feat: add Chinese README for Beike AI Support with detailed features and setup instructions
This commit is contained in:
mlogclub
@@ -1,125 +1,127 @@
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# 贝壳 AI 客服
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# Beike AI Support
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开源的 AI Agent 客服系统，支持知识库问答、人工接管、工单闭环和私有化部署。
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English | [简体中文](README_ZH.md)
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> 面向需要同时处理在线咨询、知识库问答、人工协同和服务跟踪的团队。它不是把 LLM 接进聊天框，而是一套围绕客服场景设计的 AI Helpdesk 基础系统。
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An open-source AI Agent customer support system with knowledge-based answers, human handoff, ticket workflows, and self-hosted deployment.
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## 产品预览
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> Built for teams that need online support, knowledge-base Q&A, human collaboration, and service tracking in one system. It is not just an LLM inside a chat box; it is an AI Helpdesk foundation designed around real support operations.
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客户侧在线咨询、客服工作台、知识库、模型配置和 AI Agent 编排都在同一套系统中完成。
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## Product Preview
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### 客户侧在线咨询
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Customer chat, agent workspace, knowledge base, model configuration, and AI Agent orchestration are managed in one system.
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### Customer Chat
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客户可以在 Web 聊天页中直接发起咨询。AI Agent 会先接待，基于知识库回答问题；当用户明确要求人工介入时，会触发转人工确认流程。
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### 客服工作台
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Customers can start a conversation from the web chat page. The AI Agent responds first with knowledge-grounded answers. When the user explicitly asks for a human, the system can start a handoff confirmation flow.
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### Agent Workspace
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客服工作台支持会话列表、消息处理、AI 转人工、客服回复、会话标签、关联客户和工单信息查看，适合客服日常接待使用。
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### 知识库与 AI 配置
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The support workspace includes conversation lists, message handling, AI-to-human handoff, agent replies, conversation tags, linked customers, and ticket context for daily support work.
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| 知识库 FAQ | AI Agent 配置 |
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### Knowledge Base and AI Agent Configuration
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| Knowledge Base FAQ | AI Agent Configuration |
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| --- | --- |
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|  |  |
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知识库用于沉淀 FAQ、文档和可检索内容；AI Agent 可以绑定模型配置、知识库、Skills 和工具能力，形成面向具体客服场景的智能客服实例。
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The knowledge base stores FAQs, documents, and retrievable content. AI Agents can be bound to model configurations, knowledge bases, Skills, and tools to create support agents for specific scenarios.
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### 模型配置
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### Model Configuration
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模型配置支持 OpenAI-compatible 接入方式，可分别配置大语言模型、向量模型和重排模型，并管理上下文、输出、超时、重试和启用状态。
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Model configuration supports OpenAI-compatible providers. You can configure LLMs, embedding models, rerank models, context limits, output settings, timeout, retry behavior, and enablement state.
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## 为什么选择它
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## Why Use It
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- **AI 先接待**：让 AI Agent 优先处理常见问题、标准流程和知识库问答。
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- **知识约束回答**：通过 RAG 和 Answerability Gate 判断知识片段是否足以回答，减少超出知识库范围的乱答。
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- **自然转人工**：当知识库不足、用户明确要求或流程需要人工确认时，进入人工接管。
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- **会话到工单闭环**：在线会话、客服接待、工单创建、状态流转和处理记录在同一套系统里完成。
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- **适合二次开发**：后端使用 Go，前端使用 Next.js，支持 Skills、MCP 和 OpenAI-compatible 模型接入。
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- **可私有化部署**：支持 SQLite / MySQL 和 Qdrant，适合本地体验、内网部署和企业自托管。
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- **AI-first support**: Let AI Agents handle common questions, standard procedures, and knowledge-base answers first.
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- **Knowledge-constrained replies**: Use RAG and the Answerability Gate to decide whether retrieved knowledge is strong enough to answer, reducing unsupported responses.
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- **Natural human handoff**: Move to human agents when knowledge is insufficient, the user asks for help, or a workflow requires human confirmation.
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- **Conversation-to-ticket loop**: Online chat, support handling, ticket creation, status flow, and progress records stay in one system.
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- **Built for extension**: The backend uses Go, the frontend uses Next.js, and the runtime supports Skills, MCP, and OpenAI-compatible model access.
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- **Self-host friendly**: Supports SQLite / MySQL and Qdrant for local trials, intranet deployment, and enterprise self-hosting.
