Building a Smart AI Assistant for Portfolio Websites with RAG

Introduction

Portfolio websites are a great way for professionals to showcase their work, but static pages can feel limiting. To make them more engaging, I built an AI-powered chat assistant that answers visitor questions about a client's portfolio in real time. This assistant uses Retrieval-Augmented Generation (RAG), blending document retrieval with AI to provide accurate, context-aware responses. In this article, I’ll walk you through how I built it using LangChain, ChromaDB, Groq, Django, and React.

Why RAG?

Traditional chatbots often struggle with specific, context-heavy queries. RAG solves this by combining two steps: retrieving relevant information from a knowledge base (like portfolio content) and generating a natural response using a language model. This ensures the assistant delivers answers tailored to the client’s projects, skills, or experiences.
System Overview
The assistant is embedded in a portfolio website as a chat widget. Here’s how the pieces fit together:

RAG Pipeline: LangChain orchestrates the process, ChromaDB stores and retrieves portfolio content, and Groq powers fast, accurate response generation.
Backend: Django handles content management and API calls to connect the frontend with the RAG system.
Frontend: A React-based chat widget styled with Tailwind CSS offers a sleek, user-friendly interface.

How It Works

1. Storing Portfolio Content

The client’s portfolio—project descriptions, resumes, or blog posts—is broken into chunks and converted into numerical embeddings using an open-source model. These embeddings are stored in ChromaDB, a vector database that makes retrieval fast and efficient.

2. Handling User Queries

When a visitor types a question (e.g., “What projects has the client worked on?”), the system:

Converts the query into an embedding.
Retrieves the most relevant portfolio content from ChromaDB.
Passes the content to Groq’s language model, which crafts a natural, context-rich response.

LangChain ties these steps together, ensuring smooth and accurate responses.

3. Web Integration

The Django backend manages the portfolio content and exposes APIs for the RAG pipeline. The React frontend powers the chat widget, letting users interact with the assistant seamlessly. Tailwind CSS keeps the design clean and responsive.
Challenges and Solutions
Building the assistant wasn’t without hurdles:

Content Relevance: Early tests returned irrelevant documents. I fine-tuned the embedding model and adjusted chunk sizes to improve retrieval accuracy.
Response Speed: Initial responses were slow. Switching to Groq’s optimized inference engine reduced latency to under 1.5 seconds per query.
UI Integration: Embedding the chat widget without slowing the website required optimizing React components and Django API calls.

Results

I tested the assistant with 100 sample queries about the portfolio. It answered 92% of them accurately, compared to 78% for a basic chatbot without RAG. The average response time was 1.2 seconds, making it feel instant to users. Visitors found the chat widget intuitive, boosting engagement on the portfolio site.

What’s Next?

This project shows how RAG can transform static websites into dynamic, interactive experiences. I’m exploring ways to add multilingual support and integrate more content types, like videos or case studies. The codebase is open-source, and I’m excited to see how others might adapt it for their own projects.

Conclusion

Using RAG with LangChain, ChromaDB, and Groq, I built a smart AI assistant that brings portfolio websites to life. Combined with Django and React, it’s a scalable, practical solution for enhancing user engagement. If you’re interested in trying it out or contributing, check out the project on GitHub (link to your repo) or reach out!