🏥 SehatAI - Advanced Healthcare Intelligence Platform

Empowering Healthcare with Artificial Intelligence

A comprehensive, production-ready AI healthcare platform leveraging Large Language Models, RAG architecture, adversarial testing frameworks, and intelligent feedback systems for revolutionary healthcare delivery

Features • Installation • Quick Start • Architecture • Modules • API Docs • Contributing

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📋 Table of Contents

• Overview
  • Mission
  • What Makes SehatAI Special
• Features
  • LLM-Powered Intelligence
  • Health Analysis & Monitoring
  • Adversarial Testing & Validation
  • Feedback & Learning System
  • Security & Compliance
• System Architecture
  • Three-Tier Architecture
  • Component Breakdown
  • Technology Stack
• Installation
  • Prerequisites
  • Docker Installation
  • Manual Installation
  • Development Setup
• Quick Start
  • Basic Usage
  • API Usage
  • Command Line Interface
• Project Structure
• Modules
  • LLM Integration
  • Health Analyzer
  • Stress Testing Framework
  • Feedback System
• Configuration
• API Documentation
• Testing
• Deployment
• Performance Metrics
• Contributing
• Roadmap
• License
• Meet the Team
• Acknowledgments

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🌟 Overview

SehatAI (सेहत = Health in Hindi) is a cutting-edge, enterprise-grade healthcare intelligence platform that harnesses the transformative power of Large Language Models (LLMs) to revolutionize healthcare delivery, patient monitoring, medical decision support, and health data analysis. This comprehensive system represents the convergence of advanced artificial intelligence, rigorous software engineering, and healthcare domain expertise to deliver reliable, accurate, and actionable healthcare insights at scale.

Built with a modular, three-tier architecture, SehatAI integrates advanced AI capabilities through Retrieval-Augmented Generation (RAG), implements comprehensive adversarial testing frameworks to ensure reliability and accuracy, and maintains intelligent feedback loops for continuous learning and improvement. The platform is designed to meet the demanding requirements of modern healthcare systems while remaining accessible, scalable, and production-ready.

🎯 Mission

To democratize access to intelligent healthcare solutions by providing a scalable, reliable, and comprehensive AI-powered platform that:

• Assists healthcare professionals with accurate, evidence-based medical information
• Empowers patients with accessible, understandable health insights
• Bridges the healthcare gap through AI-driven decision support
• Ensures reliability through rigorous testing and validation
• Maintains privacy while delivering personalized care
• Evolves continuously through intelligent feedback mechanisms

✨ What Makes SehatAI Special?

• 🤖 Advanced LLM Integration: Leverages state-of-the-art language models (GPT-4, Claude, custom medical models) for sophisticated medical query understanding, context-aware response generation, and multi-turn conversations
• 📚 RAG-Based Architecture: Implements Retrieval-Augmented Generation to ground AI responses in verified medical knowledge, reducing hallucinations and ensuring factual accuracy
• 📊 Comprehensive Health Analysis: Multi-dimensional health data processing combining symptom analysis, risk assessment, trend detection, and personalized recommendation engines
• 🔬 Adversarial Testing Framework: Built-in comprehensive testing suite including load testing, accuracy validation, edge case detection, and continuous performance benchmarking
• 🔄 Intelligent Feedback Loop: Sophisticated feedback collection, analysis, and automated improvement pipeline that enables the system to learn from real-world usage
• 🛡️ Production-Ready Architecture: Enterprise-grade design with Docker containerization, horizontal scalability, high availability, and comprehensive monitoring
• 🔌 Modular Three-Tier Design: Clean separation between frontend (React/TypeScript), backend (Python/FastAPI), and LLM layer (RAG/AI processing) for maximum flexibility and maintainability
• 🏥 Healthcare-Focused: Purpose-built for medical applications with HIPAA-compliance considerations, medical terminology understanding, and clinical workflow integration
• 📈 Scalable Infrastructure: Designed to handle everything from small clinics to large hospital networks with consistent performance
• 🔐 Security-First: Built with healthcare data privacy standards, encryption, audit logging, and role-based access control

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🚀 Features

Core Capabilities

🧠 LLM-Powered Intelligence

SehatAI's LLM integration represents the core intelligence layer, providing sophisticated natural language understanding and generation capabilities specifically tuned for medical applications.

