.String(0), args.Error(1)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) ClearActiveRequest(ctx context.Context, sessionID string) error {
|
||||
args := m.Called(ctx, sessionID)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) Touch(ctx context.Context, sessionID string) error {
|
||||
args := m.Called(ctx, sessionID)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) Destroy(ctx context.Context, sessionID string) error {
|
||||
args := m.Called(ctx, sessionID)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSessionManager) ActiveCount() int {
|
||||
args := m.Called()
|
||||
return args.Int(0)
|
||||
}
|
||||
|
||||
// MockSender mock WebSocket 发送器
|
||||
type MockSender struct {
|
||||
mock.Mock
|
||||
STTResults []models.WsSTTResult
|
||||
LLMChunks []models.WsLLMChunk
|
||||
LLMDones []models.WsLLMDone
|
||||
TTSAudios []models.WsTTSAudio
|
||||
Errors []models.WsError
|
||||
}
|
||||
|
||||
func NewMockSender() *MockSender {
|
||||
return &MockSender{
|
||||
STTResults: make([]models.WsSTTResult, 0),
|
||||
LLMChunks: make([]models.WsLLMChunk, 0),
|
||||
LLMDones: make([]models.WsLLMDone, 0),
|
||||
TTSAudios: make([]models.WsTTSAudio, 0),
|
||||
Errors: make([]models.WsError, 0),
|
||||
}
|
||||
}
|
||||
|
||||
func (m *MockSender) SendSTTResult(result models.WsSTTResult) error {
|
||||
m.STTResults = append(m.STTResults, result)
|
||||
args := m.Called(result)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSender) SendLLMChunk(chunk models.WsLLMChunk) error {
|
||||
m.LLMChunks = append(m.LLMChunks, chunk)
|
||||
args := m.Called(chunk)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSender) SendLLMDone(done models.WsLLMDone) error {
|
||||
m.LLMDones = append(m.LLMDones, done)
|
||||
args := m.Called(done)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSender) SendTTSAudio(audio models.WsTTSAudio) error {
|
||||
m.TTSAudios = append(m.TTSAudios, audio)
|
||||
args := m.Called(audio)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
func (m *MockSender) SendError(err models.WsError) error {
|
||||
m.Errors = append(m.Errors, err)
|
||||
args := m.Called(err)
|
||||
return args.Error(0)
|
||||
}
|
||||
|
||||
// 辅助函数：创建 LLM 流式响应
|
||||
func createLLMStream(chunks []llm.Chunk) <-chan llm.Chunk {
|
||||
ch := make(chan llm.Chunk, len(chunks))
|
||||
for _, chunk := range chunks {
|
||||
ch <- chunk
|
||||
}
|
||||
close(ch)
|
||||
return ch
|
||||
}
|
||||
|
||||
// 辅助函数：创建 TTS 流式响应
|
||||
func createTTSStream(chunks []tts.Chunk) <-chan tts.Chunk {
|
||||
ch := make(chan tts.Chunk, len(chunks))
|
||||
for _, chunk := range chunks {
|
||||
ch <- chunk
|
||||
}
|
||||
close(ch)
|
||||
return ch
|
||||
}
|
||||
|
||||
// TestProcessQuery_Success 测试完整流程
|
||||
func TestProcessQuery_Success(t *testing.T) {
|
||||
// 准备测试数据
|
||||
audioData := []byte("test audio")
|
||||
imageData := []byte("test image")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
imageBase64 := base64.StdEncoding.EncodeToString(imageData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Image: imageBase64,
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
session := &models.Session{
|
||||
ID: "session-123",
|
||||
Config: models.SessionConfig{
|
||||
TTSEnabled: true,
|
||||
DetailLevel: "low",
|
||||
Language: "zh-CN",
|
||||
},
|
||||
}
|
||||
|
||||
// 创建 mock
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
// 设置 mock 期望
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(session, nil)
|
||||
mockSession.On("AppendMessage", mock.Anything, "session-123", mock.Anything).Return(nil)
|
||||
|
||||
mockSTT.On("Recognize", mock.Anything, audioData, stt.Options{
|
||||
Encoding: "pcm_s16le",
|
||||
SampleRate: 16000,
|
||||
Language: "zh-CN",
|
||||
}).Return("你好，世界", nil)
|
||||
|
||||
mockSender.On("SendSTTResult", mock.Anything).Return(nil)
|
||||
|
||||
llmChunks := []llm.Chunk{
|
||||
{Delta: "你好"},
|
||||
{Delta: "，世界！"},
|
||||
{Done: true, TokensUsed: &llm.TokenUsage{Prompt: 10, Completion: 5, Total: 15}},
|
||||
}
|
||||
mockLLM.On("ChatStream", mock.Anything, mock.Anything).Return(createLLMStream(llmChunks), nil)
|
||||
|
||||
mockSender.On("SendLLMChunk", mock.Anything).Return(nil)
|
||||
mockSender.On("SendLLMDone", mock.Anything).Return(nil)
|
||||
|
||||
ttsChunks := []tts.Chunk{
|
||||
{Audio: []byte("audio1"), IsLast: false},
|
||||
{Audio: []byte("audio2"), IsLast: true},
|
||||
}
|
||||
mockTTS.On("SynthesizeStream", mock.Anything, mock.Anything, mock.Anything).Return(createTTSStream(ttsChunks), nil)
|
||||
|
||||
mockSender.On("SendTTSAudio", mock.Anything).Return(nil)
|
||||
|
||||
// 创建 Pipeline
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
// 执行
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
// 验证
|
||||
assert.NoError(t, err)
|
||||
assert.Len(t, mockSender.STTResults, 1)
|
||||
assert.Equal(t, "你好，世界", mockSender.STTResults[0].Text)
|
||||
assert.Len(t, mockSender.LLMChunks, 2)
|
||||
assert.Len(t, mockSender.LLMDones, 1)
|
||||
assert.Len(t, mockSender.TTSAudios, 2)
|
||||
|
||||
mockSTT.AssertExpectations(t)
|
||||
mockLLM.AssertExpectations(t)
|
||||
mockTTS.AssertExpectations(t)
|
||||
mockSession.AssertExpectations(t)
|
||||
}
|
||||
|
||||
// TestProcessQuery_STTError 测试 STT 失败降级
|
||||
func TestProcessQuery_STTError(t *testing.T) {
|
||||
audioData := []byte("test audio")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(&models.Session{
|
||||
ID: "session-123",
|
||||
Config: models.SessionConfig{
|
||||
Language: "zh-CN",
|
||||
},
|
||||
}, nil)
|
||||
|
||||
mockSTT.On("Recognize", mock.Anything, audioData, mock.Anything).