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## 核心能力
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## Core Capabilities
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- **AI Agent 客服**：AI 优先回复，支持兜底、确认、工具调用和人工协同。
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- **在线会话系统**：支持访客会话、消息收发、未读状态、会话分配、转接和关闭。
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- **客服工作台**：客服可接管会话、回复用户、转接同事、关联客户和创建工单。
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- **知识库 RAG**：支持知识库、文档、FAQ、切片、向量检索、检索日志和质量分析。
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- **Answerability Gate**：判断检索内容是否足以支撑回答，不足时返回兜底提示并建议联系人工。
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- **工单系统**：支持从会话创建工单、分类、指派、状态流转、进展记录和闭环处理。
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- **客服组织管理**：支持客服档案、客服组、排班和自动分配能力。
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- **AI 扩展能力**：支持 Skills、MCP 调试和外部工具接入。
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- **多入口接入**：提供管理后台、客服工作台、客户侧 Web 页面和嵌入式 SDK。
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- **AI Agent support**: AI replies first, with fallback, confirmation, tool calling, and human collaboration.
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- **Online conversation system**: Visitor sessions, message send/receive, unread status, assignment, transfer, and close flows.
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- **Agent workspace**: Agents can take over conversations, reply to users, transfer teammates, link customers, and create tickets.
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- **Knowledge-base RAG**: Knowledge bases, documents, FAQs, chunking, vector retrieval, retrieval logs, and quality analysis.
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- **Answerability Gate**: Checks whether retrieved content can support an answer; otherwise returns a fallback and recommends human support.
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- **Ticket system**: Create tickets from conversations, categorize, assign, move through status flows, record progress, and close the loop.
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- **Support organization management**: Agent profiles, teams, schedules, and automatic assignment.
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- **AI extensibility**: Skills, MCP debugging, and external tool integration.
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- **Multiple entry points**: Admin dashboard, agent workspace, customer-facing web pages, and embeddable SDK.
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## 适用场景
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## Use Cases
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- 官网在线客服
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- SaaS 产品支持
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- AI + 人工混合接待
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- 企业内部服务台
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- 售后、报障、投诉和运营支持
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- 需要知识库问答与人工协同的客服团队
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- Website live support
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- SaaS product support
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- AI + human hybrid support
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- Internal enterprise service desk
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- After-sales service, incident reporting, complaints, and operations support
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- Support teams that need knowledge-base Q&A with human collaboration
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## 快速开始
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## Quick Start
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推荐先用 Docker Compose 体验完整服务：
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The fastest way to try the full stack is Docker Compose:
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```bash
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docker compose up -d --build
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```
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Compose 默认会启动：
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Compose starts:
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- `cs-ai-agent`：应用服务，端口 `8083`
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- `mysql`：MySQL 8.4，数据卷 `mysql-data`
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- `qdrant`：向量数据库，数据卷 `qdrant-data`，端口 `6333` / `6334`
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- `cs-ai-agent`: application service on port `8083`
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- `mysql`: MySQL 8.4 with the `mysql-data` volume
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- `qdrant`: vector database with the `qdrant-data` volume, ports `6333` / `6334`
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启动后访问：
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After startup, open:
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- 管理后台：`http://localhost:8083/dashboard`
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- 客服工作台：`http://localhost:8083/dashboard/conversations`
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- 客户侧 Web 接入示例：`http://localhost:8083/support/demo`
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- 客户侧聊天页：`http://localhost:8083/support/chat`
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- Admin dashboard: `http://localhost:8083/dashboard`
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- Agent workspace: `http://localhost:8083/dashboard/conversations`
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- Customer web integration demo: `http://localhost:8083/support/demo`
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- Customer chat page: `http://localhost:8083/support/chat`
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默认管理员账号：
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Default administrator account:
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- 用户名：`admin`
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- 密码：`ChangeMe123!`
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- Username: `admin`
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- Password: `ChangeMe123!`
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> 首次用于公网或团队环境前，请务必修改默认管理员密码，并配置独立的鉴权、会话和模型密钥。
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> Before exposing the system to the public internet or a team environment, change the default administrator password and configure independent authentication, session, and model secrets.