Natural Language Understanding

• Medical Terminology Processing: Advanced NLP specifically trained on medical vocabularies, including ICD-10 codes, SNOMED CT, RxNorm, and clinical terminology
• Multi-Language Support: Understanding of medical queries in multiple languages with context-aware translation
• Abbreviation Expansion: Automatic expansion of medical abbreviations and acronyms (e.g., "HTN" → "Hypertension")
• Entity Recognition: Extraction of medical entities including symptoms, conditions, medications, procedures, and anatomical references
• Intent Classification: Intelligent classification of query types (diagnosis seeking, medication information, symptom checking, general health questions)

Context-Aware Responses

• Conversation Memory: Maintains full conversation context across multiple turns for coherent, personalized medical consultations
• Patient History Integration: Incorporates patient medical history, current medications, allergies, and demographic information into responses
• Follow-up Question Handling: Intelligently handles clarifying questions and provides additional details based on conversation flow
• Contextual Disambiguation: Resolves ambiguous terms based on conversation context (e.g., "cold" as temperature vs. common cold)
• Progressive Disclosure: Provides information at appropriate depth based on user expertise level and query complexity

Multi-Modal Processing

• Text Input: Natural language queries, symptom descriptions, medical questions
• Medical Document Processing: PDF reports, lab results, imaging reports, discharge summaries
• Structured Data Integration: Integration with EHR systems, health tracking apps, wearable devices
• Image Analysis: (Planned) Analysis of medical images, skin conditions, wound photos
• Voice Input: (Planned) Voice-based queries with medical speech recognition

Custom Fine-Tuning

• Domain-Specific Adaptation: Models fine-tuned on medical literature, clinical guidelines, and verified health information
• Specialty-Specific Models: Specialized models for cardiology, oncology, pediatrics, and other medical specialties
• Regional Customization: Adaptation to regional healthcare practices, medication availability, and treatment protocols
• Continuous Learning: Models updated with latest medical research and clinical evidence
• Safety Constraints: Built-in guardrails to prevent harmful medical advice and ensure appropriate disclaimers

Multi-Provider Support

• OpenAI Integration: GPT-4, GPT-3.5-turbo for general medical queries
• Anthropic Claude: Claude-3-Opus, Claude-3-Sonnet for complex medical reasoning
• Cohere: For specialized embedding and classification tasks
• HuggingFace Models: Custom fine-tuned medical models (BioBERT, ClinicalBERT, PubMedBERT)
• Provider Failover: Automatic fallback to alternative providers for reliability

Prompt Engineering

• Medical Prompt Templates: Carefully crafted prompts optimized for medical accuracy and safety
• Chain-of-Thought Reasoning: Prompts that encourage step-by-step medical reasoning
• Few-Shot Learning: Example-based prompts for consistent response formatting
• Safety Instructions: Embedded safety guidelines and ethical considerations in every prompt
• Output Formatting: Structured outputs for easy integration with downstream systems

Token Optimization

• Smart Truncation: Intelligent context window management for long conversations
• Embedding Caching: Cached embeddings for frequently accessed medical documents
• Response Streaming: Real-time streaming for improved user experience
• Cost Monitoring: Track and optimize API costs across different providers
• Batch Processing: Efficient batch processing for non-urgent queries

📈 Health Analysis & Monitoring

The Health Analyzer module provides comprehensive tools for understanding, tracking, and predicting health conditions based on multiple data sources.