|
||||
Return("", errors.New("STT service unavailable"))
|
||||
|
||||
mockSender.On("SendError", mock.Anything).Return(nil)
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
assert.Error(t, err)
|
||||
assert.Len(t, mockSender.Errors, 1)
|
||||
assert.Equal(t, "STT_ERROR", mockSender.Errors[0].Code)
|
||||
|
||||
mockSTT.AssertExpectations(t)
|
||||
mockLLM.AssertNotCalled(t, "ChatStream")
|
||||
mockTTS.AssertNotCalled(t, "SynthesizeStream")
|
||||
}
|
||||
|
||||
// TestProcessQuery_LLMError 测试 LLM 失败降级
|
||||
func TestProcessQuery_LLMError(t *testing.T) {
|
||||
audioData := []byte("test audio")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
session := &models.Session{
|
||||
ID: "session-123",
|
||||
Config: models.SessionConfig{
|
||||
TTSEnabled: true,
|
||||
Language: "zh-CN",
|
||||
},
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(session, nil)
|
||||
mockSession.On("AppendMessage", mock.Anything, "session-123", mock.Anything).Return(nil)
|
||||
|
||||
mockSTT.On("Recognize", mock.Anything, audioData, mock.Anything).Return("你好", nil)
|
||||
mockSender.On("SendSTTResult", mock.Anything).Return(nil)
|
||||
|
||||
mockLLM.On("ChatStream", mock.Anything, mock.Anything).
|
||||
Return(nil, errors.New("LLM service unavailable"))
|
||||
|
||||
mockSender.On("SendError", mock.Anything).Return(nil)
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
assert.Error(t, err)
|
||||
assert.Len(t, mockSender.Errors, 1)
|
||||
assert.Equal(t, "LLM_ERROR", mockSender.Errors[0].Code)
|
||||
|
||||
mockSTT.AssertExpectations(t)
|
||||
mockLLM.AssertExpectations(t)
|
||||
mockTTS.AssertNotCalled(t, "SynthesizeStream")
|
||||
}
|
||||
|
||||
// TestProcessQuery_TTSError 测试 TTS 失败静默跳过
|
||||
func TestProcessQuery_TTSError(t *testing.T) {
|
||||
audioData := []byte("test audio")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
session := &models.Session{
|
||||
ID: "session-123",
|
||||
Config: models.SessionConfig{
|
||||
TTSEnabled: true,
|
||||
Language: "zh-CN",
|
||||
},
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(session, nil)
|
||||
mockSession.On("AppendMessage", mock.Anything, "session-123", mock.Anything).Return(nil)
|
||||
|
||||
mockSTT.On("Recognize", mock.Anything, audioData, mock.Anything).Return("你好", nil)
|
||||
mockSender.On("SendSTTResult", mock.Anything).Return(nil)
|
||||
|
||||
llmChunks := []llm.Chunk{
|
||||
{Delta: "你好"},
|
||||
{Done: true},
|
||||
}
|
||||
mockLLM.On("ChatStream", mock.Anything, mock.Anything).Return(createLLMStream(llmChunks), nil)
|
||||
mockSender.On("SendLLMChunk", mock.Anything).Return(nil)
|
||||
mockSender.On("SendLLMDone", mock.Anything).Return(nil)
|
||||
|
||||
mockTTS.On("SynthesizeStream", mock.Anything, mock.Anything, mock.Anything).
|
||||
Return(nil, errors.New("TTS service unavailable"))
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
// TTS 失败应该静默跳过，不返回错误
|
||||
assert.NoError(t, err)
|
||||
assert.Len(t, mockSender.LLMDones, 1)
|
||||
assert.Len(t, mockSender.TTSAudios, 0)
|
||||
|
||||
mockSTT.AssertExpectations(t)
|
||||
mockLLM.AssertExpectations(t)
|
||||
mockTTS.AssertExpectations(t)
|
||||
}
|
||||
|
||||
// TestProcessQuery_ContextCancelled 测试上下文取消（Interrupt）
|
||||
func TestProcessQuery_ContextCancelled(t *testing.T) {
|
||||
audioData := []byte("test audio")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
session := &models.Session{
|
||||
ID: "session-123",
|
||||
Config: models.SessionConfig{
|
||||
TTSEnabled: true,
|
||||
Language: "zh-CN",
|
||||
},
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(session, nil)
|
||||
mockSession.On("AppendMessage", mock.Anything, "session-123", mock.Anything).Return(nil)
|
||||
|
||||
mockSTT.On("Recognize", mock.Anything, audioData, mock.Anything).Return("你好", nil)
|
||||
mockSender.On("SendSTTResult", mock.Anything).Return(nil)
|
||||
|
||||
// 创建一个会延迟的 LLM 流，以便我们可以取消上下文
|
||||
llmCh := make(chan llm.Chunk)
|
||||
go func() {
|
||||
time.Sleep(100 * time.Millisecond)
|
||||