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## 本地开发
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## Local Development
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### 环境要求
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### Requirements
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- Go `1.26+`
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- Node.js `20+`
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- `pnpm`
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- Qdrant
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### 准备配置
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### Prepare Configuration
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```bash
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cp config/config.example.yaml config/config.yaml
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```
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默认配置使用：
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The default configuration uses:
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- SQLite：`data/app.db`
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- Backend：`http://127.0.0.1:8083`
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- Qdrant gRPC：`127.0.0.1:6334`
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- SQLite: `data/app.db`
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- Backend: `http://127.0.0.1:8083`
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- Qdrant gRPC: `127.0.0.1:6334`
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如果本地还没有 Qdrant，可以用 Docker 启动：
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If Qdrant is not running locally, start it with Docker:
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```bash
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docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
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```
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安装前端依赖：
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Install frontend dependencies:
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```bash
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cd web
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@@ -127,139 +129,139 @@ pnpm install
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cd ..
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```
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同时启动后端和前端开发服务：
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Start backend and frontend development servers together:
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```bash
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make dev
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```
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或分别启动：
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Or start them separately:
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```bash
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make run-go
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make web-dev
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```
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开发环境默认入口：
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Default development URLs:
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- 管理后台：`http://localhost:3000/dashboard`
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- 客服工作台：`http://localhost:3000/dashboard/conversations`
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- 客户侧 Web 接入示例：`http://localhost:3000/support/demo`
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- 客户侧聊天页：`http://localhost:3000/support/chat`
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- Admin dashboard: `http://localhost:3000/dashboard`
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- Agent workspace: `http://localhost:3000/dashboard/conversations`
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- Customer web integration demo: `http://localhost:3000/support/demo`
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- Customer chat page: `http://localhost:3000/support/chat`
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## 技术栈
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## Tech Stack
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- Backend：Golang + Gin + GORM + `github.com/mlogclub/simple`
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- Frontend：Next.js 16 + React 19 + shadcn/ui + Tailwind CSS
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- Database：SQLite / MySQL
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- Vector DB：Qdrant
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- AI：OpenAI-compatible LLM / Embedding + RAG + Skills + MCP
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- Backend: Golang + Gin + GORM + `github.com/mlogclub/simple`
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- Frontend: Next.js 16 + React 19 + shadcn/ui + Tailwind CSS
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- Database: SQLite / MySQL
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- Vector DB: Qdrant
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- AI: OpenAI-compatible LLM / Embedding + RAG + Skills + MCP
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## 项目结构
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## Project Structure
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```text
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.
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├── cmd/ # server / migration / generator / testdata
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├── internal/
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│ ├── bootstrap/ # 启动、路由、数据库和迁移初始化
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│ ├── builders/ # model / 聚合结果到 response DTO 的映射
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│ ├── bootstrap/ # startup, routes, database, and migration initialization
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│ ├── builders/ # model / aggregate result to response DTO mapping
