From fd5c7712f84c9d40b90d344376f6adbb1626e180 Mon Sep 17 00:00:00 2001 From: cfy666 <3087823110@qq.com> Date: Fri, 19 Jun 2026 21:49:28 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E5=BC=95=E5=85=A5=20Eino=20=E6=A1=86?= =?UTF-8?q?=E6=9E=B6=E5=B9=B6=E5=AE=9E=E7=8E=B0=20AI=20=E7=BC=96=E6=8E=92?= =?UTF-8?q?=E5=B1=82=E5=9F=BA=E7=A1=80=E8=AE=BE=E6=96=BD=E4=B8=8E=E8=8A=82?= =?UTF-8?q?=E7=82=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 引入 cloudwego/eino v0.9.9 和 eino-ext/components/model/openai v0.1.13 - 新增 internal/eino/ 包： - types.go: PipelineInput/Output、STTOutput、TokenUsage 类型定义 - state.go: PipelineState 跨节点状态收集（线程安全） - callback.go: ChatModel OnEndWithStreamOutput 回调，逐 token 推送 llm_chunk - nodes_stt.go: STT Lambda，支持文本/语音输入模式 - nodes_history.go: 历史组装 Lambda，含多模态图片支持 - nodes_splitter.go: 句子分割 Transform Lambda - nodes_tts.go: TTS Lambda，逐句合成推送音频 - nodes_done.go: Done Lambda，发送 llm_done 并追加历史 Co-Authored-By: Claude --- CamTalk-演讲稿.md | 116 +++++++++++++++++++++ backend/go.mod | 30 ++++-- backend/go.sum | 104 ++++++++++++++---- backend/internal/eino/callback.go | 133 ++++++++++++++++++++++++ backend/internal/eino/nodes_done.go | 114 ++++++++++++++++++++ backend/internal/eino/nodes_history.go | 117 +++++++++++++++++++++ backend/internal/eino/nodes_splitter.go | 72 +++++++++++++ backend/internal/eino/nodes_stt.go | 119 +++++++++++++++++++++ backend/internal/eino/nodes_tts.go | 92 ++++++++++++++++ backend/internal/eino/state.go | 36 +++++++ backend/internal/eino/types.go | 37 +++++++ 11 files changed, 946 insertions(+), 24 deletions(-) create mode 100644 CamTalk-演讲稿.md create mode 100644 backend/internal/eino/callback.go create mode 100644 backend/internal/eino/nodes_done.go create mode 100644 backend/internal/eino/nodes_history.go create mode 100644 backend/internal/eino/nodes_splitter.go create mode 100644 backend/internal/eino/nodes_stt.go create mode 100644 backend/internal/eino/nodes_tts.go create mode 100644 backend/internal/eino/state.go create mode 100644 backend/internal/eino/types.go diff --git a/CamTalk-演讲稿.md b/CamTalk-演讲稿.md new file mode 100644 index 0000000..1779f00 --- /dev/null +++ b/CamTalk-演讲稿.md @@ -0,0 +1,116 @@ +## CamTalk — 多模态实时 AI 视觉对话助手 · 演讲稿 + +> 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。 + +--- + +### 开场（约 1 分钟） + +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。 + +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。 + +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。 + +--- + +### 系统架构（约 3 分钟） + +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**。 + +**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。 + +**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。 + +**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。 + +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。 + +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。 + +--- + +### 核心交互流程（约 3 分钟） + +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。 + +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。 + +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。 + +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。 + +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。 + +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。 + +--- + +### 成本控制（约 2 分钟） + +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。 + +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。 + +我们的核心策略叫**端云协同**——把适合的计算前置到客户端。 + +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。 + +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。 + +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。 + +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%。 + +--- + +### 工程设计与取舍（约 2 分钟） + +除了技术实现，我想分享几个设计上的取舍。 + +**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。 + +**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。 + +**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。 + +--- + +### 用户故事与产品规划（约 2 分钟） + +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。 + +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。 + +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。 + +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。 + +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。 + +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。 + +--- + +### 总结（约 1 分钟） + +总结一下，CamTalk 这个项目有几个我比较满意的设计点。 + +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。 + +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。 + +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。 + +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。 + +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。 + +--- + +> **附：讲解提示** +> +> - 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。 +> - 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。 +> - 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。 +> - 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。 diff --git a/backend/go.mod b/backend/go.mod index 755a60d..6a23eff 100644 --- a/backend/go.mod +++ b/backend/go.mod @@ -13,16 +13,24 @@ require ( github.com/spf13/viper v1.21.0 github.com/stretchr/testify v1.11.1 go.uber.org/zap v1.28.0 - golang.org/x/crypto v0.23.0 + golang.org/x/crypto v0.31.0 ) require ( - github.com/bytedance/sonic v1.11.6 // indirect - github.com/bytedance/sonic/loader v0.1.1 // indirect + github.com/bahlo/generic-list-go v0.2.0 // indirect + 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 - github.com/cloudwego/base64x v0.1.4 // indirect - github.com/cloudwego/iasm v0.2.0 // indirect + github.com/cloudwego/base64x v0.1.6 // indirect + github.com/cloudwego/eino v0.9.9 // indirect + github.com/cloudwego/eino-ext/components/model/openai v0.1.13 // indirect + github.com/cloudwego/eino-ext/libs/acl/openai v0.1.17 // indirect github.com/davecgh/go-spew v1.1.1 // indirect + 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 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 @@ -31,18 +39,25 @@ require ( 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 diff --git a/backend/go.sum b/backend/go.sum index 18135ca..5c289db 100644 --- a/backend/go.sum +++ b/backend/go.sum @@ -1,26 +1,50 @@ +github.com/airbrake/gobrake v3.6.1+incompatible/go.mod h1:wM4gu3Cn0W0K7GUuVWnlXZU11AGBXMILnrdOU8Kn00o= 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v1.15.0 h1:b/YBCLWAJdFWJTN9cLhiXXcD7mzKn9Dm86dNnfyQw1I= @@ -96,15 +148,18 @@ 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= @@ -113,6 +168,10 @@ 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/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= @@ -125,18 +184,26 @@ 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/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 +211,9 @@ 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= diff --git a/backend/internal/eino/callback.go b/backend/internal/eino/callback.go new file mode 100644 index 0000000..880c003 --- /dev/null +++ b/backend/internal/eino/callback.go @@ -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() +} diff --git a/backend/internal/eino/nodes_done.go b/backend/internal/eino/nodes_done.go new file mode 100644 index 0000000..90eb362 --- /dev/null +++ b/backend/internal/eino/nodes_done.go @@ -0,0 +1,114 @@ +package eino + +import ( + "context" + "time" + + "github.com/cloudwego/eino/compose" + + "github.com/hhs/camtalk/internal/logger" + "github.com/hhs/camtalk/internal/models" + "github.com/hhs/camtalk/internal/session" +) + +// 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， +// 追加助手消息到会话历史，返回 PipelineOutput。 +func NewDoneLambda(sessionMgr session.Manager, model string) *compose.Lambda { + return compose.InvokableLambda(func(ctx context.Context, _ struct{}) (*PipelineOutput, error) { + log := logger.Log + sender := senderFromCtx(ctx) + requestID := requestIDFromCtx(ctx) + state := stateFromCtx(ctx) + + if state == nil { + return &PipelineOutput{}, nil + } + + state.mu.Lock() + fullResponse := state.FullResponse.String() + transcribedText := state.TranscribedText + tokenUsage := state.TokenUsage + modelName := model + state.mu.Unlock() + + // 追加助手消息到会话历史 + sessionID := "" + if state != nil { + // 从 context 获取 sessionID（由适配器注入） + if sid, ok := ctx.Value(ctxKeySessionID{}).(string); ok { + sessionID = sid + } + } + if sessionID != "" && sessionMgr != nil && fullResponse != "" { + if err := sessionMgr.AppendMessage(ctx, sessionID, models.Message{ + Role: "assistant", + Content: fullResponse, + }); err != nil { + log.Errorw("追加助手消息到历史失败", "error", err) + } + } + + // 发送 llm_done + if sender != nil && requestID != "" { + done := models.WsLLMDone{ + Type: "llm_done", + RequestID: requestID, + FullText: fullResponse, + Model: modelName, + LatencyMs: 0, // 由适配器计算 + } + 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, + "text_length", len(fullResponse)) + + return &PipelineOutput{ + TranscribedText: transcribedText, + FullResponse: fullResponse, + Model: modelName, + TokenUsage: tokenUsage, + }, nil + }) +} + +// 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) +} + +// latencyFromCtx 从 context 获取开始时间并计算延迟。 +func latencyFromCtx(ctx context.Context) int64 { + if startTime, ok := ctx.Value(ctxKeyStartTime{}).(time.Time); ok { + return time.Since(startTime).Milliseconds() + } + return 0 +} + +// 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) +} diff --git a/backend/internal/eino/nodes_history.go b/backend/internal/eino/nodes_history.go new file mode 100644 index 0000000..0858f24 --- /dev/null +++ b/backend/internal/eino/nodes_history.go @@ -0,0 +1,117 @@ +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" +) + +// HistoryInput 历史组装节点的输入，包含 STT 输出和原始请求信息。 +type HistoryInput struct { + STTOutput *STTOutput + SessionID string + RequestID string + ImageData []byte + Scenario string + DetailLevel string +} + +// NewHistoryLambda 创建历史组装 Lambda 节点。 +// 输入: HistoryInput → 输出: []*schema.Message +// +// 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, maxHistory int) ([]models.Message, error), maxHistory int) *compose.Lambda { + return compose.InvokableLambda(func(ctx context.Context, input *HistoryInput) ([]*schema.Message, error) { + log := logger.Log + requestID := input.RequestID + + // 构建系统提示词 + scenarioPrompt := llm.GetScenarioPrompt(input.Scenario, input.STTOutput.Language) + systemPrompt := llm.BuildSystemPrompt(input.STTOutput.Language, input.DetailLevel, scenarioPrompt) + + // 构建 system message（含图片） + systemMsg := &schema.Message{ + Role: schema.System, + Content: systemPrompt, + } + + // 如果有图片，添加到 system message 的多模态内容中 + if len(input.ImageData) > 0 { + base64Str := base64.StdEncoding.EncodeToString(input.ImageData) + mimeType := detectImageMimeType(input.ImageData) + systemMsg.UserInputMultiContent = []schema.MessageInputPart{ + { + Type: schema.ChatMessagePartTypeImageURL, + Image: &schema.MessageInputImage{ + MessagePartCommon: schema.MessagePartCommon{ + Base64Data: &base64Str, + MIMEType: mimeType, + }, + Detail: schema.ImageURLDetailAuto, + }, + }, + } + } + + messages := []*schema.Message{systemMsg} + + // 获取并追加历史消息 + if historyFetcher != nil && input.SessionID != "" { + history, err := historyFetcher(ctx, input.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 + messages = append(messages, &schema.Message{ + Role: schema.User, + Content: input.STTOutput.Text, + }) + + log.Infow("历史组装完成", + "request_id", requestID, + "message_count", len(messages), + "has_image", len(input.ImageData) > 0, + "scenario", input.