System Requirements Document (SRD)

Project Name: rapid-documents

1. Introduction

The rapid-documents project aims to create a production-ready Chat with Documents Assistant that enables users to upload a large collection of files and ask natural language questions to retrieve accurate information from them. This system will serve as a reliable knowledge assistant for document collections, providing clear, concise, and factually grounded answers based on the content of the uploaded documents.

This document outlines the system requirements, functional capabilities, and design considerations for the rapid-documents project. The system will be tailored to meet the needs of users in Iran (IR), with locale-specific defaults such as Persian calendar support, timezone (IRST), and currency (IRR) where applicable.

2. System Overview

The rapid-documents assistant will process structured and unstructured data from various document types, including text, tables, lists, and spreadsheets. Users will interact with the system through a conversational interface to retrieve information, compare data, and explore document content.

The system will support multi-turn conversations, multi-document reasoning, and source awareness, ensuring users can verify answers and explore original sources. It will also handle large document repositories, providing fast and accurate retrieval of relevant information.

Key features include:

• Document understanding and reasoning
• Natural language question answering
• Multi-document and multi-turn conversation support
• Source referencing for transparency
• User-friendly interface for seamless interaction

3. Functional Requirements

• As a User, I should be able to upload multiple documents in various formats (e.g., PDF, Word, Excel).
• As a User, I should be able to ask natural language questions about the uploaded documents.
• As a User, I should be able to receive clear and concise answers based on the document content.
• As a User, I should be able to view referenced sources for each answer.
• As a User, I should be able to ask follow-up questions in a multi-turn conversation.
• As a User, I should be able to search for documents related to a specific topic.
• As a User, I should be able to identify which document contains certain information.
• As a User, I should be able to view structured data (e.g., tables) in a readable format.
• As a User, I should be able to compare data across multiple documents.
• As a User, I should be able to summarize document content.

4. User Personas

1. General User

   • Primary user of the system.
   • Uploads documents and interacts with the assistant to retrieve information.
   • Needs clear, concise, and accurate answers.

2. Admin

   • Manages system configurations and user access.
   • Monitors system performance and ensures data security.

3. Guest

   • Limited access to explore system capabilities (e.g., demo mode).
   • Cannot upload documents or access sensitive data.

5. Visuals Colors and Theme

The visual theme for rapid-documents will focus on professionalism and clarity, with a modern and intuitive design.

• Primary Colors:

  • Deep Blue (#003366) for trust and reliability.
  • White (#FFFFFF) for clarity and simplicity.
  • Light Gray (#F5F5F5) for backgrounds and subtle contrasts.

• Accent Colors:

  • Emerald Green (#2ECC71) for success messages and highlights.
  • Persian Red (#D32F2F) for errors and warnings.

• Typography:

  • Sans-serif fonts like Roboto or Open Sans for readability.

• Icons and Graphics:

  • Minimalistic and clean, with a focus on document-related imagery.

6. Signature Design Concept

Interactive Floating Document Library

The homepage of rapid-documents will feature an interactive floating document library. Users will see a 3D carousel of documents that they can rotate, zoom into, and select. Each document will appear as a floating card with a preview of its content.

When users hover over a document, it will expand slightly, showing key metadata (e.g., title, date, and type). Clicking on a document will open a detailed view, where users can explore its content or ask questions directly.

The background will feature a subtle animation of flowing paper sheets, creating a dynamic and engaging experience. Transitions between sections will include smooth scrolling and fade effects, ensuring a seamless user journey.

This design will make the system feel modern, intuitive, and visually captivating, leaving a lasting impression on users.

7. Non-Functional Requirements

• Performance: The system should retrieve answers within 2 seconds for queries involving up to 1,000 documents.
• Scalability: The system should support repositories containing up to 10,000 documents without significant performance degradation.
• Security: All uploaded documents and user interactions must be encrypted using industry-standard protocols.
• Localization: The system should support Persian language and IR-specific date/time formats.
• Availability: The system should maintain 99.9% uptime.

8. Tech Stack

• Frontend:

  • React for web interface.

• Backend:

  • Python with FastAPI for API development.

• Database:

  • MySQL for structured data storage.
  • WeaviateDB for vector-based document search.

• AI Models:

  • GPT 5.2 for user-friendly responses.
  • Claude 4.5 Opas for academic or coding-related queries.
  • Google Nano Banana for image generation (if needed).

• AI Tools:

  • Litellm for LLM routing.
  • Langchain for conversational AI workflows.

• Orchestration:

  • Docker and docker-compose for local development.
  • Kubernetes for server-side orchestration.

9. Assumptions and Constraints

• Users will primarily interact with the system in Persian or English.
• The system will operate within the IR timezone (IRST).
• Uploaded documents will not exceed 100 MB per file.
• The system will not process documents containing highly sensitive or classified information.

10. Glossary

• Document Understanding: The ability to extract and interpret content from various document formats.
• Multi-Turn Conversation: A dialogue where the system remembers previous interactions to provide contextually relevant responses.
• Vector-Based Search: A method of searching documents using embeddings to find semantically similar content.
• LLM (Large Language Model): Advanced AI models trained on vast datasets to understand and generate human-like text.

This concludes the updated System Requirements Document for rapid-documents.