llmCh <- llm.Chunk{Delta: "你"}
|
||||
time.Sleep(100 * time.Millisecond)
|
||||
llmCh <- llm.Chunk{Delta: "好"}
|
||||
close(llmCh)
|
||||
}()
|
||||
|
||||
mockLLM.On("ChatStream", mock.Anything, mock.Anything).Return((<-chan llm.Chunk)(llmCh), nil)
|
||||
mockSender.On("SendLLMChunk", mock.Anything).Return(nil)
|
||||
mockSender.On("SendLLMDone", mock.Anything).Return(nil)
|
||||
|
||||
// 创建一个会延迟的 TTS 流
|
||||
ttsCh := make(chan tts.Chunk)
|
||||
go func() {
|
||||
time.Sleep(200 * time.Millisecond)
|
||||
close(ttsCh)
|
||||
}()
|
||||
mockTTS.On("SynthesizeStream", mock.Anything, mock.Anything, mock.Anything).Return((<-chan tts.Chunk)(ttsCh), nil)
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
// 创建可取消的上下文
|
||||
ctx, cancel := context.WithCancel(context.Background())
|
||||
|
||||
// 在 50ms 后取消
|
||||
go func() {
|
||||
time.Sleep(50 * time.Millisecond)
|
||||
cancel()
|
||||
}()
|
||||
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
// 上下文取消后，流程应该正常完成（中断流但不返回错误）
|
||||
assert.NoError(t, err)
|
||||
|
||||
mockSTT.AssertExpectations(t)
|
||||
}
|
||||
|
||||
// TestProcessQuery_DisabledTTS 测试 TTS 未启用的情况
|
||||
func TestProcessQuery_DisabledTTS(t *testing.T) {
|
||||
audioData := []byte("test audio")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
session := &models.Session{
|
||||
ID: "session-123",
|
||||
Config: models.SessionConfig{
|
||||
TTSEnabled: false, // TTS 未启用
|
||||
Language: "zh-CN",
|
||||
},
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(session, nil)
|
||||
mockSession.On("AppendMessage", mock.Anything, "session-123", mock.Anything).Return(nil)
|
||||
|
||||
mockSTT.On("Recognize", mock.Anything, audioData, mock.Anything).Return("你好", nil)
|
||||
mockSender.On("SendSTTResult", mock.Anything).Return(nil)
|
||||
|
||||
llmChunks := []llm.Chunk{
|
||||
{Delta: "你好"},
|
||||
{Done: true},
|
||||
}
|
||||
mockLLM.On("ChatStream", mock.Anything, mock.Anything).Return(createLLMStream(llmChunks), nil)
|
||||
mockSender.On("SendLLMChunk", mock.Anything).Return(nil)
|
||||
mockSender.On("SendLLMDone", mock.Anything).Return(nil)
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
assert.NoError(t, err)
|
||||
assert.Len(t, mockSender.LLMDones, 1)
|
||||
assert.Len(t, mockSender.TTSAudios, 0)
|
||||
|
||||
// TTS 不应该被调用
|
||||
mockTTS.AssertNotCalled(t, "SynthesizeStream")
|
||||
}
|
||||
|
||||
// TestSplitter 测试句子切分器
|
||||
func TestSplitter(t *testing.T) {
|
||||
ch := make(chan string, 10)
|
||||
splitter := NewSplitter(ch)
|
||||
|
||||
// 输入包含多个句子的文本
|
||||
splitter.Feed("你好。")
|
||||
splitter.Feed("世界！")
|
||||
splitter.Feed("这是")
|
||||
splitter.Feed("一个测试。")
|
||||
splitter.Flush()
|
||||
|
||||
// 应该有 3 个句子
|
||||
assert.Equal(t, 3, len(ch))
|
||||
assert.Equal(t, "你好。", <-ch)
|
||||
assert.Equal(t, "世界！", <-ch)
|
||||
assert.Equal(t, "这是一个测试。", <-ch)
|
||||
}
|
||||
|
||||
// TestSplitter_NoDelimiter 测试没有分隔符的情况
|
||||
func TestSplitter_NoDelimiter(t *testing.T) {
|
||||
ch := make(chan string, 10)
|
||||
splitter := NewSplitter(ch)
|
||||
|
||||
splitter.Feed("没有分隔符的文本")
|
||||
splitter.Flush()
|
||||
|
||||
// 应该有 1 个句子（Flush 会发送剩余内容）
|
||||
assert.Equal(t, 1, len(ch))
|
||||
assert.Equal(t, "没有分隔符的文本", <-ch)
|
||||
}
|
||||
|
||||
// TestSplitter_Empty 测试空输入
|
||||
func TestSplitter_Empty(t *testing.T) {
|
||||
ch := make(chan string, 10)
|
||||
splitter := NewSplitter(ch)
|
||||
|
||||
splitter.Flush()
|
||||
|
||||
// 应该没有句子
|
||||
assert.Equal(t, 0, len(ch))
|
||||
}
|
||||
|
||||
// TestProcessQuery_InvalidAudio 测试无效音频数据
|
||||
func TestProcessQuery_InvalidAudio(t *testing.T) {
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: "invalid-base64!!!",
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSender.On("SendError", mock.Anything).Return(nil)
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
assert.Error(t, err)
|
||||
assert.Len(t, mockSender.Errors, 1)
|
||||
assert.Equal(t, "INVALID_MESSAGE", mockSender.Errors[0].Code)
|
||||
}
|
||||
|
||||
// TestProcessQuery_SessionNotFound 测试会话不存在
