Backend Implementation

This document provides a detailed technical overview of Qwello's backend implementation, focusing on the NestJS architecture, module structure, API endpoints, database integration, and job queue system.

Backend Architecture Overview

Distributed System Design

The backend implements a distributed microservices architecture that separates concerns and enables independent scaling of different system components:

Core Architecture Components

Backend Architecture:
├── API Gateway Layer (NestJS)
│   ├── Authentication & Authorization Services
│   ├── User Management & Profile Services
│   ├── Document Management Services
│   ├── Chat & Interaction Services
│   └── Search & Discovery Services
├── Processing Layer (Go Consumers)
│   ├── Document Upload Processors
│   ├── Knowledge Graph Generators
│   ├── Report Generation Services
│   └── Queue Management Systems
├── AI Integration Layer
│   ├── Vision AI Service Connectors
│   ├── Language AI Service Connectors
│   ├── Model Selection & Routing
│   └── Fallback & Error Handling
├── Data Storage Layer
│   ├── MongoDB (Knowledge Graphs & Metadata)
│   ├── AWS S3 (Document & Asset Storage)
│   ├── Redis (Caching & Queue Management)
│   └── Search Indices (Query Optimization)
└── Infrastructure Services
    ├── Monitoring & Observability
    ├── Security & Compliance
    ├── Configuration Management
    └── Deployment & Orchestration

Technology Stack

Primary Technologies

• NestJS Framework: TypeScript-based Node.js framework for API services

• Go Language: High-performance language for intensive processing tasks

• MongoDB: Document database for flexible data storage

• Redis: In-memory data store for caching and queue management

• AWS S3: Object storage for documents and generated assets

Supporting Technologies

• BullMQ: Redis-based job queue for distributed task processing

• Mongoose: MongoDB object modeling for Node.js

• JWT: JSON Web Tokens for secure authentication

• Prometheus: Metrics collection and monitoring

• Docker: Containerization for consistent deployments

API Gateway Layer (NestJS)

Service Architecture

The NestJS API gateway provides a unified interface for all client interactions while orchestrating backend services:

Module Organization

Authentication Module:

• Multi-provider authentication support (JWT, OAuth, Auth0)

• Session management and token validation

• Role-based access control and permissions

• Security middleware and guards

User Management Module:

• User profile management and preferences

• Account settings and configuration

• Usage tracking and analytics

• Subscription and billing integration

Document Management Module:

• PDF upload and validation services

• Document metadata management

• File organization and categorization

• Access control and sharing permissions

Chat Module:

• Conversational interface for document interaction

• Natural language query processing

• Chat history and session management

• Real-time communication via WebSockets

Search Module:

• Advanced search capabilities across built KG

AI Integration Module:

• AI service provider abstraction

• Model selection and routing logic

• Request/response transformation

• Error handling and fallback mechanisms

API Design Principles

RESTful Architecture

• Resource-Based URLs: Clear, intuitive URL structure

• HTTP Methods: Proper use of GET, POST, PUT, DELETE methods

• Status Codes: Appropriate HTTP status codes for different scenarios

• Content Negotiation: Support for multiple content types

• Versioning: API versioning for backward compatibility

Security Implementation

• Authentication: JWT-based authentication with refresh tokens

• Authorization: Role-based access control (RBAC)

• Input Validation: Comprehensive input validation and sanitization

• Rate Limiting: Request rate limiting to prevent abuse

• CORS: Cross-Origin Resource Sharing configuration

Performance Optimization

• Caching: Multi-level caching strategy for improved response times

• Compression: Response compression for reduced bandwidth usage

• Connection Pooling: Database connection pooling for efficiency

• Async Processing: Asynchronous processing for non-blocking operations

• Load Balancing: Support for horizontal scaling and load distribution

Processing Layer (Go Consumers)

Consumer Architecture

The Go-based processing layer handles computationally intensive tasks through distributed consumers:

Consumer Types

PDF Upload Consumer:

