Headstarter-Codebase-RAG

Headstarter-Codebase-RAG is a project designed to create an AI expert for codebases using Retrieval-Augmented Generation (RAG). The goal is to enable intuitive interactions with codebases, allowing users to understand their structure, functionality, and potential areas for improvement.

Overview

This project demonstrates how to build an AI-powered assistant capable of retrieving and analyzing relevant sections of a codebase to answer user queries. The process involves embedding the codebase, storing embeddings in a vector database (Pinecone), and leveraging large language models (LLMs) to generate context-aware responses.

The /ai-coding-agent-help command on Slack illustrates this functionality: when a query is provided, the tool retrieves the most relevant code snippets and uses an LLM to craft a helpful response.

Key Features

• Codebase Embedding: Converts codebase contents into vector representations.
• Vector Database Storage: Uses Pinecone to store and retrieve embeddings efficiently.
• LLM Integration: Generates intelligent, context-aware responses based on the codebase content.
• Web Application Interface: Interact with the AI expert through a user-friendly web app.

Learning Objectives

Through this project, you will learn to:

1. Embed codebase contents into vector formats.
2. Use Pinecone for vector storage and retrieval.
3. Implement RAG workflows to enable intelligent codebase interaction.
4. Build a web application to chat with the AI expert.

Submission Requirement

The final deliverable is a functional web application where users can chat with a codebase.

Development Options

You can choose one of the following approaches to build the web app:

1. Using Python (Streamlit):
   Refer to the Streamlit documentation for guidance on building a chatbot.

2. Using React and Next.js:
   Explore the AI Chatbot template on Vercel for a React-based implementation.