User Stories for Document AI Microservices Implementation

Document AI Invoice File Intake Service

Service Name: docai_invoice_file_intake_service

Called By: fluxx-file-intake microservice

Story 1: Database Integration

As a developer

I want to replace the mock database implementation with actual SQL Server connections

So that the Document AI Invoice File Intake Service can persist documents to the production database

Acceptance Criteria:

Replace MockFileIntakeDatabaseService with SQL Server implementation
Implement connection pooling and proper error handling
Support the actual Documents table schema with proper foreign key relationships
Handle database transactions for document creation and updates
Implement proper logging for database operations

Technical Tasks:

Install and configure SQL Server driver (e.g., mssql or tedious)
Create SqlServerFileIntakeDatabaseService implementing IFileIntakeDatabaseService
Update service factory to use SQL Server implementation
Add connection string configuration management
Implement database connection health checks

Story 2: Azure Blob Storage Integration

As a developer

I want to integrate with Azure Blob Storage for PDF file operations

So that the service can validate and process PDF documents from blob storage

Acceptance Criteria:

Integrate Azure Blob Storage client for PDF file access
Implement PDF file validation and metadata extraction
Support blob path validation and file existence checks
Handle Azure storage authentication and error scenarios
Implement blob content hashing for duplicate detection

Technical Tasks:

Install and configure Azure Storage SDK (@azure/storage-blob)
Create blob storage utility functions for PDF operations
Implement PDF file validation logic
Add Azure storage configuration to service initialization
Create error handling for blob storage operations

Story 3: Client Configuration and Testing

As a developer

I want comprehensive testing and client configuration validation

So that the Document AI Invoice File Intake Service is production-ready with proper quality assurance

Acceptance Criteria:

Create comprehensive unit tests for all service methods
Implement integration tests with actual database and blob storage
Add client configuration validation using actual config schema
Implement proper error handling and logging throughout the service
Create performance benchmarks and monitoring capabilities

Technical Tasks:

Set up Jest/Vitest testing framework with appropriate mocks
Create unit tests for each service method with edge cases
Implement integration tests with test database
Add client configuration validation logic using provided schema
Implement structured logging with correlation IDs
Create performance monitoring and metrics collection

Document AI Invoice Extraction Service

Service Name: docai_invoice_extraction_service

Called By: fluxx-extraction-service microservice

Story 1: Azure Services Integration

As a developer

I want to integrate with Azure Document Intelligence and OpenAI APIs

So that the Document AI Invoice Extraction Service can perform OCR and AI-based data extraction

Acceptance Criteria:

Integrate Azure Document Intelligence for PDF OCR and bounding region extraction
Implement OpenAI API integration using client-specific configurations
Support multiple AI models and prompts based on client configuration
Handle API rate limiting, retries, and error scenarios
Implement confidence scoring and extraction validation

Technical Tasks:

Install Azure Document Intelligence SDK (@azure/ai-form-recognizer)
Install OpenAI SDK (openai)
Implement Document Intelligence client with proper authentication
Create OpenAI client with client-specific prompt handling
Implement retry logic and rate limiting for both services
Add API response validation and error handling

Story 2: PDF Processing and Image Generation

As a developer

I want to implement PDF to image conversion and processing

So that the service can generate images for OCR and AI analysis

Acceptance Criteria:

Convert PDF pages to high-quality images suitable for OCR
Support multi-page PDF processing with proper page sequencing
Implement image optimization for AI processing
Store generated images in Azure Blob Storage with proper naming
Handle PDF processing errors and edge cases (corrupted files, large files)

Technical Tasks:

Install PDF processing library (pdf2pic or pdf-poppler)
Implement PDF to image conversion with quality optimization
Create image storage utilities for Azure Blob Storage
Add support for multi-page PDF processing
Implement proper cleanup of temporary files and images
Add error handling for PDF corruption and processing failures

Story 3: Database Integration and Freight Processing

As a developer

I want to integrate with SQL Server and implement freight pattern processing

So that the service can persist extracted data and apply client-specific business rules

Acceptance Criteria:

Replace mock database with actual SQL Server implementation for ExtractedEntities table
Implement freight pattern matching using client configuration
Support complex invoice data transformation and validation
Handle database transactions for extraction results
Implement post-processing business logic for freight, taxes, and line items

Technical Tasks:

