DB-chat System Requirements Document

1. Introduction

DB-chat is a micro SaaS project designed to provide teams with natural language access to their internal data. The goal is to enable users to interact with their databases using natural language queries without the need for custom dashboards. This document outlines the system requirements for the development of DB-chat.

2. System Overview

DB-chat aims to simplify the way teams interact with their databases by allowing natural language queries. This system will leverage AI models to interpret user queries and return relevant data from the database. The service will be accessible via an API, making it easy to integrate with existing systems.

3. Functional Requirements as Story Points

• As a User, I should be able to input natural language queries to access database information.
• As a Developer, I should be able to integrate the DB-chat API with existing systems to enable natural language queries.
• As an Admin, I should be able to manage user access and permissions for querying the database.
• As a User, I should receive accurate and relevant data in response to my natural language queries.

4. User Personas

• User: Individuals who interact with the database using natural language queries.
• Developer: Individuals responsible for integrating the DB-chat API with existing systems.
• Admin: Individuals responsible for managing user access and permissions.

5. Core User Flows

• User inputs a natural language query -> System processes the query using AI -> System retrieves relevant data from the database -> User receives the data.
• Developer integrates DB-chat API with existing system -> System enables natural language query functionality.
• Admin sets up user access and permissions -> Users are granted appropriate access to query the database.

6. Visuals Colors and Theme

• primary: #007BFF (a professional blue for brand identity)
• primary_light: #66B2FF (a lighter tint for hover states)
• secondary: #6C757D (a neutral gray for supporting elements)
• accent: #28A745 (a green for CTAs and active states)
• highlight: #FFC107 (a yellow for notifications and hover states)
• bg: #F8F9FA (a light gray for the page background)
• surface: #FFFFFF (a white for card/panel backgrounds)
• text: #212529 (a dark gray for primary text)
• text_muted: #6C757D (a softer gray for secondary text)
• border: #DEE2E6 (a subtle gray for borders)

7. Signature Design Concept

Interactive Data Flow Visualization

The landing page will feature an interactive data flow visualization. Users will see a dynamic illustration of data moving through a pipeline, representing the transformation from natural language input to database query and finally to data output. Users can hover over different sections of the pipeline to see detailed animations of data being processed and transformed. This concept will be built using motion/react for smooth animations and interactions.

Landing Hero Motion Brief

The hero section will depict a user typing a query, which then transforms into a stream of data flowing through a stylized pipeline. As the data moves, it changes color and form, symbolizing the processing and retrieval stages. The final stage will show the data being neatly organized and presented back to the user. This loop will last 10 seconds and will be responsive to user interactions, such as hovering to pause the animation or clicking to see more details.

8. Interaction Model & Motion Direction

• Intended Interaction Model: Animated
• The landing page will feature moderate scroll-triggered reveals and hover transitions. Interactive elements will have spring physics for a polished feel. This approach is suitable for a feature-rich product page that emphasizes the innovative nature of DB-chat.

9. Non-Functional Requirements

• The system must handle a minimum of 1000 concurrent users.
• The response time for queries should not exceed 2 seconds.
• The system should ensure data privacy and comply with relevant data protection regulations.

10. Tech Stack

• Frontend: React for Web
• Backend: FastAPI
• Database: MySQL or MariaDB, using alembic for migrations
• AI Models: GPT 5.4 for user-friendly responses
• AI Tools: Litellm for LLM Routing, Langchain
• Local Orchestration: Docker, docker-compose
• Server-side Orchestration: Kubernetes

11. Assumptions and Constraints

• The system assumes users have basic knowledge of natural language queries.
• The system will be constrained by the database's existing schema and data structure.
• Integration with existing systems will require API access.

12. Glossary

• API: Application Programming Interface
• AI: Artificial Intelligence
• DB-chat: The project name for the natural language database query system
• Natural Language Query: A query expressed in everyday language rather than a formal query language like SQL.