|
||||
func TestProcessQuery_SessionNotFound(t *testing.T) {
|
||||
audioData := []byte("test audio")
|
||||
audioBase64 := base64.StdEncoding.EncodeToString(audioData)
|
||||
|
||||
req := models.WsQuery{
|
||||
Type: "query",
|
||||
RequestID: "req-123",
|
||||
Audio: audioBase64,
|
||||
}
|
||||
|
||||
mockSTT := new(MockSTTService)
|
||||
mockLLM := new(MockLLMService)
|
||||
mockTTS := new(MockTTSService)
|
||||
mockSession := new(MockSessionManager)
|
||||
mockSender := NewMockSender()
|
||||
|
||||
mockSession.On("SetActiveRequest", mock.Anything, "session-123", "req-123").Return(nil)
|
||||
mockSession.On("ClearActiveRequest", mock.Anything, "session-123").Return(nil)
|
||||
mockSession.On("Get", mock.Anything, "session-123").Return(nil, errors.New("session not found"))
|
||||
|
||||
mockSender.On("SendError", mock.Anything).Return(nil)
|
||||
|
||||
pipeline := New(mockSTT, mockLLM, mockTTS, mockSession, &config.Config{
|
||||
AI: config.AIConfig{
|
||||
LLM: config.LLMConfig{Model: "gpt-4o"},
|
||||
TTS: config.TTSConfig{Voice: "alloy", Speed: 1.0, OutputFormat: "mp3", SampleRate: 24000},
|
||||
},
|
||||
})
|
||||
|
||||
ctx := context.Background()
|
||||
err := pipeline.ProcessQuery(ctx, "session-123", req, nil, mockSender)
|
||||
|
||||
assert.Error(t, err)
|
||||
assert.Len(t, mockSender.Errors, 1)
|
||||
assert.Equal(t, "SESSION_NOT_FOUND", mockSender.Errors[0].Code)
|
||||
}
|
||||
@@ -1,55 +0,0 @@
|
||||
package orchestrator
|
||||
|
||||
import "strings"
|
||||
|
||||
// sentenceDelimiters 句子分隔符集合。
|
||||
var sentenceDelimiters = map[rune]bool{
|
||||
'。': true,
|
||||
'！': true,
|
||||
'？': true,
|
||||
'\n': true,
|
||||
'.': true,
|
||||
'!': true,
|
||||
'?': true,
|
||||
}
|
||||
|
||||
// Splitter 句子切分器。
|
||||
// 将流式文本按句子边界切分，发送到 channel 供 TTS 合成。
|
||||
type Splitter struct {
|
||||
ch chan<- string
|
||||
buffer strings.Builder
|
||||
}
|
||||
|
||||
// NewSplitter 创建句子切分器。
|
||||
// ch 用于接收切分后的句子文本。
|
||||
func NewSplitter(ch chan<- string) *Splitter {
|
||||
return &Splitter{
|
||||
ch: ch,
|
||||
}
|
||||
}
|
||||
|
||||
// Feed 输入增量文本，遇到句子分隔符时发送完整句子。
|
||||
func (s *Splitter) Feed(delta string) {
|
||||
for _, r := range delta {
|
||||
s.buffer.WriteRune(r)
|
||||
if sentenceDelimiters[r] {
|
||||
s.flushBuffer()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Flush 刷新缓冲区中的剩余文本（即使没有句子分隔符）。
|
||||
func (s *Splitter) Flush() {
|
||||
if s.buffer.Len() > 0 {
|
||||
s.flushBuffer()
|
||||
}
|
||||
}
|
||||
|
||||
// flushBuffer 将缓冲区内容发送到 channel 并清空。
|
||||
func (s *Splitter) flushBuffer() {
|
||||
text := strings.TrimSpace(s.buffer.String())
|
||||
if text != "" {
|
||||
s.ch <- text
|
||||
}
|
||||
s.buffer.Reset()
|
||||
}
|
||||
@@ -47,7 +47,12 @@ func userSessKey(id string) string { return fmt.Sprintf("user:%s:sessions", id)
|
||||
|
||||
// Create 创建新会话。userID 为空表示匿名会话。
|
||||
func (m *RedisManager) Create(ctx context.Context, userID string, config models.SessionConfig) (string, error) {
|
||||
id := uuidNew()
|
||||
return m.CreateWithID(ctx, uuidNew(), userID, config)
|
||||
}
|
||||
|
||||
// CreateWithID 使用指定 ID 创建新会话。
|
||||
// 供 TieredManager 调用，确保 L1/L2 使用相同的 session ID。
|
||||
func (m *RedisManager) CreateWithID(ctx context.Context, id string, userID string, config models.SessionConfig) (string, error) {
|
||||
now := time.Now().UTC()
|
||||
|
||||
pipe := m.rdb.Pipeline()
|
||||
|
||||
@@ -127,9 +127,9 @@ func (m *TieredManager) Create(ctx context.Context, userID string, config models
|
||||
return "", err
|
||||
}
|
||||
|
||||
// L2: Redis（同步）
|
||||
// L2: Redis（同步），使用 L1 生成的 ID 保证一致性
|
||||
if m.isRedisOK() {
|
||||
if _, err := m.l2.Create(ctx, userID, config); err != nil {
|
||||
if _, err := m.l2.CreateWithID(ctx, id, userID, config); err != nil {
|
||||
logger.Log.Warnw("Redis Create failed, continuing without L2",
|
||||
"session", id, "error", err)
|
||||
}
|
||||
|
||||
246
docs/11-Eino框架技术文档.md
Normal file
246
docs/11-Eino框架技术文档.md
Normal file
