Project Helper AI Chatbot
01/2025 - Present
Project Overview
Project Helper is an advanced AI chatbot designed to solve a common problem faced by developers: understanding large, complex codebases. When joining a new project or navigating an unfamiliar repository, developers often spend hours reading through documentation and code to understand the project structure, dependencies, and functionality. This chatbot leverages cutting-edge AI technology to provide instant, contextually-aware answers about any codebase. Built as a full-stack application, it combines AWS Bedrock's powerful language models with LangChain's orchestration capabilities to enable natural, conversational queries about code. The system maintains a PostgreSQL database that synchronizes with GitHub repositories through webhooks, ensuring the AI always has access to the latest code. Using a Retrieval-Augmented Generation (RAG) system with vector embeddings, the chatbot can understand the semantic meaning of code and provide accurate, relevant responses to developer questions.
Problem
Developers waste significant time trying to understand unfamiliar codebases, searching through documentation and code files to answer simple questions about project structure, functionality, and implementation details.
Solution
Built an AI-powered chatbot that allows developers to ask questions about any repository in natural language and receive instant, contextually accurate answers by leveraging RAG architecture and real-time repository synchronization.
Key Features
- Natural language queries about codebase structure and functionality
- Real-time repository synchronization via GitHub webhooks
- Vector embeddings for semantic code understanding
- RAG system for contextually relevant responses
- Support for multiple programming languages and frameworks
- PostgreSQL database for efficient data management
Challenges & Learnings
Challenges Faced
- Integrating AWS Bedrock with LangChain for optimal performance
- Designing an efficient vector embedding system for code
- Implementing real-time webhook synchronization with GitHub
- Balancing response accuracy with query speed
Key Learnings
- Deep understanding of RAG architecture and implementation
- Experience with AWS Bedrock and advanced AI/ML services
- Skills in vector database optimization and semantic search
- Knowledge of GitHub webhooks and API integration
- Full-stack development with TypeScript, React, and Python