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│ ├── handlers/ # dashboard / api / third HTTP handlers
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│ ├── middleware/ # Gin middleware
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│ ├── migration/ # 幂等数据迁移
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│ ├── migration/ # idempotent data migrations
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│ ├── models/ # GORM models
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│ ├── repositories/ # 数据访问层
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│ ├── services/ # 业务编排和事务边界
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│ ├── repositories/ # data access layer
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│ ├── services/ # business orchestration and transaction boundaries
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│ ├── ai/ # LLM / RAG / Runtime / Skills / MCP
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│ └── pkg/ # config / dto / enums / httpx / utils 等基础包
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├── web/ # Next.js 前端工程
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│ ├── app/dashboard/ # 管理后台与客服工作台
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│ ├── app/support/ # 客户侧接入和聊天页面
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│ ├── components/ # React 组件
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│ ├── lib/ # API client、SDK 源码和工具函数
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│ └── public/sdk/ # 构建后的嵌入式 SDK
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├── config/ # 配置文件
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├── docker/ # Docker 配置
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└── docs/ # 项目文档
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│ └── pkg/ # config / dto / enums / httpx / utils and shared packages
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├── web/ # Next.js frontend project
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│ ├── app/dashboard/ # admin dashboard and agent workspace
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│ ├── app/support/ # customer integration and chat pages
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│ ├── components/ # React components
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│ ├── lib/ # API client, SDK source, and utilities
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│ └── public/sdk/ # built embeddable SDK
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├── config/ # configuration files
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├── docker/ # Docker configuration
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└── docs/ # documentation site
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```
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## 常用命令
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## Common Commands
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```bash
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make dev # 同时启动后端和前端开发服务
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make run # 构建前端 SPA 后启动后端
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make run-go # 启动后端，自动确保 SPA 已构建
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make web-dev # 启动前端开发服务
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make build # 构建前端 SPA 和当前平台 Go 二进制
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make build-linux # 构建 linux/amd64 二进制
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make release # 构建常用平台二进制
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make web-build-spa # 构建 web 静态 SPA 和嵌入式 SDK
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make test # 运行 Go 测试，自动确保 SPA 已构建
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make check # 运行 Go 测试、前端 typecheck 和 lint
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make generator # 执行代码生成
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make enums # 生成前端枚举
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make migration # 执行 migration
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make testdata # 初始化演示/测试数据
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make dev # start backend and frontend development servers
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make run # build the frontend SPA, then start the backend
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make run-go # start the backend and ensure the SPA has been built
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make web-dev # start the frontend development server
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make build # build the frontend SPA and current-platform Go binary
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make build-linux # build the linux/amd64 binary
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make release # build common release binaries
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make web-build-spa # build the web static SPA and embeddable SDK
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make test # run Go tests after ensuring the SPA is built
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make check # run Go tests, frontend typecheck, and lint
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make generator # run code generation
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make enums # generate frontend enums
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make migration # run migrations
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make testdata # initialize demo/test data
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```
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## AI Agent 工作流
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## AI Agent Workflow
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```mermaid
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flowchart TD
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A[用户发起咨询<br/>Web 客服入口 / Open API] --> B[创建或匹配会话]
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B --> C[客户发送消息]
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C --> D[触发 AI Reply Runtime]