Create SqlServerExtractionDatabaseService implementing IExtractionDatabaseService
Implement freight pattern matching algorithm using regex patterns from client config
Add invoice data transformation and validation logic
Create complex JSON payload handling for ExtractedEntitiesPayload column
Implement database transaction management for extraction operations
Add business logic for freight calculations and tax processing

Story 4: Testing and Production Readiness

As a developer

I want comprehensive testing and monitoring for the Document AI Invoice Extraction Service

So that the service is reliable and maintainable in production

Acceptance Criteria:

Create unit tests for all extraction pipeline methods
Implement integration tests with actual Azure services (using test accounts)
Add performance testing for large PDF processing
Implement comprehensive error handling and logging
Create monitoring and alerting for extraction failures and performance

Technical Tasks:

Set up testing framework with Azure service mocks
Create unit tests for AI extraction, PDF processing, and database operations
Implement integration tests with test Azure accounts
Add performance testing for various PDF sizes and complexity
Implement structured logging with extraction correlation IDs
Create health checks and monitoring endpoints
Add alerting for extraction failures and performance degradation

Cross-Service Stories

Story 1: Service Communication and Integration

As a developer

I want proper communication between Document AI services

So that the services work together seamlessly in the Fluxx microservice architecture

Acceptance Criteria:

Define clear API contracts between services
Implement proper service discovery and communication
Handle service failures and retry logic
Support asynchronous processing with proper status tracking
Implement end-to-end tracing and monitoring

Technical Tasks:

Define service API interfaces and communication protocols
Implement HTTP/REST APIs or message queue integration
Add service health checks and discovery mechanisms
Create retry and circuit breaker patterns for service communication
Implement distributed tracing across services
Add end-to-end monitoring and alerting

Story 2: Configuration Management and Deployment

As a developer

I want proper configuration management and deployment pipelines

So that both Document AI services can be deployed and managed efficiently in production

Acceptance Criteria:

Implement environment-based configuration management
Create Docker containers for both services
Set up CI/CD pipelines for automated deployment
Implement proper secret management for API keys and connection strings
Support blue-green or canary deployment strategies

Technical Tasks:

Create environment-specific configuration files
Implement configuration validation and loading
Create Dockerfiles and docker-compose configurations
Set up CI/CD pipelines (GitHub Actions/Azure DevOps)
Integrate Azure Key Vault for secret management
Implement deployment automation and rollback procedures

Story 3: Monitoring and Observability

As a developer

I want comprehensive monitoring and observability across both Document AI services

So that we can track performance, debug issues, and ensure service health

Acceptance Criteria:

Implement structured logging with correlation IDs
Create performance metrics and dashboards
Set up alerting for critical failures and performance degradation
Implement health checks and service status monitoring
Support distributed tracing across the entire processing pipeline

Technical Tasks:

Integrate logging framework (Winston, Pino) with structured output
Set up metrics collection (Prometheus, Application Insights)
Create monitoring dashboards (Grafana, Azure Monitor)
Implement alerting rules and notification channels
Add health check endpoints for both services
Configure distributed tracing (Jaeger, Application Insights)

Priority Matrix

High Priority (MVP)

Document AI Invoice File Intake Service - Database Integration (Story 1)
Document AI Invoice Extraction Service - Azure Services Integration (Story 1)
Document AI Invoice Extraction Service - Database Integration (Story 3)

Medium Priority (Production Ready)

Document AI Invoice File Intake Service - Azure Blob Storage Integration (Story 2)
Document AI Invoice Extraction Service - PDF Processing (Story 2)
Cross-Service Communication (Story 1)

Lower Priority (Operational Excellence)

Document AI Invoice File Intake Service - Testing (Story 3)
Document AI Invoice Extraction Service - Testing (Story 4)
Configuration Management (Story 2)
Monitoring and Observability (Story 3)

Estimated Effort

| Story | Complexity | Estimated Story Points |

|-------|------------|----------------------|

| DocAI File Intake - Database Integration | Medium | 5 |

| DocAI File Intake - Blob Storage Integration | Medium | 5 |

| DocAI File Intake - Testing & Config | High | 8 |

| DocAI Extraction - Azure Services Integration | High | 13 |

| DocAI Extraction - PDF Processing | Medium | 8 |

| DocAI Extraction - Database & Freight | High | 13 |

| DocAI Extraction - Testing | High | 8 |

| Cross-Service Communication | Medium | 5 |

| Configuration Management | Medium | 5 |

| Monitoring & Observability | High | 8 |

Total Estimated Effort: 78 Story Points