@@ -0,0 +1,246 @@
|
||||
# CamTalk Eino 框架技术文档
|
||||
|
||||
> 创建日期：2026-06-19
|
||||
> 状态：已实施
|
||||
|
||||
## 1. 框架简介
|
||||
|
||||
[CloudWeGo Eino](https://github.com/cloudwego/eino) 是字节跳动 CloudWeGo 团队开源的 AI 应用开发框架，提供基于图（Graph）的编排能力、组件抽象和流式处理支持。
|
||||
|
||||
CamTalk 使用 Eino 替代原有的手写 goroutine 管道，实现 STT → LLM → TTS 的声明式编排。
|
||||
|
||||
## 2. 技术选型
|
||||
|
||||
### 2.1 为什么选 Eino
|
||||
|
||||
| 维度 | 手写 goroutine（旧方案） | Eino Graph（新方案） |
|
||||
|------|------------------------|---------------------|
|
||||
| 编排方式 | 手动 `go func()` + `sync.WaitGroup` | 声明式 DAG，类型安全 |
|
||||
| 流式处理 | 自定义 `chan` 传递 | `StreamReader` + `Pipe`，自动转换 |
|
||||
| 错误处理 | 各节点独立处理，不一致 | Graph 级别统一错误传播 |
|
||||
| 回调/AOP | 日志散落各处 | `callbacks.Handler` 统一注入 |
|
||||
| 配置灵活性 | Pipeline 创建时固定 | 每请求 `Option` 动态注入 |
|
||||
| 可测试性 | 需启动 goroutine | `Graph.Invoke()` 直接测试 |
|
||||
| 扩展性 | 修改 Pipeline 代码 | 添加节点 + 边，无侵入 |
|
||||
| 并发安全 | 手动 `sync` | State 自动加锁 |
|
||||
|
||||
### 2.2 Eino vs 其他编排框架
|
||||
|
||||
| 框架 | 特点 | CamTalk 适用性 |
|
||||
|------|------|---------------|
|
||||
| **Eino** | Go 原生、类型安全、流式原生 | ✅ 完美匹配 |
|
||||
| LangChain Go | 生态丰富但较重 | ❌ 过度抽象 |
|
||||
| 自研编排 | 完全可控 | ❌ 维护成本高 |
|
||||
|
||||
**选择 Eino 的核心理由**：
|
||||
1. Go 原生，泛型支持，编译时类型检查
|
||||
2. 原生流式处理（`StreamReader`），适合 LLM token 级推送
|
||||
3. Graph 支持分支、并行、循环，满足当前和未来需求
|
||||
4. Callback 机制实现 AOP（日志、指标、消息推送）
|
||||
5. eino-ext 提供 OpenAI ChatModel 实现，直接对接 DashScope
|
||||
|
||||
### 2.3 核心依赖版本
|
||||
|
||||
```
|
||||
github.com/cloudwego/eino v0.9.9
|
||||
github.com/cloudwego/eino-ext/components/model/openai v0.1.13
|
||||
```
|
||||
|
||||
## 3. Eino 核心概念
|
||||
|
||||
### 3.1 Lambda
|
||||
|
||||
Lambda 是 Graph 中的可组合函数单元，支持四种模式：
|
||||
|
||||
| 模式 | 函数签名 | 构造方法 | 说明 |
|
||||
|------|---------|---------|------|
|
||||
| Invoke | `I → O` | `compose.InvokableLambda()` | 同步调用 |
|
||||
| Stream | `I → StreamReader[O]` | `compose.StreamableLambda()` | 流式输出 |
|
||||
| Collect | `StreamReader[I] → O` | `compose.CollectableLambda()` | 流式输入 |
|
||||
| Transform | `StreamReader[I] → StreamReader[O]` | `compose.TransformableLambda()` | 双向流式 |
|
||||
|
||||
**返回类型**：所有 Lambda 构造函数返回 `*compose.Lambda`。
|
||||
|
||||
### 3.2 Graph
|
||||
|
||||
Graph 是有向无环图（DAG）编排器，支持：
|
||||
- **节点**：Lambda、ChatModel、ToolsNode 等
|
||||
- **边**：`g.AddEdge(from, to)` 定义数据流向
|
||||
- **分支**：`g.AddBranch()` 条件路由
|
||||
- **State**：`compose.WithGenLocalState()` 跨节点共享状态
|
||||
|
||||
```go
|
||||
g := compose.NewGraph[PipelineInput, PipelineOutput]()
|
||||
g.AddLambdaNode("stt", sttLambda)
|
||||
g.AddChatModelNode("llm", chatModel)
|
||||
g.AddEdge(compose.START, "stt")
|
||||
g.AddEdge("stt", "llm")
|
||||
g.AddEdge("llm", compose.END)
|
||||
|
||||
runnable, err := g.Compile(ctx)
|
||||
output, err := runnable.Invoke(ctx, input) // 同步调用
|
||||
stream, err := runnable.Stream(ctx, input) // 流式调用
|
||||
```
|
||||
|
||||
### 3.3 ChatModel
|
||||
|
||||
ChatModel 是 LLM 组件抽象，接口定义：
|
||||
|
||||
```go
|
||||
type BaseChatModel interface {
|
||||
Generate(ctx, []*schema.Message, ...Option) (*schema.Message, error)
|
||||
Stream(ctx, []*schema.Message, ...Option) (*schema.StreamReader[*schema.Message], error)
|
||||
}
|
||||
```
|
||||
|
||||
CamTalk 使用 `eino-ext/components/model/openai` 实现，通过 `BaseURL` 对接 DashScope：
|
||||
|
||||
```go
|
||||
chatModel, _ := openai.NewChatModel(ctx, &openai.ChatModelConfig{
|
||||
APIKey: cfg.AI.LLM.APIKey,
|
||||
Model: cfg.AI.LLM.Model,
|
||||
BaseURL: cfg.AI.LLM.Endpoint, // "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
})
|
||||
```
|
||||
|
||||
### 3.4 StreamReader
|
||||
|
||||
`schema.StreamReader[T]` 是 Eino 的流式数据抽象：
|
||||
- `sr.Recv()` 读取一帧，`io.EOF` 表示流结束
|
||||
- `schema.Pipe[T](bufSize)` 创建 `StreamReader` + `StreamWriter` 对
|
||||
- 框架自动处理 `T ↔ StreamReader[T]` 的转换（装箱/concat）
|
||||
|
||||
### 3.5 Callback
|
||||
|
||||
Callback 是 Eino 的 AOP 机制，支持节点生命周期钩子：
|
||||
|
||||
```go
|
||||
type Handler interface {
|
||||
OnStart(ctx, *RunInfo, CallbackInput) context.Context
|
||||