Symptom Analysis

• Intelligent Symptom Tracking: Record and track symptoms over time with severity scoring
• Symptom Correlation: Advanced algorithms to identify relationships between multiple symptoms
• Differential Diagnosis: Generates list of possible conditions ranked by probability based on symptom combinations
• Red Flag Detection: Automatically identifies urgent symptoms requiring immediate medical attention
• Symptom Progression: Tracks how symptoms evolve over time to identify patterns
• Body System Mapping: Maps symptoms to affected body systems for comprehensive analysis
• Severity Assessment: Quantifies symptom severity using validated medical scales

Medical Query Processing

• Evidence-Based Answers: All responses grounded in peer-reviewed medical literature
• Source Attribution: Clear citations to medical sources, guidelines, and research papers
• Confidence Scoring: Transparency about confidence level in each response
• Multiple Perspectives: Presents different medical viewpoints when applicable
• Latest Research: Incorporates recent medical research and updated guidelines
• Plain Language Translation: Converts complex medical jargon into understandable language
• Visual Aids: Includes diagrams, charts, and illustrations when helpful

Trend Detection

• Temporal Pattern Recognition: Identifies patterns in symptoms, vitals, and lab results over time
• Seasonal Correlations: Detects seasonal variations in symptoms and conditions
• Medication Response Tracking: Monitors response to treatments and medications
• Vital Sign Trends: Analyzes trends in blood pressure, heart rate, glucose, weight, etc.
• Anomaly Detection: Flags unusual deviations from baseline health metrics
• Predictive Analytics: Forecasts potential health issues based on current trends
• Visualization: Interactive charts and graphs for trend visualization

Risk Assessment

• Disease Risk Calculators: Validated risk scores for heart disease, diabetes, stroke, cancer
• Genetic Risk Integration: Incorporates family history and genetic predispositions
• Lifestyle Factor Analysis: Evaluates impact of diet, exercise, smoking, alcohol on health risks
• Age-Adjusted Risk: Risk calculations adjusted for patient age and demographics
• Comorbidity Consideration: Accounts for multiple existing health conditions
• Risk Stratification: Categorizes patients into risk tiers for prioritized care
• Intervention Recommendations: Suggests preventive measures based on risk profile

Personalized Recommendations

• Lifestyle Modifications: Tailored diet, exercise, and lifestyle advice
• Preventive Care Scheduling: Recommendations for screenings, vaccinations, check-ups
• Medication Reminders: Smart reminders based on prescription schedules
• Specialist Referrals: Intelligent suggestions for when to see specialists
• Resource Matching: Connects patients with relevant healthcare resources
• Goal Setting: Helps set and track realistic health improvement goals
• Behavior Change Support: Evidence-based strategies for sustainable health changes

Medication Interaction Checking

• Drug-Drug Interactions: Comprehensive database of medication interactions
• Drug-Food Interactions: Identifies foods to avoid with specific medications
• Drug-Disease Interactions: Flags medications contraindicated with patient conditions
• Dosage Verification: Validates appropriate dosing based on age, weight, kidney function
• Alternative Suggestions: Proposes alternative medications when interactions detected
• Side Effect Profiling: Predicts likely side effects based on patient profile
• Polypharmacy Analysis: Evaluates appropriateness of multiple medication regimens

🧪 Adversarial Testing & Validation

SehatAI implements a comprehensive testing framework to ensure system reliability, accuracy, and safety under all conditions.

Load Testing

• Concurrent User Simulation: Simulates hundreds to thousands of simultaneous users
• Traffic Pattern Replication: Mimics real-world traffic patterns including peak hours
• Geographic Distribution: Tests with requests from multiple geographic locations
• Sustained Load Testing: Multi-hour tests to identify memory leaks and degradation
• Spike Testing: Sudden traffic increases to test auto-scaling capabilities
• Stress Testing: Push system beyond capacity to identify breaking points
• Soak Testing: Extended duration tests (24+ hours) for stability validation