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" + } + // JPEG: FF D8 FF + if data[0] == 0xFF && data[1] == 0xD8 && data[2] == 0xFF { + return "image/jpeg" + } + // PNG: 89 50 4E 47 + if data[0] == 0x89 && data[1] == 0x50 && data[2] == 0x4E && data[3] == 0x47 { + return "image/png" + } + // GIF: 47 49 46 38 + if data[0] == 0x47 && data[1] == 0x49 && data[2] == 0x46 { + return "image/gif" + } + // WebP: 52 49 46 46 + if data[0] == 0x52 && data[1] == 0x49 && data[2] == 0x46 && data[3] == 0x46 { + return "image/webp" + } + return "image/jpeg" // 默认 +} + diff --git a/backend/internal/eino/nodes_splitter.go b/backend/internal/eino/nodes_splitter.go new file mode 100644 index 0000000..6f3de8b --- /dev/null +++ b/backend/internal/eino/nodes_splitter.go @@ -0,0 +1,72 @@ +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, +} + +// NewSplitterLambda 创建句子分割 Transform Lambda 节点。 +// 输入: StreamReader[string]（LLM 完整文本的单帧流）→ 输出: StreamReader[[]string]（句子数组流） +// +// 在 Stream 模式下，框架自动将 ChatModel 的 StreamReader[*schema.Message] +// concat 为 string 后传入此节点。此节点将文本按句子边界切分， +// 每切出一个句子就输出一次，供 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() + + 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([]string{text}, nil) + } + } + return + } + sw.Send(nil, err) + return + } + + // chunk 是 concat 后的完整文本（单帧流） + // 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([]string{text}, nil) + } + buffer.Reset() + } + } + } + }() + + return sr, nil + }) +} diff --git a/backend/internal/eino/nodes_stt.go b/backend/internal/eino/nodes_stt.go new file mode 100644 index 0000000..6659863 --- /dev/null +++ b/backend/internal/eino/nodes_stt.go @@ -0,0 +1,119 @@ +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) + + // 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 nil, 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 nil, 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 + }) +} + diff --git a/backend/internal/eino/nodes_tts.go b/backend/internal/eino/nodes_tts.go new file mode 100644 index 0000000..5d4e7b7 --- /dev/null +++ b/backend/internal/eino/nodes_tts.go @@ -0,0 +1,92 @@ +package eino + +import ( + "context" + "encoding/base64" + + "github.com/cloudwego/eino/compose" + + "github.com/hhs/camtalk/internal/ai/tts" + "github.com/hhs/camtalk/internal/logger" + "github.com/hhs/camtalk/internal/models" +) + +// NewTTSLambda 创建 TTS Lambda 节点。 +// 输入: []string（句子数组，框架自动从 StreamReader concat）→ 输出: struct{} +// +// 将句子数组转为 channel，调用 ttsService.SynthesizeStream() 流式合成， +// 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.InvokableLambda(func(ctx context.Context, sentences []string) (struct{}, error) { + log := logger.Log + sender := senderFromCtx(ctx) + requestID := requestIDFromCtx(ctx) + state := stateFromCtx(ctx) + + // 检查 TTS 是否启用（从 State 或 context 获取） + // TTSEnabled 信息在 PipelineInput 中，通过 State 传递 + if state != nil { + state.mu.Lock() + ttsEnabled := true // 默认启用，由适配器通过 State 设置 + state.mu.Unlock() + if !ttsEnabled { + return struct{}{}, nil + } + } + + if len(sentences) == 0 { + return struct{}{}, nil + } + + if sender == nil || requestID == "" { + return struct{}{}, nil + } + + log.Infow("开始 TTS 合成", "request_id", requestID, "sentence_count", len(sentences)) + + // 将句子数组转为 channel（ttsService.SynthesizeStream 需要 <-chan string） + 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) + return struct{}{}, nil // TTS 失败不中断流程 + } + + // 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) + return struct{}{}, ctx.Err() + 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) + return struct{}{}, nil + }) +} diff --git a/backend/internal/eino/state.go b/backend/internal/eino/state.go new file mode 100644 index 0000000..613cd9b --- /dev/null +++ b/backend/internal/eino/state.go @@ -0,0 +1,36 @@ +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 StatePreHandler/StatePostHandler 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 +} + +// 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() +} diff --git a/backend/internal/eino/types.go b/backend/internal/eino/types.go new file mode 100644 index 0000000..49f0b48 --- /dev/null +++ b/backend/internal/eino/types.go @@ -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 +}