RFP RAG System - Development Package

This repository contains the complete specification and development package for the RFP RAG (Retrieval-Augmented Generation) system.

📁 Package Contents

1. user-stories.md

Complete set of user stories covering:

• Upload & Document Management (US-001 to US-004)
• Retrieval & Query functionality (US-005 to US-008)
• Acceptance criteria for each story

2. functional-specification.md

Detailed functional requirements including:

• System overview
• Data schemas (RFP and Document metadata)
• Upload and retrieval workflows
• Non-functional requirements (performance, security, usability)
• Integration requirements
• Constraints and assumptions

3. api-specification.yaml

OpenAPI 3.0 specification with:

• Complete REST API endpoints
• Request/response schemas
• Authentication requirements
• Error handling specifications
• Can be imported into Swagger UI or Postman

4. ui-wireframes.md

UI/UX specifications including:

• Application layout
• Upload page designs
• Column mapping interface
• Search/query interface
• Component specifications
• Responsive design guidelines

5. project-implementation-plan.md

Comprehensive project plan with:

• 10-week implementation timeline
• Technical architecture
• Resource requirements
• Risk management matrix
• Deployment strategy
• Budget estimates

6. sample-api-payloads.json

Example API requests and responses for:

• Document upload
• Column mapping
• RAG queries
• Status checking
• Error scenarios

7. data-models.py

Python/Pydantic data models for:

• Request/response models
• Database schemas
• Enums and validators
• Ready for FastAPI implementation

🚀 Quick Start for Development Team

1. Review Documentation

   • Start with user-stories.md to understand requirements
   • Read functional-specification.md for detailed specs
   • Check ui-wireframes.md for UI requirements

2. Set Up Development

   • Import api-specification.yaml into your API development tool
   • Use data-models.py as the foundation for backend models
   • Reference sample-api-payloads.json for testing

3. Follow Implementation Plan

   • Refer to project-implementation-plan.md for timeline
   • Use the phased approach outlined in the plan
   • Track progress against defined milestones

🛠 Technology Stack

• Backend: Python/FastAPI
• Frontend: React with TypeScript
• Database: PostgreSQL + Vector DB (Pinecone/Weaviate)
• Storage: S3-compatible object storage
• Infrastructure: Docker/Kubernetes

📊 Key Features

1. Document Upload

   • Support for Excel, PDF, and DOCX files
   • Intelligent column mapping for RFPs
   • Metadata tagging for documents

2. Semantic Search

   • Natural language queries
   • Vector-based similarity matching
   • Relevance scoring

3. Result Management

   • Categorized responses (Readily Available, Configuration, etc.)
   • Effort estimation
   • Historical remarks tracking

📝 Next Steps

1. Technical Setup

   • Configure development environment
   • Set up CI/CD pipeline
   • Initialize databases

2. Development Kickoff

   • Team assignments based on resource plan
   • Sprint planning using user stories
   • Architecture review meeting

3. Stakeholder Communication

   • Share project plan with sponsors
   • Schedule regular demo sessions
   • Set up feedback channels

📞 Contact

For questions about this specification package:

• Technical Lead: [To be assigned]
• Project Manager: [To be assigned]
• Product Owner: Ravi Kant

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Version: 1.0
Created: July 11, 2025
iStatus*: Ready for Development