System Requirements Document (SRD)

1. Introduction

This document outlines the system requirements for a production-level Retrieval-Augmented Generation (RAG) software. The RAG system is designed to enhance information retrieval and generation capabilities by combining retrieval mechanisms with generative AI models. The document includes functional and non-functional requirements, system architecture details, reranking formulas, and other necessary specifications.

2. System Overview

The RAG software will serve as a robust solution for retrieving relevant information from large datasets and generating coherent and contextually accurate responses. It will leverage advanced AI models, a scalable backend, and efficient database systems to ensure high performance and reliability. The system will incorporate reranking mechanisms to optimize the relevance of retrieved data before passing it to generative models.

Key features of the system:

• Retrieval of relevant documents using advanced search algorithms.
• Integration of generative AI models for response generation.
• Reranking formulas to prioritize the most relevant retrieved data.
• Scalable architecture for production-level deployment.

3. Functional Requirements as Story Points

• As User I should be able to input a query and receive a contextually accurate response.
• As Admin I should be able to monitor system performance and manage data sources.
• As User I should be able to view the sources of retrieved information for transparency.
• As Admin I should be able to update and maintain the reranking formulas.
• As User I should be able to interact with the system via a web or mobile interface.
• As Admin I should be able to configure AI model parameters for optimal performance.
• As User I should be able to retrieve information from structured and unstructured datasets.
• As Admin I should be able to scale the system to handle increased traffic and data volume.

4. User Personas

1. User

• Primary user of the system.
• Inputs queries and consumes generated responses.
• Requires transparency and accuracy in retrieved information.

2. Admin

• Responsible for system maintenance and optimization.
• Monitors performance metrics and manages data sources.
• Configures AI models and reranking formulas.

5. Visuals Colors and Theme

The RAG software will adopt a professional and modern theme:

• Primary Color: Deep Blue (#003366) for trust and reliability.
• Secondary Color: Light Gray (#F5F5F5) for a clean interface.
• Accent Color: Emerald Green (#50C878) for actionable elements like buttons.
• Font: Sans-serif fonts such as Open Sans or Roboto for readability.
• Icons: Minimalistic and intuitive icons for navigation.

6. Non-Functional Requirements

• Scalability: The system must handle up to 10,000 concurrent users and process large datasets efficiently.
• Reliability: Ensure 99.9% uptime for production-level deployment.
• Performance: Responses should be generated within 2 seconds for typical queries.
• Security: Implement robust authentication and encryption mechanisms to protect user data.
• Maintainability: The system should allow easy updates to reranking formulas and AI models.
• Usability: The interface should be intuitive and user-friendly for both Users and Admins.

7. Tech Stack

Frontend

• React for Web
• React Native for Mobile App

Backend

• Python
• FastAPI

Database RDBMS

• MySQL or MariaDB (using Alembic for migrations)

Database NoSQL

• MongoDB

AI Models

• GPT 5.2 for user-friendly response generation.
• Claude 4.5 Opas for academic or coding-related queries.
• Gemini 3 Pro for friendly and conversational responses.

AI Tools

• Litellm for LLM routing.
• Langchain for chaining AI model interactions.

Local Orchestration

• Docker
• Docker Compose

Server-Side Orchestration

• Kubernetes

8. System Architecture

The architecture for the RAG software is designed to ensure scalability, reliability, and efficiency. Below is an overview of the system components:

1. Query Processing Layer

• Accepts user queries via web or mobile interfaces.
• Preprocesses queries to remove noise and extract key terms.

2. Retrieval Layer

• Uses vector search algorithms to retrieve relevant documents from structured and unstructured datasets.
• Integrates WeaviateDB for vector-based retrieval.

3. Reranking Layer

• Applies reranking formulas to prioritize the most relevant documents.
• Reranking formulas include:
  • BM25 Scoring: Traditional term frequency-based scoring.
  • Semantic Similarity: Uses embeddings from AI models to calculate similarity scores.
  • Hybrid Scoring: Combines BM25 and semantic similarity scores for optimal ranking.

4. Generative Layer

• Passes the top-ranked documents to generative AI models (e.g., GPT 5.2, Claude 4.5 Opas).
• Generates responses based on retrieved context.

5. Output Layer

• Formats the generated response and displays it to the user.
• Includes source links for transparency.

6. Admin Dashboard

• Provides tools for monitoring system performance and managing data sources.
• Allows configuration of reranking formulas and AI model parameters.

7. Orchestration and Deployment

• Uses Docker and Kubernetes for containerization and orchestration.
• Ensures high availability and scalability.

9. Assumptions and Constraints

Assumptions

• Users will primarily interact with the system via web or mobile interfaces.
• The system will handle queries in English initially, with potential for multilingual support in the future.
• Data sources will include both structured (e.g., databases) and unstructured (e.g., text files) formats.

Constraints

• The system must comply with data privacy regulations such as GDPR.
• AI model updates may require downtime for retraining and deployment.
• Limited budget for initial deployment may restrict the use of certain advanced tools.

10. Glossary

• RAG: Retrieval-Augmented Generation, a system combining retrieval mechanisms with generative AI models.
• BM25: A ranking function used for information retrieval based on term frequency.
• Semantic Similarity: A measure of how similar two pieces of text are in meaning.
• Vector Search: A method of searching using vector embeddings to find semantically similar items.
• Generative AI Models: AI systems capable of generating text based on input context.
• Docker: A platform for containerizing applications.
• Kubernetes: A system for automating deployment, scaling, and management of containerized applications.

This updated SRD includes the requested system architecture details and reranking formulas for production-level RAG software.