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D --> E[加载会话历史 / AI 配置]
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E --> F[按绑定知识库执行检索]
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F --> G{知识片段是否足以回答?}
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G -- 否 --> Z[返回知识库兜底提示<br/>并建议联系人工客服]
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G -- 是 --> H[准备 Skills / MCP Tools]
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H --> I[将可信知识上下文交给 Agent]
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I --> J{直接回复?}
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J -- 是 --> K[LLM 基于知识生成回复并返回用户]
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J -- 否 --> N{是否调用 Graph / MCP Tool?}
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N -- 是 --> O[执行 Skill / Graph / MCP Tool]
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O --> P{需要用户确认?}
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P -- 否 --> I
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P -- 是 --> Q[向用户发起确认]
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Q --> R{用户确认结果}
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R -- 确认转人工 --> S[会话转人工并进入待接入池]
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S --> T[自动分配或人工分配]
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T --> U[客服工作台接管]
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U --> V{是否需要工单跟踪?}
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V -- 是 --> W[创建或关联工单]
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V -- 否 --> X[人工继续处理]
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A[User starts a support request<br/>Web support entry / Open API] --> B[Create or match a conversation]
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B --> C[Customer sends a message]
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C --> D[Trigger AI Reply Runtime]
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D --> E[Load conversation history / AI configuration]
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E --> F[Retrieve from bound knowledge bases]
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F --> G{Are retrieved chunks enough to answer?}
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G -- No --> Z[Return knowledge fallback<br/>and recommend human support]
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G -- Yes --> H[Prepare Skills / MCP Tools]
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H --> I[Pass trusted knowledge context to the Agent]
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I --> J{Direct reply?}
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J -- Yes --> K[LLM generates a knowledge-grounded reply]
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J -- No --> N{Call Graph / MCP Tool?}
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N -- Yes --> O[Run Skill / Graph / MCP Tool]
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O --> P{Need user confirmation?}
|
||||
P -- No --> I
|
||||
P -- Yes --> Q[Ask the user to confirm]
|
||||
Q --> R{Confirmation result}
|
||||
R -- Confirm handoff --> S[Move conversation to human handoff pool]
|
||||
S --> T[Automatic or manual assignment]
|
||||
T --> U[Agent workspace takeover]
|
||||
U --> V{Need ticket tracking?}
|
||||
V -- Yes --> W[Create or link a ticket]
|
||||
V -- No --> X[Human agent continues handling]
|
||||
W --> X
|
||||
X --> Y[问题解决并关闭]
|
||||
R -- 确认建单 --> AA[从当前会话创建工单]
|
||||
X --> Y[Resolve and close]
|
||||
R -- Confirm ticket --> AA[Create a ticket from the current conversation]
|
||||
AA --> I
|
||||
R -- 取消 --> K
|
||||
N -- 否 --> K
|
||||
R -- Cancel --> K
|
||||
N -- No --> K
|
||||
```
|
||||
|
||||
## 业务闭环
|
||||
## Support Loop
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[客户咨询] --> B[AI Agent 接待]
|
||||
B --> C{知识库可回答?}
|
||||
C -- 是 --> D[AI 基于可信知识回复]
|
||||
C -- 否 --> E[兜底提示 / 建议人工]
|
||||
D --> F{是否需要人工?}
|
||||
E --> G[人工接管]
|
||||
F -- 否 --> H[会话结束或沉淀数据]
|
||||
F -- 是 --> G
|
||||
G --> I[客服工作台处理]
|
||||
I --> J{是否需要跟踪?}
|
||||
J -- 是 --> K[创建 / 关联工单]
|
||||
J -- 否 --> L[直接解决]
|
||||
K --> M[工单流转与进展记录]
|
||||
M --> N[处理完成]
|
||||
A[Customer request] --> B[AI Agent handles first]
|
||||
B --> C{Can the knowledge base answer?}
|
||||
C -- Yes --> D[AI replies with trusted knowledge]
|
||||
C -- No --> E[Fallback / recommend human support]
|
||||
D --> F{Need a human?}
|
||||
E --> G[Human takeover]
|
||||
F -- No --> H[Conversation ends or data is retained]
|
||||
F -- Yes --> G
|
||||
G --> I[Agent workspace handles the case]
|
||||
I --> J{Need follow-up tracking?}
|
||||
J -- Yes --> K[Create / link a ticket]
|
||||
J -- No --> L[Resolve directly]
|
||||
K --> M[Ticket status flow and progress records]
|
||||
M --> N[Complete]
|
||||
L --> N
|
||||
```
|
||||
|
||||
## Docker 镜像
|
||||
## Docker Image
|
||||
|
||||
如果只需要构建应用镜像，可以自行准备 MySQL 和 Qdrant，并挂载配置文件：
|
||||
If you only need to build the application image, prepare MySQL and Qdrant yourself and mount a configuration file:
|
||||
|
||||
```bash
|
||||
docker build -t cs-ai-agent .
|
||||
@@ -269,15 +271,15 @@ docker run --rm -p 8083:8083 \
|
||||
cs-ai-agent
|
||||
```
|
||||
|
||||
Compose 使用 [docker/cs-ai-agent.yaml](docker/cs-ai-agent.yaml) 作为容器内配置，应用会通过 Docker 内部服务名访问 `mysql` 和 `qdrant`。
|
||||
Compose uses [docker/cs-ai-agent.yaml](docker/cs-ai-agent.yaml) as the in-container configuration. The application reaches `mysql` and `qdrant` through Docker service names.
|
||||
|
||||
## 开源定位
|
||||
## Open-source Positioning
|
||||
|
||||
`贝壳 AI 客服`适合作为以下方向的开源基础项目：
|
||||
`Beike AI Support` is useful as an open-source foundation for:
|
||||
|
||||
- AI 客服系统
|
||||
- AI Helpdesk / AI Support Platform
|
||||
- RAG 可回答性判定 + Human Handoff 的落地样板
|
||||
- 面向企业场景的 AI Agent 应用框架
|
||||
- AI customer support systems
|
||||
- AI Helpdesk / AI Support Platform projects
|
||||
- RAG answerability + human handoff implementation references
|
||||
- Enterprise AI Agent application frameworks
|
||||
|
||||
如果你在寻找一个以 AI Agent 为中心，而不是仅仅把 LLM 嵌进聊天框的客服系统，这个项目就是为此设计的。
|
||||
If you are looking for a customer support system centered on AI Agents rather than a simple LLM chat box, this project is designed for that purpose.