OnEnd(ctx, *RunInfo, CallbackOutput) context.Context
|
||||
OnError(ctx, *RunInfo, error) context.Context
|
||||
OnStartWithStreamInput(ctx, *RunInfo, *StreamReader[CallbackInput]) context.Context
|
||||
OnEndWithStreamOutput(ctx, *RunInfo, *StreamReader[CallbackOutput]) context.Context
|
||||
}
|
||||
```
|
||||
|
||||
CamTalk 使用 `utils/callbacks.NewHandlerHelper()` 构建 typed handler：
|
||||
- `ModelCallbackHandler.OnEndWithStreamOutput`：逐 token 推送 `llm_chunk`
|
||||
|
||||
### 3.6 State
|
||||
|
||||
Graph 全局状态，通过 `WithGenLocalState` 注册：
|
||||
|
||||
```go
|
||||
type PipelineState struct {
|
||||
FullResponse strings.Builder
|
||||
TranscribedText string
|
||||
TokenUsage *TokenUsage
|
||||
}
|
||||
|
||||
g := compose.NewGraph[I, O](compose.WithGenLocalState(func(ctx context.Context) *PipelineState {
|
||||
return &PipelineState{}
|
||||
}))
|
||||
```
|
||||
|
||||
节点通过 `compose.ProcessState` 读写 State。
|
||||
|
||||
## 4. CamTalk Graph 设计
|
||||
|
||||
### 4.1 拓扑
|
||||
|
||||
```
|
||||
START → STT → History → ChatModel → Splitter → TTS → Done → END
|
||||
```
|
||||
|
||||
| 节点 | 类型 | 输入 → 输出 | 职责 |
|
||||
|------|------|------------|------|
|
||||
| STT | InvokableLambda | `PipelineInput → STTOutput` | 语音识别，写入 State |
|
||||
| History | InvokableLambda | `STTOutput → []*schema.Message` | 组装提示词和历史 |
|
||||
| ChatModel | ChatModel（原生） | `[]*schema.Message → StreamReader[*Message]` | LLM 流式推理 |
|
||||
| Splitter | TransformableLambda | `StreamReader[string] → StreamReader[[]string]` | 句子切分 |
|
||||
| TTS | InvokableLambda | `[]string → struct{}` | 语音合成，推送音频 |
|
||||
| Done | InvokableLambda | `struct{} → PipelineOutput` | 发送 llm_done |
|
||||
|
||||
### 4.2 流式模式
|
||||
|
||||
Graph 使用 **Stream 模式**调用：
|
||||
- 内部所有节点以 Transform 模式运行
|
||||
- ChatModel 的 `Stream()` 方法实现真正的 token 级流式
|
||||
- 适配器消费 `StreamReader[PipelineOutput]` 触发整条链路
|
||||
|
||||
### 4.3 消息推送机制
|
||||
|
||||
| 消息 | 推送方式 | 时机 |
|
||||
|------|---------|------|
|
||||
| `stt_result` | Lambda 内部直接调用 Sender | STT 完成后 |
|
||||
| `llm_chunk` | Callback `OnEndWithStreamOutput` | ChatModel 逐 token |
|
||||
| `tts_audio` | Lambda 内部直接调用 Sender | TTS 逐句合成 |
|
||||
| `llm_done` | Lambda 内部直接调用 Sender | Done 节点执行时 |
|
||||
|
||||
**Context 注入**：Sender、RequestID、SessionID、PipelineState 通过 `context.WithValue` 传递。
|
||||
|
||||
### 4.4 多模态支持
|
||||
|
||||
History 节点将图片构建为 `schema.Message.UserInputMultiContent`：
|
||||
|
||||
```go
|
||||
systemMsg.UserInputMultiContent = []schema.MessageInputPart{
|
||||
{
|
||||
Type: schema.ChatMessagePartTypeImageURL,
|
||||
Image: &schema.MessageInputImage{
|
||||
MessagePartCommon: schema.MessagePartCommon{
|
||||
Base64Data: &base64Str,
|
||||
MIMEType: "image/jpeg",
|
||||
},
|
||||
Detail: schema.ImageURLDetailAuto,
|
||||
},
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
## 5. 目录结构
|
||||
|
||||
```
|
||||
backend/internal/eino/
|
||||
├── types.go # PipelineInput/Output、STTOutput、TokenUsage
|
||||
├── state.go # PipelineState（跨节点状态）
|
||||
├── callback.go # Callback handler（LLM token 推送）
|
||||
├── graph.go # Graph 构建与编译
|
||||
├── adapter.go # EinoOrchestrator（Orchestrator 接口适配器）
|
||||
├── nodes_stt.go # STT Lambda
|
||||
├── nodes_history.go # 历史组装 Lambda
|
||||
├── nodes_splitter.go # 句子分割 Transform Lambda
|
||||
├── nodes_tts.go # TTS Lambda
|
||||
├── nodes_done.go # Done Lambda
|
||||
└── graph_test.go # 单元测试
|
||||
```
|
||||
|
||||
## 6. 注意事项
|
||||
|
||||
### 6.1 值类型 vs 指针类型
|
||||
|
||||
Graph 泛型参数必须使用值类型（`PipelineInput`/`PipelineOutput`），所有 Lambda 的输入输出也使用值类型。框架在 Transform 模式下会自动处理 `T` 和 `StreamReader[T]` 的转换。
|
||||
|
||||
### 6.2 Callback 运行时传入
|
||||
|
||||
Callback 通过 `Stream()` 的 option 传入，不在 `Compile()` 时注册：
|
||||
|
||||
```go
|
||||
streamReader, err := runnable.Stream(ctx, input, compose.WithCallbacks(handler))
|
||||
```
|
||||
|
||||
### 6.3 eino-ext 与 DashScope 兼容性
|
||||
|
||||
eino-ext OpenAI ChatModel 通过 `BaseURL` 对接 DashScope 兼容接口。需注意：
|
||||
- 多模态图片使用 `Base64Data` + `MIMEType` 格式
|
||||
- `Timeout` 控制单次请求超时