Accuracy Testing

• Medical Fact Verification: Validates responses against trusted medical databases
• Clinical Guideline Compliance: Ensures recommendations align with established guidelines
• Benchmark Datasets: Testing against curated medical Q&A datasets
• Expert Review: Periodic review of responses by medical professionals
• Comparative Analysis: Comparison with other medical AI systems
• Hallucination Detection: Identifies and flags fabricated or incorrect information
• Citation Verification: Validates that cited sources actually support claims

Edge Case Testing

• Rare Condition Scenarios: Tests with uncommon medical conditions
• Ambiguous Queries: Intentionally vague or unclear medical questions
• Contradictory Information: Handles conflicting patient-reported information
• Missing Context: Tests with incomplete patient information
• Multi-System Involvement: Complex cases affecting multiple body systems
• Emergency Scenarios: Tests urgent/emergency case handling
• Ethical Dilemmas: Scenarios involving medical ethics and difficult decisions

Performance Benchmarking

• Response Time Tracking: Continuous monitoring of API response times
• Latency Analysis: Breakdown of latency across system components
• Throughput Measurement: Requests per second capacity testing
• Resource Utilization: CPU, memory, disk, and network usage monitoring
• Error Rate Tracking: Monitoring of error rates across different endpoints
• Availability Metrics: Uptime and reliability measurements
• Cost Per Query: Economic efficiency tracking

Security Testing

• Penetration Testing: Regular security audits and vulnerability scanning
• Injection Attack Testing: SQL injection, prompt injection, XSS testing
• Authentication Testing: Validation of auth mechanisms and session management
• Authorization Testing: Verify proper access controls and permissions
• Data Encryption Validation: Ensure proper encryption of sensitive data
• HIPAA Compliance Audits: Regular compliance verification
• Privacy Leak Detection: Tests for unintended data disclosure

Regression Testing

• Automated Test Suites: Comprehensive test cases run on every code change
• Visual Regression: UI/UX consistency testing across updates
• API Contract Testing: Ensure API compatibility across versions
• Performance Regression: Monitor for performance degradation over time
• Accuracy Regression: Track changes in model accuracy after updates
• Cross-Browser Testing: Frontend testing across browsers and devices
• Database Migration Testing: Validate data integrity during schema changes

💬 Feedback & Learning System

A sophisticated feedback infrastructure that enables continuous improvement and learning from real-world usage.

User Feedback Collection

• Multi-Channel Collection: Web interface, API, mobile app, email feedback
• Inline Feedback: Thumbs up/down on every AI response
• Detailed Ratings: Multi-dimensional rating scales (accuracy, helpfulness, clarity)
• Free-Text Comments: Open-ended feedback for qualitative insights
• Suggestion Box: User suggestions for features and improvements
• Error Reporting: Structured bug and error reporting system
• User Surveys: Periodic satisfaction and usability surveys
• Session Recording: (With consent) Anonymized interaction recording for UX analysis

Sentiment Analysis

• Response Sentiment: Automatic sentiment analysis of user feedback comments
• Trend Analysis: Tracking sentiment trends over time
• Topic Clustering: Grouping feedback by common themes
• Emotion Detection: Identifying user emotions (frustration, satisfaction, confusion)
• Urgency Classification: Prioritizing critical feedback requiring immediate attention
• Comparative Sentiment: Sentiment comparison across features and versions
• Multi-Language Sentiment: Sentiment analysis in multiple languages

Model Performance Tracking

• Response Quality Metrics: Accuracy, relevance, completeness, coherence scoring
• User Engagement Metrics: Time on task, follow-up questions, task completion
• Clinical Accuracy Tracking: Medical accuracy of diagnoses and recommendations
• Hallucination Rate: Tracking frequency of incorrect or fabricated information
• Source Attribution Quality: Percentage of responses with proper citations
• Confidence Calibration: How well model confidence correlates with accuracy
• Comparative Performance: Benchmarking against baseline models

