Blazer AI: Intelligent Student Handbook and Website Assistant

This is a submission for the Heroku "Back to School" AI Challenge

Blazer AI: Intelligent Student Handbook Assistant

What I Built

Blazer AI is an AI-powered chatbot designed specifically for AB Tech students and faculty to quickly find information buried within the school's extensive website and student handbook. The application solves the common problem of students and faculty struggling to navigate institutional resources and find answers to academic questions.

Built in just 2 weeks while managing 5 classes, Blazer AI combines vector similarity search with intelligent query routing to provide contextually relevant answers. The system can handle both specific course inquiries (like "Tell me about CSC-151") and general questions (like "What are the graduation requirements?") by automatically detecting query types and routing them through appropriate search mechanisms.

The application transforms traditionally static institutional knowledge into an interactive, conversational experience that's available 24/7 for student and faculty support.

Category

Student Success - Blazer AI directly addresses student success by making institutional knowledge more accessible, reducing time spent searching for academic information, and providing instant answers to common student questions about courses, policies, and campus resources.

Educator Empowerment - The same that can be said about the student success category towards Blazer AI can be said about the educator empowerment potential. All of my instructors have a really hard time referring to specific sections in the handbook for policies, as well as referring to specific webpages to answer questions, due to our school website being over 3000 pages. Educator empowerment may be more applicable, as the typical student may ask questions related only to their program of study or particular classes. In contrast, now educators and faculty have instant contextual access to all of the website and handbook data.

Demo

Live Application: https://blazer-ai-abt.com/

Source Code:

• Frontend: https://github.com/echtoplasm/handBooky-frontend
• Backend: https://github.com/echtoplasm/handBooky-backend

Screenshots

Homepage before chatting:

Normal questions being asked:

Demo GIF:

GIF showing the React state management and context being passed between two components to ask questions of the chatbot

Key Features Demonstrated

• Intelligent query routing between vector and non-vector search
• Real-time AI responses with source citations
• Mobile-responsive design optimized for student use
• Sample questions tailored to common student inquiries

Technical Architecture

• Frontend: React application deployed via Heroku Static Buildpack
• Backend: Node.js/Express API with PostgreSQL vector database
• AI Integration: Claude 3.5 Haiku via Heroku Managed Inference

How I Used Heroku AI

Heroku Managed Inference: Integrated Claude 3.5 Haiku for natural language processing and response generation. The AI agent is configured with a comprehensive system prompt that restricts responses to school-related topics and ensures citations are included.

Heroku Embedding Model API: Used for generating vector embeddings from user queries, enabling semantic similarity search through the knowledge base.

pgvector Integration: Leveraged PostgreSQL with pgvector extension for storing and querying text embeddings, allowing the system to find contextually relevant information even when exact keyword matches aren't available.

Agent Coordination

The system employs intelligent routing logic:

1. Course Code Detection: Regex pattern matching identifies specific course queries (e.g., "CSC-151") and routes them to direct database lookups
2. Vector Search: General queries generate embeddings and perform similarity searches through the vector database
3. Context Assembly: Retrieved information is structured with source metadata and fed to the AI agent for natural language response generation

Technical Implementation

Multi-Agent Architecture

• Query Router: Analyzes incoming messages to determine search strategy
• Vector Search Agent: Handles semantic similarity searches using embeddings
• Course Lookup Agent: Manages direct database queries for specific course codes
• Response Generator: Claude 3.5 Haiku processes context and generates student-friendly responses

Key Technologies

• PERN Stack: PostgreSQL, Express.js, React, Node.js
• Vector Database: PostgreSQL with pgvector for similarity search
• AI Integration: Heroku Managed Inference with Claude 3.5 Haiku

Technical Challenges Solved

Intelligent Query Routing: Developed a hybrid approach that combines exact course code matching with semantic vector search, optimizing both accuracy and response time.

Data Processing Pipeline: Built utilities to process PDF documents and website content into structured, searchable chunks with embeddings, handling inconsistent institutional data formats.

Context Optimization: Implemented context assembly that includes source metadata, prerequisites, and corequisites to provide comprehensive responses while staying within token limits.

Responsive Design: Created a mobile-first interface optimized for student use patterns, recognizing that most students access information via smartphones.

Development Timeline: Solo-developed and completed in 2 weeks while maintaining a full course load, demonstrating efficient project management and focused technical implementation.

Data Sources: Integrated AB Tech's student handbook (PDF) and website sitemap data, creating a comprehensive knowledge base covering academic policies, course information, and campus resources.

The application successfully transforms static institutional documentation into an interactive, intelligent assistant that understands student intent and provides relevant, cited information in a conversational format.

Future Implementations

The future implementation of this application would allow users to drag and drop their own sitemap data or handbook PDF data, and the app would parse and embed it for them. This would allow any institution to have an RAG agent implemented and ready to answer any of their questions related to their institution's resources. This was the original vision for this application, but due to time constraints and being a solo developer, I had to scale back the ambition a bit.