EcommSpringBot

# Intelligent Customer Service Using Spring AI Alibaba > Spring AI Alibaba Repo: https://github.com/alibaba/spring-ai-alibaba > > Spring AI Alibaba Website: https://java2ai.com > > Spring AI Alibaba Website Repo: https://github.com/springaialibaba/spring-ai-alibaba-website EcommSpringBot EcommSpringBot is an enterprise-level intelligent customer service assistant system based on SpringAI Alibaba. It supports users to complete order inquiries, automatic cancellations, rule Q&A and other operations through natural language interaction. It integrates microservice architecture, RAG technology, Redis chat context memory and Elasticsearch vector database to realize a privatized and scalable intelligent agent service platform. 🌟 Project Highlights • 🧠 Implemented enterprise intelligent agent dialogue capabilities based on SpringAI Alibaba (supports Tongyi Qianwen/OpenAI) • 🔗 Modular design: Order service, middle-end service, SSE connection to MCP Server, and RAG knowledge base are clearly separated • 🧾 Supports natural language query orders and automatic cancellation of orders • 📚 Use Elasticsearch to build an enterprise knowledge base, supporting RAG retrieval enhancement • 🧠 Use Redis to implement context memory function, supporting multi-turn conversations 🏗️ Module Structure EcommSpringBot/ ├── mall-order/ # Core order service (Spring Boot + MySQL) ├── mall-order-cmp-server/ # CMP middle-end service (encapsulates order capabilities) ├── mall-order-cmp_sso-client/ # Provides Controller interface + Redis chat context support ├── mall-order-es-rag/ # Vector knowledge base module (return and exchange rule documents → ES vector) ├── mall-order-graph-server/ # MCP graph computing service (based on Spring AI Alibaba) └── README.md 📌 Features ✅ 1. Natural Language Operation Order Service • Users can use natural language dialogues, such as: • "I want to query the order of user USER1005" • "Help me cancel the order of user USER1005" Automatically match the interface through Spring Ai Alibaba Function Calling and call the mall-order service to complete. ⸻ ✅ 2. SSE Real-time Reply + Redis Context Memory • Use SSE (Server-Sent Events) to establish a streaming connection • Implement user dialogue context memory based on Redis • Support continuous context query and concise and natural human-computer interaction experience ⸻ ✅ 3. Vector Retrieval Enhancement (RAG) + ES • Organize the contents of return and exchange rules, invoice instructions, etc. to build a knowledge base and provide dynamic modification of the knowledge base content • Use the text-embedding-v1 model to generate vectors and store them in Elasticsearch • Users can automatically semantically match the content of the document and answer questions such as "How long is the return time for my product?" 🚀 Quick Start Prerequisites • JDK 17+ • Maven 3.9+ • Redis • MySQL • Elasticsearch 8.11+ Startup sequence # Start the mall-order service cd mall-order && mvn spring-boot:run # Start mall-order-cmp-server cd ../mall-order-cmp-server && mvn spring-boot:run # Start the sso-client interface layer (including AI intelligent agent interface) cd ../mall-order-cmp_sso-client && mvn spring-boot:run # Start the es-rag service (for knowledge base management) cd ../mall-order-es-rag && mvn spring-boot:run 📖 📄 LICENSE This project is open sourced based on Apache 2.0 License and can be used, modified and distributed freely. ⸻ If you think this project is valuable, you are welcome to Star⭐️ or Fork🍴! 📬 Contact the author: 996766130@qq.com ## Front-end page