|
||||
- 流式输出通过 `Stream()` 方法获取 `StreamReader[*schema.Message]`
|
||||
|
||||
### 6.4 框架自动类型转换
|
||||
|
||||
Eino 框架在编排场景中自动处理以下转换：
|
||||
- **T → StreamReader[T]**：将完整值装箱为单帧流（非流式 → 假流式）
|
||||
- **StreamReader[T] → T**：将流 concat 为完整值（流式 → 非流式）
|
||||
|
||||
这使得不同流式模式的节点可以无缝连接。
|
||||
204
docs/12-Eino重构实施记录.md
Normal file
204
docs/12-Eino重构实施记录.md
Normal file
@@ -0,0 +1,204 @@
|
||||
# CamTalk Eino 重构实施记录
|
||||
|
||||
> 创建日期：2026-06-19
|
||||
> 状态：已完成
|
||||
|
||||
## 1. 重构背景
|
||||
|
||||
CamTalk 原 AI 编排层（`internal/orchestrator/pipeline.go`）使用手写 goroutine + WaitGroup + channel 实现 STT → LLM → TTS 流式管道，存在以下问题：
|
||||
|
||||
1. **编排逻辑硬编码**：流程写死在 `ProcessQuery()` 中，扩展需重写 goroutine 调度
|
||||
2. **并发控制粗糙**：手动 `go func()` + `sync.WaitGroup`，缺乏结构化流式传递
|
||||
3. **无回调/AOP 机制**：日志、指标、追踪散落各处
|
||||
4. **配置耦合**：模型名、TTS 参数硬编码在 Pipeline 结构体
|
||||
5. **错误处理不一致**：TTS 错误被静默吞掉，缺乏统一模式
|
||||
|
||||
**重构目标**：使用 Eino Graph 替换手写 Pipeline，实现声明式编排、统一回调、按请求动态配置，保持 WebSocket 协议和 REST API 不变。
|
||||
|
||||
## 2. 整体架构变更
|
||||
|
||||
### 2.1 重构前
|
||||
|
||||
```
|
||||
WS Handler → Orchestrator.Pipeline.ProcessQuery()
|
||||
├→ goroutine: STT.Recognize()
|
||||
├→ goroutine: LLM.ChatStream() ──→ chan chunk ──→ Sender
|
||||
└→ goroutine: Splitter → TTS.SynthesizeStream() ──→ chan audio ──→ Sender
|
||||
WaitGroup.Wait()
|
||||
Sender.SendLLMDone()
|
||||
```
|
||||
|
||||
### 2.2 重构后
|
||||
|
||||
```
|
||||
WS Handler → EinoOrchestrator.ProcessQuery()
|
||||
├→ Graph.Stream(ctx, input)
|
||||
│ ├→ STT Lambda ─→ History Lambda ─→ ChatModel ─→ Splitter ─→ TTS ─→ Done
|
||||
│ │ (State 写入) (Callback (Transform) (Invoke) (Invoke)
|
||||
│ │ 流式推送)
|
||||
│ └→ 消费 StreamReader（触发整条链路惰性执行）
|
||||
└→ 追加助手消息到历史
|
||||
```
|
||||
|
||||
### 2.3 关键设计决策
|
||||
|
||||
| 决策 | 选择 | 理由 |
|
||||
|------|------|------|
|
||||
| Graph 调用模式 | Stream | ChatModel 需要真正的 token 级流式输出 |
|
||||
| LLM 组件 | eino-ext ChatModel | 原生 Eino 组件，直接对接 DashScope |
|
||||
| 消息推送 | Callback（LLM）+ Sender（其他） | LLM token 流式推送需要 Callback |
|
||||
| 值类型 vs 指针 | 值类型统一 | 避免框架类型转换不匹配 |
|
||||
| 历史追加 | 适配器负责 | Done 节点只负责发送 llm_done |
|
||||
|
||||
## 3. 分阶段实施
|
||||
|
||||
### Phase 1：基础设施（提交 `fd5c771`）
|
||||
|
||||
**目标**：引入 Eino 依赖，创建基础类型和 Callback。
|
||||
|
||||
**任务清单**：
|
||||
|
||||
| 任务 | 文件 | 说明 |
|
||||
|------|------|------|
|
||||
| 引入 Eino 依赖 | `go.mod` | `eino v0.9.9` + `eino-ext/components/model/openai v0.1.13` |
|
||||
| 数据类型定义 | `eino/types.go` | `PipelineInput`、`PipelineOutput`、`STTOutput`、`TokenUsage` |
|
||||
| State 定义 | `eino/state.go` | `PipelineState` 含 `sync.Mutex` 并发保护 |
|
||||
| 消息推送 Callback | `eino/callback.go` | `BuildCallbackHandler()` 使用 `callbacks.NewHandlerHelper()` |
|
||||
|
||||
**关键实现**：
|
||||
- `PipelineState` 使用 `strings.Builder` + `sync.Mutex` 累积 LLM 完整回复
|
||||
- Callback 通过 `ModelCallbackHandler.OnEndWithStreamOutput` 逐 token 推送 `llm_chunk`
|
||||
- Sender/RequestID/PipelineState 通过 `context.WithValue` 注入
|
||||
|
||||
**验证**：`go build ./cmd/server` ✓
|
||||
|
||||
---
|
||||
|
||||
### Phase 2：节点实现（提交 `fd5c771`）
|
||||
|
||||
**目标**：实现 Graph 中的 5 个 Lambda 节点。
|
||||
|
||||
**任务清单**：
|
||||
|
||||
| 任务 | 文件 | Lambda 类型 | 输入 → 输出 |
|
||||
|------|------|------------|------------|
|
||||
| STT Lambda | `eino/nodes_stt.go` | InvokableLambda | `PipelineInput → STTOutput` |
|
||||
| 历史组装 Lambda | `eino/nodes_history.go` | InvokableLambda | `STTOutput → []*schema.Message` |
|
||||
| 句子分割 Lambda | `eino/nodes_splitter.go` | TransformableLambda | `StreamReader[string] → StreamReader[[]string]` |
|
||||
| TTS Lambda | `eino/nodes_tts.go` | InvokableLambda | `[]string → struct{}` |
|
||||
| Done Lambda | `eino/nodes_done.go` | InvokableLambda | `struct{} → PipelineOutput` |
|
||||
|
||||
**关键实现**：
|
||||
- STT 节点将输入元数据写入 State，供下游节点读取
|
||||
- History 节点从 State 读取 SessionID/Scenario/ImageData，构建系统提示词 + 多模态消息
|
||||
- Splitter 使用 `TransformableLambda` 按句子分隔符切分，逐句输出给 TTS
|
||||
- TTS 节点调用 `ttsService.SynthesizeStream()`，逐 chunk 推送 `tts_audio`
|
||||
