AI Assisted Docs

Introduction

With the help of a customized ChatGPT, our project offers a novel way to access and engage with documentation by utilizing artificial intelligence. Through the integration of cutting-edge machine learning methodologies and database technology, we provide a dynamic and user-friendly interface for navigating and understanding complex data. The following characteristics best sum up our AI-assisted documentation system:

📘 Knowledge Base Development

• Pre-built Information Repository: Our system begins with a thorough preparation of the knowledge base. To ensure a rich and easily accessible content basis, this entails organizing and selecting documentation into .mdx files. This knowledge base serves as the foundation for our AI's comprehension and reaction skills.

🚀 Embedding Integration with pgvector for Effective Data Representation

• We optimize data storage and retrieval by integrating vector embeddings into a Supabase database using text-embedding-ada-002. This allows for the representation of complex documentation content in a form that our AI can understand, enabling quick and accurate information retrieval.

🔍 Vector Similarity Search

• This intuitive method of information retrieval allows our system to quickly identify the most pertinent data in response to user inquiries, ensuring users find exactly what they need efficiently and precisely.

💡 Adaptive AI-Driven Responses

• Customized Communication: At the core of our AI-assisted documentation is the integration with OpenAI's GPT-3.5, enabling the generation of personalized text completions. This dynamic interaction model not only answers queries but also provides insights, suggestions, and further reading options, all streamed directly to the user.

Disclaimer

The documentation provided herein is solely for testing and demonstration purposes. It is not the official documentation for DevZero.

Technology Stack

Our project leverages a robust technology stack, focusing on Supabase for database management and Next.js for our development framework.

Database: Supabase

• Supabase is an open-source alternative to Firebase, offering a scalable PostgreSQL database with built-in authentication, real-time capabilities, and automatic APIs. It simplifies backend development, facilitating rapid project scaling.

Framework: Next.js

• Next.js is a React framework enhancing web development with features like hybrid static & server rendering, TypeScript support, and smart bundling. Designed for simplicity and performance in production environments.

Getting Started

Acquiring an OpenAI API Key

• Sign Up: Visit the OpenAI Developer Portal and sign up for an account if you haven't already.
• Generate API Key: Once logged in, navigate to the API section and follow the instructions to generate a new API key.

Configuring Your API Key in the Application

After obtaining your OpenAI API key, you'll follow a similar process to configure it within your application:

• Create a .env File: Create a file named .env. This file is where you'll store your OpenAI API key securely.

• Add API Key to .env File: Open the .env file and add the following line, replacing your_api_key_here with the API key you obtained from OpenAI:

OPENAI_API_KEY=your_api_key_here

• Example Configuration: Refer to the .env.example file in the root directory for an example of how to format your .env file correctly.

Installation

Prerequisites

• Docker

Run locally:

git clone https://github.com/devzero-inc/ai-assisted-docs.git
cd ai-assisted-docs
docker compose up

App will be running on PORT:3000 -> http://localhost:3000/

Now just go to http://localhost:3000/ and explore the application.