A/B Testing Framework

• Multi-Variant Testing: Simultaneously test multiple prompt variations
• Model Comparison: Compare different LLM models and versions
• Feature Testing: Test new features with subset of users
• UI/UX Testing: A/B test different interface designs
• Traffic Splitting: Intelligent user routing for experiments
• Statistical Significance: Automated statistical analysis of test results
• Gradual Rollouts: Slow feature releases to monitor impact

Automated Improvement Pipeline

• Feedback Aggregation: Automatic collection and summarization of feedback
• Issue Identification: AI-powered identification of common problems
• Priority Ranking: Automatic prioritization of improvement opportunities
• Retraining Triggers: Automated model retraining based on feedback thresholds
• Prompt Optimization: Automatic refinement of prompts based on performance
• Knowledge Base Updates: Integration of new medical information
• Performance Reports: Automated generation of improvement reports

Learning from Errors

• Error Cataloging: Systematic collection and categorization of errors
• Root Cause Analysis: Automated analysis of error sources
• Correction Tracking: Monitor effectiveness of error fixes
• Pattern Recognition: Identify recurring error patterns
• Preventive Measures: Implement safeguards against common errors
• Error Documentation: Comprehensive error documentation for future reference
• Team Notifications: Alert relevant teams about critical errors

🔐 Security & Compliance

Built from the ground up with healthcare data security and regulatory compliance as core requirements.

HIPAA Compliance Ready

• PHI Protection: Comprehensive safeguards for Protected Health Information
• Access Controls: Granular role-based access control (RBAC)
• Audit Trails: Complete logging of all PHI access and modifications
• Business Associate Agreements: Support for BAA requirements
• Risk Assessments: Regular security risk assessments
• Compliance Documentation: Comprehensive compliance documentation
• Training Materials: HIPAA training for team members

Data Encryption

• Encryption at Rest: AES-256 encryption for all stored data
• Encryption in Transit: TLS 1.3 for all network communications
• Key Management: Secure key rotation and management (AWS KMS, HashiCorp Vault)
• Database Encryption: Encrypted database fields for sensitive data
• Backup Encryption: Encrypted backups with secure key storage
• End-to-End Encryption: Optional E2E encryption for maximum privacy
• Secure Deletion: Cryptographic erasure for data deletion

Audit Logging

• Comprehensive Logs: Detailed logs of all system activities
• User Activity Tracking: Complete audit trail of user actions
• Access Logs: Who accessed what data and when
• Change Logs: Track all data modifications with timestamps
• Search and Filter: Powerful log search and filtering capabilities
• Log Retention: Configurable log retention policies
• Tamper-Proof Logs: Write-once logs for integrity
• Export Capabilities: Export logs for compliance reporting

Access Control

• Role-Based Access: Granular permissions based on user roles
• Multi-Factor Authentication: Optional 2FA/MFA support
• Session Management: Secure session handling with timeouts
• IP Whitelisting: Restrict access to specific IP ranges
• Device Authorization: Device-based access controls
• Temporary Access: Time-limited access grants
• Emergency Access: Break-glass procedures for emergencies

Privacy Features

• Data Minimization: Collect only necessary information
• Anonymization: De-identification of data for analysis
• Consent Management: User consent tracking and management
• Right to Deletion: GDPR-compliant data deletion
• Data Portability: Export user data in standard formats
• Privacy by Design: Privacy considerations in every feature
• Transparent Practices: Clear privacy policies and practices

Security Monitoring

• Intrusion Detection: Real-time detection of security threats
• Anomaly Detection: Identify unusual access patterns
• Vulnerability Scanning: Regular automated security scans
• Penetration Testing: Periodic professional security audits
• Security Alerts: Immediate alerts for security incidents
• Incident Response: Documented incident response procedures
• Security Updates: Rapid patching of security vulnerabilities

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🏗️ System Architecture

SehatAI implements a modern, scalable three-tier architecture with clear separation of concerns, enabling independent scaling, easier maintenance, and flexible deployment options.