• Initial document processing and validation

• File format verification and optimization

• Metadata extraction and storage

• Queue job creation for downstream processing

Knowledge Graph Consumer:

• AI-powered content analysis and extraction

• Entity recognition and relationship mapping

• Graph construction and optimization

• Result storage and indexing

Report Consumer:

• Summary generation and insight extraction

• Report formatting and presentation

• Export generation in multiple formats

• Notification and completion handling

Processing Capabilities

Concurrent Processing

• Worker Pool Management: Dynamic worker scaling based on load

• Parallel Processing: Simultaneous processing of multiple documents

• Resource Management: Efficient CPU and memory utilization

• Load Balancing: Intelligent distribution of processing tasks

Error Handling and Recovery

• Retry Mechanisms: Automatic retry with exponential backoff

• Dead Letter Queues: Handling of failed processing jobs

• Circuit Breakers: Protection against cascading failures

• Health Monitoring: Continuous health checks and alerting

Performance Optimization

• Memory Management: Efficient memory usage for large documents

• Garbage Collection: Optimized garbage collection strategies

• Connection Pooling: Database and service connection pooling

• Batch Processing: Batch operations for improved efficiency

AI Integration Architecture

Multi-Provider Integration

The system integrates with multiple AI service providers for redundancy and optimization:

