What is Mcp Rag Agency Book Appointments

mcp-rag-agency-book-appointments is an AI-powered appointment booking system designed for agencies to efficiently schedule appointments and manage queues in real-time. It utilizes advanced technologies like AI Chat Bots and Retrieval-Augmented Generation (RAG) for context-aware decision-making.

Use cases

Use cases include booking medical appointments, scheduling consultations, managing service appointments, and any scenario where real-time queue management and appointment booking are essential.

How to use

Users can interact with the system through a user-friendly API, allowing them to book appointments, manage queues, and utilize AI features such as smart queries and document scanning. The system supports natural language prompts for easy interaction.

Key features

Key features include real-time queue management, built-in IdentityServer, AI Chat Bots, RAG for enhanced responses, support for off days and daily limits, microservices architecture, and integration with Apache Kafka and ElasticSearch for optimized performance.

Where to use

This system is ideal for agencies in various sectors, including healthcare, service industries, and any organization that requires efficient appointment scheduling and queue management.

🗓️ AI-Powered Appointment Booking System

✨ Overview

This project is a cutting-edge AI-driven appointment booking system designed for agencies to schedule appointments, and manage queues in real-time efficiently. With AI Chat Bots, Retrieval-Augmented Generation (RAG), and MCP Client/Server integration, the system enables context-aware decision-making using external knowledge sources.

Built with Domain-Driven Development (DDD) principles, the system leverages Apache Kafka & ElasticSearch for appointment indexing, ensuring optimized search & performance across thousands of appointments! The architecture is microservices-ready, making it scalable and modular for enterprise adoption. Now powered by Qdrant Vector DB, it supports AI-driven semantic search and retrieval for enhanced user experiences.

🚀 Key Features

✅ Built-in IdentityServer with Asymmetric JWT Signing 🔐
✅ AI Chat Bots via PromptAPI 🤖
✅ Retrieval-augmented generation (RAG) – combines real-time knowledge retrieval with language generation for smarter, context-aware responses 🧠🔍
✅ Intelligent document parsing using LLM – extracts clean, structured, semantically meaningful text from messy HTML and scanned PDFs 📄🧹🤖
✅ Users can update the AI brain using RAG by scanning URLs & PDF documents on the fly – parsed content is embedded and stored in Qdrant for semantic search 📄🌐⚡
✅ RAG with Hangfire for document scan, parse & upload to Qdrant ⚙️
✅ MCP client/server ready ⚡
✅ Supports off days & max daily appointments 📅
✅ Real-time queue grid via API ⏳
✅ Domain-driven development (DDD) architecture 🏗️
✅ Event dispatcher for domain events 📨
✅ Apache Kafka & ElasticSearch for appointment indexing 📡
✅ Qdrant vector database for AI semantic search 🧠✨
✅ FluentValidation for validation logic ✅
✅ Swagger, LINQ, IoC, WebAPI 🛠️
✅ Automatic email template generation by AI LLM 📧✨
✅ API with Brain – users can type prompts in natural language 🧠📝
✅ Microservices-ready, modular & scalable 🏢🔄
✅ Cloud-ready (Azure/AWS) ☁️
✅ Refit-powered REST API clients 🔌
✅ Producer-consumer pattern with buffer cache for real-time insert, save & update 📤📥
✅ Next.js + React.js chatbot UI – real-time chat interface integrated with backend LLM API 💬⚛️ – supports theme changes
✅ Redis implementation for faster public key retrieval and chat store caching 🧰🔑

📜 Architecture Diagram

User → API Gateway → Appointment Service → Event Processing (Kafka) → Search Index (ElasticSearch)  
                  ↳ AI Decision Layer (RAG, MCP Client/Server, Qdrant Vector DB)  

🔄 User Flow

1️⃣ User registers on the platform 📝
2️⃣ Admin assigns “Agent” role to the user 👤✅
3️⃣ Admin registers an agency to the system 🏢
4️⃣ Agent adds agency users/customers (who will book appointments) 👥
5️⃣ Agent schedules an appointment for an agency user/customer 📅
6️⃣ AI automatically generates an appointment confirmation email template ✉️🤖
7️⃣ Appointment is indexed in Apache Kafka & ElasticSearch for real-time search 📡
8️⃣ User/customer gets notified with details via AI-enhanced email template 🚀
9️⃣ User interacts with AI freely via API with Brain – type any prompt, get smart AI responses 🧠💬
🔟 Qdrant Vector DB enhances search accuracy with AI-powered similarity matching 🔍💡
1️⃣1️⃣ Microservices-ready architecture ensures efficient scaling across multiple agencies 🏢⚙️

This ensures a streamlined booking experience, allowing agencies to manage appointments efficiently with real-time indexing, AI-generated email templates, and AI-driven semantic search with Qdrant!

🧰 Tech Stack

🟦 .NET 9 – modern, performant runtime for cloud-native applications
🛡️ IdentityServer – secure authentication and token issuance
📅 Hangfire – background job scheduling for asynchronous workflows
📡 Apache Kafka – distributed event streaming platform
🔍 ElasticSearch – high-speed, full-text search for appointment indexing
🧠 Qdrant – vector DB for semantic AI search
🧾 PromptAPI – LLM-based AI chatbot integration
🔌 Refit – declarative REST API clients with interface-based contracts
✅ FluentValidation – fluent rules for robust input validation
🧪 Swagger / OpenAPI – API documentation and test interface
☁️ Azure / AWS Ready – cloud-native infrastructure compatible
📜 Marten DB (PostgreSQL) – event sourcing and document database
🐘 PostgreSQL – backing store for MartenDB event sourcing and ElasticSearch sync
🗄️ MS SQL Server – primary application database for transactional data
🧱 producer/consumer repository pattern – buffer-backed async layer for write-heavy workloads
⚛️ Next.js / React.js – fast, modern frontend framework for interactive chatbot UI
🧠 Redis – in-memory cache for fast public key access and general-purpose caching

🛡️ Security & Access Control

⚠️ Strict access policies & authentication layers
🔐 JWT-based authentication
🔄 Audit logs for booking activities

📬 Contributing

We welcome new features, bug fixes, and performance improvements. 🚀
Feel free to submit pull requests or open issues!

⚡ Future Enhancements

🔮 AI-driven appointment recommendations
📢 Automated notifications for schedule changes
📡 Machine Learning for capacity prediction

📜 License - Apache License 2.0 (TL;DR)

This project follows the Apache License 2.0, which means:

• ✅ You can use, modify, and distribute the code freely.
• ✅ You must include the original license when distributing.
• ✅ You must include the NOTICE file if one is provided.
• ✅ You can use this in personal & commercial projects.
• ✅ No warranties – use at your own risk! 🚀

For full details, check the Apache License 2.0.

💡 This system isn’t just another booking tool—it’s an intelligent, scalable AI-powered solution.
Let’s reshape the future of scheduling with AI, event-driven processing, scalable microservices, and AI-powered search with Qdrant Vector DB! 🚀🔥

This project is based on my other project: https://github.com/myonathanlinkedin/productinfo-mcp-rag

📸 Screenshots of the chatbots