|
||||
|
||||
+285
@@ -0,0 +1,285 @@
|
||||
# 贝壳 AI 客服
|
||||
|
||||
[English](README.md) | 简体中文
|
||||
|
||||
开源的 AI Agent 客服系统，支持知识库问答、人工接管、工单闭环和私有化部署。
|
||||
|
||||
> 面向需要同时处理在线咨询、知识库问答、人工协同和服务跟踪的团队。它不是把 LLM 接进聊天框，而是一套围绕客服场景设计的 AI Helpdesk 基础系统。
|
||||
|
||||
## 产品预览
|
||||
|
||||
客户侧在线咨询、客服工作台、知识库、模型配置和 AI Agent 编排都在同一套系统中完成。
|
||||
|
||||
### 客户侧在线咨询
|
||||
|
||||
|
||||
|
||||
客户可以在 Web 聊天页中直接发起咨询。AI Agent 会先接待，基于知识库回答问题；当用户明确要求人工介入时，会触发转人工确认流程。
|
||||
|
||||
### 客服工作台
|
||||
|
||||
|
||||
|
||||
客服工作台支持会话列表、消息处理、AI 转人工、客服回复、会话标签、关联客户和工单信息查看，适合客服日常接待使用。
|
||||
|
||||
### 知识库与 AI 配置
|
||||
|
||||
| 知识库 FAQ | AI Agent 配置 |
|
||||
| --- | --- |
|
||||
|  |  |
|
||||
|
||||
知识库用于沉淀 FAQ、文档和可检索内容；AI Agent 可以绑定模型配置、知识库、Skills 和工具能力，形成面向具体客服场景的智能客服实例。
|
||||
|
||||
### 模型配置
|
||||
|
||||
|
||||
|
||||
模型配置支持 OpenAI-compatible 接入方式，可分别配置大语言模型、向量模型和重排模型，并管理上下文、输出、超时、重试和启用状态。
|
||||
|
||||
## 为什么选择它
|
||||
|
||||
- **AI 先接待**：让 AI Agent 优先处理常见问题、标准流程和知识库问答。
|
||||
- **知识约束回答**：通过 RAG 和 Answerability Gate 判断知识片段是否足以回答，减少超出知识库范围的乱答。
|
||||
- **自然转人工**：当知识库不足、用户明确要求或流程需要人工确认时，进入人工接管。
|
||||
- **会话到工单闭环**：在线会话、客服接待、工单创建、状态流转和处理记录在同一套系统里完成。
|
||||
- **适合二次开发**：后端使用 Go，前端使用 Next.js，支持 Skills、MCP 和 OpenAI-compatible 模型接入。
|
||||
- **可私有化部署**：支持 SQLite / MySQL 和 Qdrant，适合本地体验、内网部署和企业自托管。
|
||||
|
||||
## 核心能力
|
||||
|
||||
- **AI Agent 客服**：AI 优先回复，支持兜底、确认、工具调用和人工协同。
|
||||
- **在线会话系统**：支持访客会话、消息收发、未读状态、会话分配、转接和关闭。
|
||||
- **客服工作台**：客服可接管会话、回复用户、转接同事、关联客户和创建工单。
|
||||
- **知识库 RAG**：支持知识库、文档、FAQ、切片、向量检索、检索日志和质量分析。
|
||||
- **Answerability Gate**：判断检索内容是否足以支撑回答，不足时返回兜底提示并建议联系人工。
|
||||
- **工单系统**：支持从会话创建工单、分类、指派、状态流转、进展记录和闭环处理。
|
||||
- **客服组织管理**：支持客服档案、客服组、排班和自动分配能力。
|
||||
- **AI 扩展能力**：支持 Skills、MCP 调试和外部工具接入。
|
||||
- **多入口接入**：提供管理后台、客服工作台、客户侧 Web 页面和嵌入式 SDK。
|
||||
|
||||
## 适用场景
|
||||
|
||||
- 官网在线客服
|
||||
- SaaS 产品支持
|
||||
- AI + 人工混合接待
|
||||
- 企业内部服务台
|
||||
- 售后、报障、投诉和运营支持
|
||||
- 需要知识库问答与人工协同的客服团队
|
||||
|
||||
## 快速开始
|
||||
|
||||
推荐先用 Docker Compose 体验完整服务：
|
||||
|
||||
```bash
|
||||
docker compose up -d --build
|
||||
```
|
||||
|
||||
Compose 默认会启动：
|
||||
|
||||
- `cs-ai-agent`：应用服务，端口 `8083`
|
||||
- `mysql`：MySQL 8.4，数据卷 `mysql-data`
|
||||
- `qdrant`：向量数据库，数据卷 `qdrant-data`，端口 `6333` / `6334`
|
||||
|
||||
启动后访问：
|
||||
|
||||
- 管理后台：`http://localhost:8083/dashboard`
|
||||
- 客服工作台：`http://localhost:8083/dashboard/conversations`
|
||||
- 客户侧 Web 接入示例：`http://localhost:8083/support/demo`
|
||||
- 客户侧聊天页：`http://localhost:8083/support/chat`
|
||||
|
||||
默认管理员账号：
|
||||
|
||||
- 用户名：`admin`
|
||||
- 密码：`ChangeMe123!`
|
||||
|
||||
> 首次用于公网或团队环境前，请务必修改默认管理员密码，并配置独立的鉴权、会话和模型密钥。
|
||||
|
||||
## 本地开发
|
||||
|
||||
### 环境要求
|
||||
|
||||
- Go `1.26+`
|
||||
- Node.js `20+`
|
||||
- `pnpm`
|
||||
- Qdrant
|
||||
|
||||
### 准备配置
|
||||
|
||||
```bash
|
||||
cp config/config.example.yaml config/config.yaml
|
||||
```
|
||||
|
||||
默认配置使用：
|
||||
|
||||
- SQLite：`data/app.db`
|
||||
- Backend：`http://127.0.0.1:8083`
|
||||
- Qdrant gRPC：`127.0.0.1:6334`
|
||||
|
||||
如果本地还没有 Qdrant，可以用 Docker 启动：
|
||||
|
||||
```bash
|
||||
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
|
||||
```
|
||||
|
||||
安装前端依赖：
|
||||
|
||||
```bash
|
||||
cd web
|
||||
pnpm install
|
||||
cd ..