- Done 节点从 State 读取完整回复，发送 `llm_done`
|
||||
- 所有 Lambda 使用值类型（非指针），返回 `*compose.Lambda`
|
||||
|
||||
**验证**：`go build ./internal/eino/...` ✓
|
||||
|
||||
---
|
||||
|
||||
### Phase 3：Graph 构建与适配器（提交 `4b731b5`）
|
||||
|
||||
**目标**：构建 Graph、实现适配器、切换 main.go。
|
||||
|
||||
**任务清单**：
|
||||
|
||||
| 任务 | 文件 | 说明 |
|
||||
|------|------|------|
|
||||
| Graph 构建 | `eino/graph.go` | `NewPipelineGraph()` 组装 6 个节点 + 边 + 编译 |
|
||||
| 适配器 | `eino/adapter.go` | `EinoOrchestrator` 实现 `orchestrator.Orchestrator` 接口 |
|
||||
| main.go 切换 | `cmd/server/main.go` | 移除旧 LLM + orchestrator，替换为 Eino |
|
||||
|
||||
**Graph 拓扑**：
|
||||
```
|
||||
START → STT → History → ChatModel → Splitter → TTS → Done → END
|
||||
```
|
||||
|
||||
**适配器职责**：
|
||||
1. 解码 base64 音频/图片
|
||||
2. 获取会话配置
|
||||
3. 注入 Sender/RequestID/SessionID/StartTime/State 到 context
|
||||
4. 追加用户消息到历史
|
||||
5. 调用 `graph.Stream(ctx, input, callbacks)` 触发惰性执行
|
||||
6. 消费 `StreamReader[PipelineOutput]`
|
||||
7. 追加助手消息到历史
|
||||
|
||||
**关键实现**：
|
||||
- eino-ext ChatModel 配置：`BaseURL` 对接 DashScope，`Timeout` 控制请求超时
|
||||
- Callback 在运行时通过 `compose.WithCallbacks()` 传入，不在编译时注册
|
||||
- 元数据（SessionID/Scenario 等）通过 State 跨节点传递，不通过 Graph 边传递
|
||||
|
||||
**变更文件**：
|
||||
- 修改 `state.go`：新增 SessionID/RequestID/ImageData 等字段
|
||||
- 修改 `nodes_stt.go`：写入元数据到 State
|
||||
- 修改 `nodes_history.go`：从 State 读取元数据（移除 HistoryInput 依赖）
|
||||
- 修改 `nodes_done.go`：移除历史追加（由适配器负责）
|
||||
|
||||
**验证**：`go build ./cmd/server` ✓，`go vet ./...` ✓
|
||||
|
||||
---
|
||||
|
||||
### Phase 4：清理与测试（提交 `4ffd845`）
|
||||
|
||||
**目标**：删除旧代码，编写单元测试。
|
||||
|
||||
**删除的文件**：
|
||||
|
||||
| 文件 | 说明 |
|
||||
|------|------|
|
||||
| `orchestrator/pipeline.go` | 旧 STT→LLM→TTS 手写 goroutine 管道（-547 行） |
|
||||
| `orchestrator/splitter.go` | 旧句子切分器（-114 行） |
|
||||
| `orchestrator/pipeline_test.go` | 旧 Pipeline 测试（-309 行） |
|
||||
| `ai/llm/openai.go` | 旧 LLM OpenAI 实现（-548 行） |
|
||||
| `ai/llm/openai_test.go` | 旧 LLM 测试（-143 行） |
|
||||
|
||||
**保留的文件**：
|
||||
|
||||
| 文件 | 保留原因 |
|
||||
|------|---------|
|
||||
| `orchestrator/orchestrator.go` | Orchestrator 接口（ws/handler 依赖） |
|
||||
| `orchestrator/sender.go` | Sender 接口（eino/callback 依赖） |
|
||||
| `ai/llm/llm.go` | Request/Chunk/TokenUsage 类型定义 |
|
||||
| `ai/llm/prompt.go` | BuildSystemPrompt（eino/nodes_history 依赖） |
|
||||
| `ai/llm/scenarios.go` | GetScenarioPrompt（eino/nodes_history 依赖） |
|
||||
|
||||
**新增测试**：`eino/graph_test.go`（13 个测试）
|
||||
|
||||
| 测试 | 覆盖内容 |
|
||||
|------|---------|
|
||||
| `TestDetectImageMimeType` | JPEG/PNG/GIF/WebP/未知格式检测 |
|
||||
| `TestBuildPipelineInput` | 文本输入构建 |
|
||||
| `TestBuildPipelineInput_WithAudioData` | 音频+图片输入构建 |
|
||||
| `TestPipelineState_AppendAndGet` | State 文本追加和读取 |
|
||||
| `TestPipelineState_ConcurrentAccess` | State 并发安全（100 goroutine） |
|
||||
| `TestContextInjection` | Sender/RequestID/State 注入和提取 |
|
||||
| `TestLatencyFromCtx` | 延迟计算 |
|
||||
| `TestEinoOrchestrator_ImplementsInterface` | 接口实现检查 |
|
||||
| `TestNew*Lambda_ReturnsNonNil` | 5 个 Lambda 构造函数非空检查 |
|
||||
|
||||
**验证**：`go build ./...` ✓，`go vet ./...` ✓，`go test ./...` ✓
|
||||
|
||||
## 4. 代码变更统计
|
||||
|
||||
| 阶段 | 提交 | 新增 | 删除 | 净变化 |
|
||||
|------|------|------|------|--------|
|
||||
| Phase 1 + 2 | `fd5c771` | +946 | -24 | +922 |
|
||||
| Phase 3 | `4b731b5` | +395 | -98 | +297 |
|
||||
| Phase 4 | `4ffd845` | +235 | -1661 | -1426 |
|
||||
| **合计** | | **+1576** | **-1783** | **-207** |
|
||||
|
||||
重构后代码量净减少 207 行，同时获得了更好的可维护性、可测试性和可扩展性。
|
||||
|
||||
## 5. 遗留事项
|
||||
|
||||
| 事项 | 优先级 | 说明 |
|
||||
|------|--------|------|
|
||||
| eino-ext ChatModel DashScope 兼容性端到端验证 | 高 | 需要真实 API Key 验证流式输出和多模态 |
|
||||
| LLM 超时控制 | 中 | eino-ext ChatModel 的 `Timeout` 配置需验证 |
|
||||
| TTS 流式优化 | 中 | 当前 TTS 是 InvokableLambda，可改为 StreamableLambda |
|
||||
| ReAct Agent 扩展 | 低 | 基于 Graph Branch 实现工具调用循环 |
|
||||
| Model Router | 低 | 按场景/成本路由不同 LLM |
|
||||
| 指标监控 | 低 | 通过 Callback 接入 Prometheus |
|
||||
Reference in New Issue
Block a user