MnemosyneAI Chatbot
An intelligent chatbot integrated into a portfolio website, providing real-time assistance and information to visitors using advanced RAG techniques.
Project Overview
MnemosyneAI is a sophisticated chatbot designed to enhance user interaction on a portfolio website. It provides real-time assistance, answers queries about projects and skills, and offers a personalized experience to visitors using advanced Retrieval-Augmented Generation (RAG) techniques.
Technical Implementation
The chatbot is built using React and TypeScript, integrated into a Next.js application. It uses Redux for state management, the Groq API for natural language processing, and Tailwind CSS for styling. The RAG system is implemented using Pinecone for vector storage and LangChain for seamless integration with the language model. The chat interface is designed to be responsive and user-friendly, with features like message streaming and markdown support.
Technologies Used
Key Features
- Real-time chat interface with message streaming
- Context-aware responses based on current page and RAG system
- Markdown and code syntax highlighting support
- Persistent chat history using Redux and local storage
- Responsive design for mobile and desktop
- Secure API communication with token-based authentication
- RAG system for improved accuracy and contextual understanding
- Dynamic content retrieval based on user queries
Challenges and Solutions
One of the main challenges was implementing a smooth streaming experience for the chat responses while maintaining a responsive UI. This was overcome by using React's state management capabilities and careful stream handling. Another significant challenge was integrating the RAG system effectively, which required optimizing vector storage and retrieval processes to maintain low latency in responses. We solved this by fine-tuning the Pinecone index and implementing efficient caching strategies.
Outcome and Impact
MnemosyneAI has significantly improved user engagement on the portfolio website, providing instant, relevant, and highly accurate information to visitors. The RAG system has enhanced the chatbot's ability to understand context and provide more nuanced responses, often drawing from the specific content of the portfolio. It has reduced the need for extensive navigation by offering quick, contextual answers and has become a unique selling point of the portfolio, showcasing both front-end development skills and advanced AI integration capabilities.