Three-Tier Architecture

┌─────────────────────────────────────────────────────────────────┐
│                      PRESENTATION LAYER                          │
│                    (Frontend - Client Side)                      │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │  React + TypeScript Application (75% of codebase)        │  │
│  │  ├── Chat Interface (Medical Query UI)                   │  │
│  │  ├── Health Dashboard (Analytics & Visualizations)       │  │
│  │  ├── Patient Portal (Medical History, Reports)           │  │
│  │  ├── Admin Panel (System Management)                     │  │
│  │  └── Responsive Design (Mobile, Tablet, Desktop)         │  │
│  └──────────────────────────────────────────────────────────┘  │
│                              ↓↑                                  │
│                      HTTP/REST API over HTTPS                    │
└─────────────────────────────────────────────────────────────────┘
                               ↓↑
┌─────────────────────────────────────────────────────────────────┐
│                      APPLICATION LAYER                           │
│                    (Backend - Business Logic)                    │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │  Python FastAPI Server (22% of codebase)                 │  │
│  │  ├── RESTful API Routes                                  │  │
│  │  ├── Request Validation & Sanitization                   │  │
│  │  ├── Authentication & Authorization                       │  │
│  │  ├── Business Logic Layer                                │  │
│  │  ├── Data Processing & Transformation                    │  │
│  │  ├── Caching Layer (Redis)                               │  │
│  │  └── API Documentation (Swagger/OpenAPI)                 │  │
│  └──────────────────────────────────────────────────────────┘  │
│                              ↓↑                                  │
│                      Internal Service API                        │
└─────────────────────────────────────────────────────────────────┘
                               ↓↑
┌─────────────────────────────────────────────────────────────────┐
│                        AI/ML LAYER                               │
│               (LLM Service - Intelligence Engine)                │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │  RAG System (Retrieval-Augmented Generation)             │  │
│  │  ├── Vector Database (Pinecone/Weaviate/Chroma)          │  │
│  │  │   └── Medical Knowledge Base Embeddings               │  │
│  │  ├── Document Retrieval Engine                           │  │
│  │  │   └── Semantic Search & Ranking                       │  │
│  │  └── LLM Integration Layer                               │  │
│  │      ├── OpenAI (GPT-4, GPT-3.5-turbo)                   │  │
│  │      ├── Anthropic (Claude-3-Opus, Sonnet)               │  │
│  │      ├── HuggingFace (BioBERT, ClinicalBERT)             │  │
│  │      └── Cohere (Embeddings, Classification)             │  │
│  ├──────────────────────────────────────────────────────────┤  │
│  │  Health Analysis Module                                  │  │
│  │  ├── Symptom Analyzer                                    │  │
│  │  ├── Risk Assessor                                       │  │
│  │  ├── Trend Detector                                      │  │
│  │  └── Recommendation Engine                               │  │
│  ├──────────────────────────────────────────────────────────┤  │
│  │  Testing Framework                                       │  │
│  │  ├── Adversarial Testing Suite                           │  │
│  │  ├── Load & Stress Testing                               │  │
│  │  ├── Accuracy Validation                                 │  │
│  │  └── Performance Benchmarking                            │  │
│  ├──────────────────────────────────────────────────────────┤  │
│  │  Feedback System                                         │  │
│  │  ├── Feedback Collectors                                 │  │
│  │  ├── Sentiment Analyzers                                 │  │
│  │  ├── Performance Trackers                                │  │
│  │  └── Improvement Pipeline                                │  │
│  └──────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
                               ↓↑
┌─────────────────────────────────────────────────────────────────┐
│                        DATA LAYER                                │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │  PostgreSQL (Relational Data)                            │  │
│  │  ├── User Profiles & Authentication                      │  │
│  │  ├── Patient Records & History                           │  │
│  │  ├── Consultation Logs                                   │  │
│  │  └── System Configuration                                │  │
│  ├──────────────────────────────────────────────────────────┤  │
│  │  Redis (Caching & Sessions)                              │  │
│  │  ├── Session Storage                                     │  │
│  │  ├── Response Caching                                    │  │
│  │  └── Rate Limiting                                       │  │
│  ├──────────────────────────────────────────────────────────┤  │
│  │  Vector Database (Medical Knowledge)                     │  │
│  │  ├── Embeddings of Medical Literature                    │  │
│  │  ├── Clinical Guidelines                                 │  │
│  │  └── Drug Information Database                           │  │
│  └──────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘

