cim_summary/.kiro/specs/codebase-cleanup-and-upload-fix/requirements.md

Requirements Document

Introduction

The CIM Document Processor is experiencing backend processing failures that prevent the full document processing pipeline from working correctly. The system has a complex architecture with multiple services (Document AI, LLM processing, PDF generation, vector database, etc.) that need to be cleaned up and properly integrated to ensure reliable document processing from upload through final PDF generation.

Requirements

Requirement 1

User Story: As a developer, I want a clean and properly functioning backend codebase, so that I can reliably process CIM documents without errors.

Acceptance Criteria

1. WHEN the backend starts THEN all services SHALL initialize without errors
2. WHEN environment variables are loaded THEN all required configuration SHALL be validated and available
3. WHEN database connections are established THEN all database operations SHALL work correctly
4. WHEN external service integrations are tested THEN Google Document AI, Claude AI, and Firebase Storage SHALL be properly connected

Requirement 2

User Story: As a user, I want to upload PDF documents successfully, so that I can process CIM documents for analysis.

Acceptance Criteria

1. WHEN a user uploads a PDF file THEN the file SHALL be stored in Firebase storage
2. WHEN upload is confirmed THEN a processing job SHALL be created in the database
3. WHEN upload fails THEN the user SHALL receive clear error messages
4. WHEN upload monitoring is active THEN real-time progress SHALL be tracked and displayed

Requirement 3

User Story: As a user, I want the document processing pipeline to work end-to-end, so that I can get structured CIM analysis results.

Acceptance Criteria

1. WHEN a document is uploaded THEN Google Document AI SHALL extract text successfully
2. WHEN text is extracted THEN the optimized agentic RAG processor SHALL chunk and process the content
3. WHEN chunks are processed THEN vector embeddings SHALL be generated and stored
4. WHEN LLM analysis is triggered THEN Claude AI SHALL generate structured CIM review data
5. WHEN analysis is complete THEN a PDF summary SHALL be generated using Puppeteer
6. WHEN processing fails at any step THEN error handling SHALL provide graceful degradation

Requirement 4

User Story: As a developer, I want proper error handling and logging throughout the system, so that I can diagnose and fix issues quickly.

Acceptance Criteria

1. WHEN errors occur THEN they SHALL be logged with correlation IDs for tracking
2. WHEN API calls fail THEN retry logic SHALL be implemented with exponential backoff
3. WHEN processing fails THEN partial results SHALL be preserved where possible
4. WHEN system health is checked THEN monitoring endpoints SHALL provide accurate status information

Requirement 5

User Story: As a user, I want the frontend to properly communicate with the backend, so that I can see processing status and results in real-time.

Acceptance Criteria

1. WHEN frontend makes API calls THEN authentication SHALL work correctly
2. WHEN processing is in progress THEN real-time status updates SHALL be displayed
3. WHEN processing is complete THEN results SHALL be downloadable
4. WHEN errors occur THEN user-friendly error messages SHALL be shown

Requirement 6

User Story: As a developer, I want clean service dependencies and proper separation of concerns, so that the codebase is maintainable and testable.

Acceptance Criteria

1. WHEN services are initialized THEN dependencies SHALL be properly injected
2. WHEN business logic is executed THEN it SHALL be separated from API routing
3. WHEN database operations are performed THEN they SHALL use proper connection pooling
4. WHEN external APIs are called THEN they SHALL have proper rate limiting and error handling