|
||||
```
|
||||
|
||||
同时启动后端和前端开发服务：
|
||||
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
或分别启动：
|
||||
|
||||
```bash
|
||||
make run-go
|
||||
make web-dev
|
||||
```
|
||||
|
||||
开发环境默认入口：
|
||||
|
||||
- 管理后台：`http://localhost:3000/dashboard`
|
||||
- 客服工作台：`http://localhost:3000/dashboard/conversations`
|
||||
- 客户侧 Web 接入示例：`http://localhost:3000/support/demo`
|
||||
- 客户侧聊天页：`http://localhost:3000/support/chat`
|
||||
|
||||
## 技术栈
|
||||
|
||||
- Backend：Golang + Gin + GORM + `github.com/mlogclub/simple`
|
||||
- Frontend：Next.js 16 + React 19 + shadcn/ui + Tailwind CSS
|
||||
- Database：SQLite / MySQL
|
||||
- Vector DB：Qdrant
|
||||
- AI：OpenAI-compatible LLM / Embedding + RAG + Skills + MCP
|
||||
|
||||
## 项目结构
|
||||
|
||||
```text
|
||||
.
|
||||
├── cmd/ # server / migration / generator / testdata
|
||||
├── internal/
|
||||
│ ├── bootstrap/ # 启动、路由、数据库和迁移初始化
|
||||
│ ├── builders/ # model / 聚合结果到 response DTO 的映射
|
||||
│ ├── handlers/ # dashboard / api / third HTTP handlers
|
||||
│ ├── middleware/ # Gin middleware
|
||||
│ ├── migration/ # 幂等数据迁移
|
||||
│ ├── models/ # GORM models
|
||||
│ ├── repositories/ # 数据访问层
|
||||
│ ├── services/ # 业务编排和事务边界
|
||||
│ ├── ai/ # LLM / RAG / Runtime / Skills / MCP
|
||||
│ └── pkg/ # config / dto / enums / httpx / utils 等基础包
|
||||
├── web/ # Next.js 前端工程
|
||||
│ ├── app/dashboard/ # 管理后台与客服工作台
|
||||
│ ├── app/support/ # 客户侧接入和聊天页面
|
||||
│ ├── components/ # React 组件
|
||||
│ ├── lib/ # API client、SDK 源码和工具函数
|
||||
│ └── public/sdk/ # 构建后的嵌入式 SDK
|
||||
├── config/ # 配置文件
|
||||
├── docker/ # Docker 配置
|
||||
└── docs/ # 项目文档
|
||||
```
|
||||
|
||||
## 常用命令
|
||||
|
||||
```bash
|
||||
make dev # 同时启动后端和前端开发服务
|
||||
make run # 构建前端 SPA 后启动后端
|
||||
make run-go # 启动后端，自动确保 SPA 已构建
|
||||
make web-dev # 启动前端开发服务
|
||||
make build # 构建前端 SPA 和当前平台 Go 二进制
|
||||
make build-linux # 构建 linux/amd64 二进制
|
||||
make release # 构建常用平台二进制
|
||||
make web-build-spa # 构建 web 静态 SPA 和嵌入式 SDK
|
||||
make test # 运行 Go 测试，自动确保 SPA 已构建
|
||||
make check # 运行 Go 测试、前端 typecheck 和 lint
|
||||
make generator # 执行代码生成
|
||||
make enums # 生成前端枚举
|
||||
make migration # 执行 migration
|
||||
make testdata # 初始化演示/测试数据
|
||||
```
|
||||
|
||||