Provider Abstraction Layer

• Unified Interface: Common interface for all AI providers

• Provider Selection: Intelligent selection based on requirements

• Load Balancing: Distribution of requests across providers

• Failover Handling: Automatic failover to backup providers

Model Management

• Model Registry: Centralized registry of available models

• Capability Mapping: Mapping of models to specific capabilities

• Performance Tracking: Monitoring of model performance and accuracy

• Version Management: Handling of model updates and versioning

Request Processing

• Request Routing: Intelligent routing based on content and requirements

• Batch Processing: Batching of requests for efficiency

• Response Caching: Caching of AI responses for repeated queries

• Quality Assurance: Validation and quality checking of AI outputs

AI Service Integration

Vision AI Services

• Document Analysis: Layout understanding and structure recognition

• Text Extraction: OCR and text recognition capabilities

• Image Processing: Analysis of charts, diagrams, and visual elements

• Quality Enhancement: Image preprocessing and optimization

Language AI Services

• Entity Recognition: Identification and classification of entities

• Relationship Extraction: Discovery of entity relationships

• Text Understanding: Semantic analysis and comprehension

• Query Processing: Natural language query understanding and response

Data Storage Architecture

Database Design

MongoDB Implementation

• Document-Oriented Storage: Flexible schema for varied data types

• Collection Organization: Logical organization of data collections

• Indexing Strategy: Optimized indexing for query performance

• Replication: Data replication for high availability

• Sharding: Horizontal scaling for large datasets

Data Models

• User Data: User profiles, preferences, and authentication information

• Document Metadata: Document properties, processing status, and organization

• Knowledge Graphs: Entity and relationship data with attributes

• Chat Data: Conversation history and interaction logs

• Analytics Data: Usage statistics and performance metrics

Storage Services

AWS S3 Integration

• Document Storage: Secure storage of original PDF documents

• Asset Management: Storage of processed images and generated assets

• Backup and Archival: Long-term storage and backup strategies

• Access Control: Fine-grained access control and permissions

• Content Delivery: Integration with CDN for global distribution

Redis Implementation

• Caching Layer: High-performance caching for frequently accessed data

• Session Storage: User session data and temporary storage

• Queue Management: Job queue storage and management

• Real-Time Data: Storage of real-time metrics and counters

• Pub/Sub: Message publishing and subscription for real-time features

Queue Management System

Distributed Queue Architecture

BullMQ Implementation

• Job Queues: Multiple specialized queues for different processing types

• Priority Handling: Priority-based job processing

• Delayed Jobs: Scheduling of jobs for future execution

• Job Retry: Automatic retry of failed jobs with backoff strategies

• Progress Tracking: Real-time progress tracking and reporting

Queue Types

• Upload Queue: Document upload and initial processing

• Processing Queue: AI-powered document analysis

• Report Queue: Summary and report generation

• Notification Queue: User notifications and alerts

• Maintenance Queue: System maintenance and cleanup tasks

Performance Optimization

• Concurrency Control: Optimal concurrency settings for different queue types

• Resource Allocation: Dynamic resource allocation based on queue load

• Monitoring: Comprehensive monitoring of queue performance

• Scaling: Automatic scaling of queue workers based on demand

Security Implementation

Authentication and Authorization

Multi-Provider Authentication

• JWT Tokens: Secure token-based authentication

• OAuth Integration: Support for OAuth providers (Google, GitHub, etc.)

• Auth0 Integration: Enterprise authentication service integration

• Multi-Factor Authentication: Support for 2FA and MFA

• Session Management: Secure session handling and timeout policies

Access Control

• Role-Based Access Control: Granular permissions based on user roles

• Resource-Level Permissions: Fine-grained access control for resources

• API Key Management: Secure API key generation and management

• Audit Logging: Comprehensive logging of access and operations

Data Security

Encryption

• Data at Rest: Encryption of stored data in databases and file systems

• Data in Transit: TLS/SSL encryption for all network communications

• Key Management: Secure key generation, storage, and rotation

• Certificate Management: SSL certificate management and renewal

Privacy Protection

• Data Anonymization: Anonymization of sensitive user data

• Consent Management: User consent tracking and management

• Data Retention: Automated data retention and deletion policies

Monitoring and Observability

Metrics Collection

Application Metrics

• Performance Metrics: Response times, throughput, and error rates

• Business Metrics: User engagement, document processing statistics

• Resource Metrics: CPU, memory, and storage utilization

• Custom Metrics: Domain-specific metrics and KPIs

Infrastructure Monitoring

• System Health: Server health and availability monitoring

• Database Performance: Database query performance and optimization

• Queue Monitoring: Queue depth, processing times, and worker status

• Network Monitoring: Network latency, bandwidth, and connectivity

Logging and Tracing

Centralized Logging

• Structured Logging: JSON-formatted logs for easy parsing and analysis

• Log Aggregation: Centralized collection of logs from all services

• Log Retention: Configurable log retention policies

• Search and Analysis: Full-text search and analysis of log data

Distributed Tracing

• Request Tracing: End-to-end tracing of requests across services

• Performance Analysis: Identification of performance bottlenecks

• Error Tracking: Comprehensive error tracking and analysis

• Dependency Mapping: Visualization of service dependencies

Alerting and Notifications

Alert Management

• Threshold-Based Alerts: Alerts based on metric thresholds

• Anomaly Detection: Machine learning-based anomaly detection

• Alert Routing: Intelligent routing of alerts to appropriate teams

• Escalation Policies: Automated escalation for critical issues

Notification Channels

• Email Notifications: Email alerts for important events

Scalability and Performance

Horizontal Scaling

Service Scaling

• Microservice Architecture: Independent scaling of individual services

• Container Orchestration: Kubernetes-based container orchestration

• Auto-Scaling: Automatic scaling based on load and performance metrics

• Load Balancing: Intelligent load distribution across service instances

Database Scaling

• Read Replicas: Read replica scaling for improved read performance

• Sharding: Horizontal partitioning of data across multiple databases

• Connection Pooling: Efficient database connection management

• Query Optimization: Continuous query performance optimization

Performance Optimization

Caching Strategy

• Multi-Level Caching: Application, database, and CDN caching

• Cache Invalidation: Intelligent cache invalidation strategies

• Cache Warming: Proactive cache warming for improved performance

• Cache Monitoring: Monitoring of cache hit rates and performance

Resource Optimization

• Memory Management: Efficient memory usage and garbage collection

• CPU Optimization: CPU-intensive task optimization

• I/O Optimization: Disk and network I/O optimization

• Resource Pooling: Connection and resource pooling for efficiency

This backend implementation provides a robust, scalable foundation that can handle the complex requirements of document processing and knowledge graph generation while maintaining high performance, security, and reliability standards.