Healthcare AI Demo

ACME Medical Clinic
AI Assistant

An intelligent conversational AI system for healthcare appointment management, powered by RAG (Retrieval-Augmented Generation), vector search, and advanced natural language processing.

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What This Demo Does

Experience a fully functional AI-powered medical assistant that handles appointments, answers questions, and provides personalized healthcare information.

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Smart Appointment Booking

Multi-step conversational flow guides users through selecting specialization, doctor, date, and time while checking real-time availability.

  • check Context-aware conversation flow
  • check Real-time availability checking
  • check Conflict detection & validation
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RAG-Powered Knowledge Base

Semantic search using vector embeddings finds the most relevant information from a comprehensive knowledge base about services, pricing, and procedures.

  • check HuggingFace embedding generation
  • check PostgreSQL pgvector similarity search
  • check Context-aware answer generation
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Session Management

Persistent conversation state allows users to continue where they left off, with full chat history and data collection across sessions.

  • check Cookie-based session tracking
  • check Conversation history persistence
  • check State machine for workflow

How It Works

Behind the scenes: The technical architecture powering this intelligent assistant

Conversation Flow Architecture

1

Intent Detection

Analyzes user input to determine whether they want to book, query, cancel, or get information

2

State Machine Processing

Multi-step workflow guides users through required information collection

3

Data Validation

Real-time validation of dates, times, phone numbers, and conflict checking

4

Database Persistence

All data stored securely in PostgreSQL with session management

Example Booking Flow

User: "I want to book an appointment"
Bot: Shows specialization options
User: "1" (Cardiology)
Bot: Shows available doctors
User: "1" (Dr. John Doe)
Bot: Shows available dates
... continues until complete

RAG Knowledge Retrieval

📝 User asks: "What are your hours?"
🔄 Generate embedding via HuggingFace API
🔍 Vector similarity search in pgvector
📚 Retrieve top 3 relevant documents
🤖 Send context + query to Groq LLM
💬 Generate natural language response

RAG (Retrieval-Augmented Generation)

Instead of relying solely on the LLM's knowledge, RAG enhances responses with relevant information from a custom knowledge base.

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Accurate Information

Responses based on your specific business data

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Always Up-to-Date

Update knowledge base without retraining models

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Source Attribution

Know exactly where information comes from

Technology Stack

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Groq API

LLama 3.3 70B for fast, intelligent responses

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HuggingFace

Sentence transformers for text embeddings

database

PostgreSQL + pgvector

Vector database for semantic search

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ASP.NET Core

C# backend with Razor Pages

Key Features & Capabilities

token Conversation Features

  • • Multi-turn dialogue support
  • • Context retention across messages
  • • Intent recognition
  • • Entity extraction
  • • Natural language understanding

storage Data Management

  • • Session persistence
  • • Chat history logging
  • • Appointment conflict detection
  • • Doctor schedule management
  • • Real-time availability

search Search & Retrieval

  • • Vector similarity search
  • • Semantic matching
  • • Knowledge base queries
  • • HNSW index for speed
  • • Cosine similarity ranking

Ready to Try It?

Experience the power of AI-driven healthcare automation. Book an appointment, ask questions, and explore the capabilities of this intelligent assistant.

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Want to Build Something Similar?

Interested in implementing AI chatbots for your business? I can help you build custom solutions tailored to your specific needs.

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