## AI Agent 工作流
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A[用户发起咨询<br/>Web 客服入口 / Open API] --> B[创建或匹配会话]
|
||||
B --> C[客户发送消息]
|
||||
C --> D[触发 AI Reply Runtime]
|
||||
D --> E[加载会话历史 / AI 配置]
|
||||
E --> F[按绑定知识库执行检索]
|
||||
F --> G{知识片段是否足以回答?}
|
||||
G -- 否 --> Z[返回知识库兜底提示<br/>并建议联系人工客服]
|
||||
G -- 是 --> H[准备 Skills / MCP Tools]
|
||||
H --> I[将可信知识上下文交给 Agent]
|
||||
I --> J{直接回复?}
|
||||
J -- 是 --> K[LLM 基于知识生成回复并返回用户]
|
||||
J -- 否 --> N{是否调用 Graph / MCP Tool?}
|
||||
N -- 是 --> O[执行 Skill / Graph / MCP Tool]
|
||||
O --> P{需要用户确认?}
|
||||
P -- 否 --> I
|
||||
P -- 是 --> Q[向用户发起确认]
|
||||
Q --> R{用户确认结果}
|
||||
R -- 确认转人工 --> S[会话转人工并进入待接入池]
|
||||
S --> T[自动分配或人工分配]
|
||||
T --> U[客服工作台接管]
|
||||
U --> V{是否需要工单跟踪?}
|
||||
V -- 是 --> W[创建或关联工单]
|
||||
V -- 否 --> X[人工继续处理]
|
||||
W --> X
|
||||
X --> Y[问题解决并关闭]
|
||||
R -- 确认建单 --> AA[从当前会话创建工单]
|
||||
AA --> I
|
||||
R -- 取消 --> K
|
||||
N -- 否 --> K
|
||||
```
|
||||
|
||||
## 业务闭环
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[客户咨询] --> B[AI Agent 接待]
|
||||
B --> C{知识库可回答?}
|
||||
C -- 是 --> D[AI 基于可信知识回复]
|
||||
C -- 否 --> E[兜底提示 / 建议人工]
|
||||
D --> F{是否需要人工?}
|
||||
E --> G[人工接管]
|
||||
F -- 否 --> H[会话结束或沉淀数据]
|
||||
F -- 是 --> G
|
||||
G --> I[客服工作台处理]
|
||||
I --> J{是否需要跟踪?}
|
||||
J -- 是 --> K[创建 / 关联工单]
|
||||
J -- 否 --> L[直接解决]
|
||||
K --> M[工单流转与进展记录]
|
||||
M --> N[处理完成]
|
||||
L --> N
|
||||
```
|
||||
|
||||
## Docker 镜像
|
||||
|
||||
如果只需要构建应用镜像，可以自行准备 MySQL 和 Qdrant，并挂载配置文件：
|
||||
|
||||
```bash
|
||||
docker build -t cs-ai-agent .
|
||||
docker run --rm -p 8083:8083 \
|
||||
-v $(pwd)/docker/cs-ai-agent.yaml:/app/config/config.yaml:ro \
|
||||
-v cs-ai-agent-data:/app/data \
|
||||
cs-ai-agent
|
||||
```
|
||||
|
||||
Compose 使用 [docker/cs-ai-agent.yaml](docker/cs-ai-agent.yaml) 作为容器内配置，应用会通过 Docker 内部服务名访问 `mysql` 和 `qdrant`。
|
||||
|
||||
## 开源定位
|
||||
|
||||
`贝壳 AI 客服`适合作为以下方向的开源基础项目：
|
||||
|
||||
- AI 客服系统
|
||||
- AI Helpdesk / AI Support Platform
|
||||
- RAG 可回答性判定 + Human Handoff 的落地样板
|
||||
- 面向企业场景的 AI Agent 应用框架
|
||||
|
||||
如果你在寻找一个以 AI Agent 为中心，而不是仅仅把 LLM 嵌进聊天框的客服系统，这个项目就是为此设计的。
|
||||
+1
-1
Submodule docs updated: 5fcf626b2d...b00f5b8e09
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