System Requirements Document (SRD)

1. Introduction

This document outlines the requirements for developing a Retrieval-Augmented Generation (RAG) system with an automatic context memory feature. The RAG system is designed to enhance the retrieval of relevant information and generate responses based on user queries. The addition of automatic context memory will improve the system's ability to maintain and utilize contextual information across interactions.

2. System Overview

The RAG system is an AI-powered solution that combines information retrieval with generative AI capabilities. It is designed to provide accurate and contextually relevant responses to user queries by leveraging a combination of structured and unstructured data sources. The automatic context memory feature will enable the system to retain and recall contextual information from previous interactions, ensuring continuity and improving user experience.

3. Functional Requirements as Story Points

• As a User, I should be able to interact with the RAG system and receive responses that consider the context of previous queries.
• As a User, I should be able to reset or clear the context memory when needed.
• As an Admin, I should be able to configure the duration and scope of context retention for the system.
• As an Admin, I should be able to monitor and audit the context memory usage for compliance and optimization.
• As a Developer, I should be able to integrate the automatic context memory feature with existing RAG workflows seamlessly.
• As a Developer, I should be able to test and debug the context memory functionality effectively.

4. User Personas

4.1 User

• Primary users of the RAG system who interact with it to retrieve information and generate responses. They benefit from the automatic context memory feature for enhanced continuity in interactions.

4.2 Admin

• Responsible for managing and configuring the RAG system, including the automatic context memory feature. They ensure compliance, optimize system performance, and monitor usage.

4.3 Developer

• Technical personnel responsible for building, maintaining, and improving the RAG system. They implement and test the automatic context memory feature.

5. Visuals Colors and Theme

The RAG system will adopt a modern and professional theme with the following color palette:

• Primary Color: Deep Blue (#003366) for headers and key UI elements.
• Secondary Color: Light Gray (#F2F2F2) for backgrounds and secondary elements.
• Accent Color: Emerald Green (#50C878) for buttons and highlights.
• Text Color: Black (#000000) for readability.
• Error Color: Crimson Red (#DC143C) for alerts and error messages.

This theme ensures a clean, user-friendly interface suitable for professional and academic environments.

6. Non-Functional Requirements

• Performance: The system should respond to user queries within 2 seconds, including context memory retrieval.
• Scalability: The system should support up to 10,000 concurrent users without degradation in performance.
• Security: Context memory data must be encrypted both in transit and at rest to ensure user privacy.
• Reliability: The system should maintain 99.9% uptime and provide robust error handling mechanisms.
• Usability: The interface should be intuitive and accessible, adhering to WCAG 2.1 standards.
• Compliance: The system should comply with GDPR and other relevant data protection regulations.

7. Tech Stack

Frontend

• React for Web

Backend

• Python
• FastAPI

Database RDBMS

• MySQL, using Alembic for migrations

Database NoSQL

• MongoDB

Database VectorDB

• WeaviateDB

AI Models

• GPT 5.2 for user-friendly responses
• Claude 4.5 Opas for academic or coding work
• Gemini 3 Pro for friendly responses

AI Tools

• Litellm for LLM Routing
• Langchain

Local Orchestration

• Docker
• docker-compose

Server-Side Orchestration

• Kubernetes

8. Assumptions and Constraints

Assumptions

• Users will primarily interact with the system via a web interface.
• The automatic context memory feature will store contextual data for a configurable duration (e.g., session-based or time-based).
• The system will handle sensitive data and must comply with data protection regulations.

Constraints

• The system must operate within the limits of the chosen tech stack and infrastructure.
• Context memory storage size must be optimized to prevent excessive resource consumption.
• The system must ensure backward compatibility with existing RAG workflows.

9. Glossary

• RAG (Retrieval-Augmented Generation): A system that combines information retrieval with generative AI to provide contextually relevant responses.
• Context Memory: A feature that enables the system to retain and recall contextual information from previous interactions.
• LLM (Large Language Model): Advanced AI models designed to understand and generate human-like text.
• WCAG (Web Content Accessibility Guidelines): Standards for ensuring web accessibility for users with disabilities.
• GDPR (General Data Protection Regulation): European regulation for data protection and privacy.

This document provides a comprehensive overview of the updated requirements for the RAG system with the automatic context memory feature.