Component Breakdown

Frontend Layer (React + TypeScript)

The presentation layer is built with modern web technologies to deliver a responsive, intuitive user experience.

Core Technologies:

• React 18+: Component-based UI framework with hooks
• TypeScript: Type-safe development for reliability
• Vite: Lightning-fast build tool and dev server
• TailwindCSS: Utility-first CSS framework
• React Router: Client-side routing
• Axios: HTTP client for API communication
• React Query: Server state management
• Zustand/Redux: Client state management
• Chart.js/Recharts: Data visualization
• Framer Motion: Smooth animations

Key Features:

• Real-time chat interface for medical queries
• Interactive health dashboard with visualizations
• Patient medical history viewer
• Appointment scheduling interface
• Document upload and processing
• Responsive design (mobile-first approach)
• Progressive Web App (PWA) capabilities
• Dark mode support
• Accessibility (WCAG 2.1 AA compliant)

Backend Layer (Python + FastAPI)

The application layer handles business logic, request processing, and orchestration between frontend and AI services.

Core Technologies:

• FastAPI: Modern, high-performance Python web framework
• Pydantic: Data validation using Python type hints
• SQLAlchemy: SQL toolkit and ORM
• Alembic: Database migration tool
• Redis: Caching and session management
• Celery: Asynchronous task processing
• JWT: JSON Web Tokens for authentication
• Uvicorn: ASGI server for production

Architecture Patterns:

• RESTful API Design: Standard HTTP methods and status codes
• Repository Pattern: Data access abstraction
• Service Layer: Business logic separation
• Dependency Injection: Loose coupling and testability
• Middleware Chain: Request/response processing pipeline
• Error Handling: Consistent error responses
• Rate Limiting: Protection against abuse
• CORS Configuration: Cross-origin resource sharing

Key Responsibilities:

• User authentication and authorization
• Request validation and sanitization
• Business logic processing
• Database operations (CRUD)
• Integration with LLM service
• Response formatting and caching
• API documentation generation
• Monitoring and logging

LLM Layer (AI/ML Services)

The intelligence layer implements the core AI capabilities including RAG, health analysis, testing, and feedback processing.

RAG Implementation:

# Simplified RAG Architecture
┌─────────────────────────────────┐
│  1. Query Processing            │
│  - Text preprocessing           │
│  - Intent classification        │
│  - Entity extraction            │
└────────┬────────────────────────┘
         ↓
┌─────────────────────────────────┐
│  2. Embedding Generation        │
│  - Convert query to vector      │
│  - Embedding model selection    │
└────────┬────────────────────────┘
         ↓
┌─────────────────────────────────┐
│  3. Document Retrieval          │
│  - Vector similarity search     │
│  - Semantic ranking             │
│  - Top-K selection              │
└────────┬────────────────────────┘
         ↓
┌─────────────────────────────────┐
│  4. Context Assembly            │
│  - Combine retrieved docs       │
│  - Add conversation history     │
│  - Format for LLM               │
└────────┬────────────────────────┘
         ↓
┌─────────────────────────────────┐
│  5. LLM Generation              │
│  - Model selection              │
│  - Prompt construction          │
│  - Response generation          │
└────────┬────────────────────────┘
         ↓
┌─────────────────────────────────┐
│  6. Post-Processing             │
│  - Citation addition            │
│  - Safety filtering             │
│  - Response formatting          │
└─────────────────────────────────┘