Creating an AI Coding Assistant

Creating an AI Coding Assistant

Hello there,

Today, we're going to discuss the creation of a code assistant app. The primary goal of this app is to enable users to input a GitHub link to a specific repository branch, allowing the app to download the repository structure and all associated files. It will then generate a custom GPT model using OpenAI APIs, incorporating the repository files and their structure as context. This context-aware GPT assistant can then be queried by the user with questions about their codebase. Here’s the detailed breakdown of the process:

Features

1. User Interaction:

   • User inputs a GitHub link to a specific repository and branch.

2. Repository Handling:

   • The website downloads the repository structure along with all files.

3. Custom GPT Creation:

   • A custom GPT model is created using OpenAI APIs, incorporating the repository files as context.

4. Querying:

   • Users can query this custom GPT assistant, benefiting from its full context awareness of the repository.

Implementation

The website will be built using Next.js and hosted on a private Vercel website. The MVP version will have some limitations such as:

• No persistence.
• No security and login features.
• Use of the paid OpenAI APIs for smaller repositories only.

Architecture Comparison

Several architectures and services were considered:

1. Chat GPT:

   • Pros: Has file upload APIs, assistant creation features.
   • Cons: Smaller repositories only due to API constraints.

2. Claude:

   • Pros: Rated higher for coding, max five files.
   • Cons: No file upload APIs, less friendly API.

3. GitHub Co-pilot:

   • Pros: Autocomplete and code suggestion with context.
   • Cons: Limited to ChatGPT version 3.5, not as imaginative for coding solutions according to some users.

Implementation Steps

1. APIs Tie-Up:

   • Using Chat GPT initially due to clearer use case.

2. Website Features:

   • Simple interface where users can input GitHub repository links.
   • Backend will handle the file download, and create vectors and custom GPT models.

3. Coding Practice:

   • Aligning the API configuration on the backend.
   • Ensuring the assistant creation and query-answering process flows seamlessly.

Testing and Results

After implementing the backend and testing it with a sample repository, we can verify if the assistant works by querying it and ensuring it responds intelligently.

Example query: "Explain what three components are in my repository." The assistant will then respond with intelligent answers based on the files provided.

Limitations and Future Work

Improvements could involve handling more file types, leveraging code interpreters for better responses, and differently populating chat contexts.

Keywords

• AI Coding Assistant
• Custom GPT Model
• Next.js
• OpenAI APIs
• Repository Context
• Vercel Hosting
• Chat GPT
• Claude
• GitHub Co-pilot

FAQ

1. What is the main functionality of the AI Coding Assistant?

The AI Coding Assistant allows users to input a GitHub link, download associated files, and generate a custom GPT model that can be queried about the codebase.

2. Which technologies were used in creating the AI Coding Assistant?

The app uses Next.js for the frontend and OpenAI APIs to create and query custom GPT models. It is hosted on Vercel.

3. What are the limitations of the current MVP version?

The MVP lacks persistence, security, and login features. It also uses the paid OpenAI APIs, which limits the size of repositories it can handle.

4. How does the AI assist in understanding the codebase?

By providing the GPT model with the repository files as context, the assistant can intelligently answer questions about the codebase structure and functionality.

5. What architecture decisions were made?

Initially, Chat GPT was selected due to its comprehensive API offerings, including file upload capabilities, despite considering Claude and GitHub Co-pilot for their respective strengths.

6. How does this compare to GitHub Co-pilot?

The AI Coding Assistant uses more advanced GPT models and provides imaginative coding solutions. However, GitHub Co-pilot is more integrated with actual coding environments like VS Code and has full context of the project.