BiocBot - AI-Powered Study Assistant

BiocBot is an AI-powered study assistant platform that enables students to interact with course material in a chat-based format. Instructors can upload documents (PDFs, DOCX, or TXT), which are automatically parsed, chunked, and embedded into a vector database (Qdrant) for semantic search. When a student asks a question, the system retrieves relevant chunks and generates a response grounded in course content.

🚀 Features

• Document Management: Upload and organize course materials
• Vector Search: Semantic search across documents using Qdrant
• AI Chat Interface: Student interaction with course content
• Assessment Questions: Create and manage course assessments
• Course Structure: Organize content by units/lectures
• User Management: Separate interfaces for instructors and students

🏗️ Architecture

BiocBot follows a split architecture with a public frontend and a private backend, adhering to clear separation of concerns for maintainability and security.

Tech Stack

• Frontend: HTML + Vanilla JS (no frameworks), styled via separate CSS files
• Backend: Node.js (Express), built with modular architecture
• Database: MongoDB (for documents, user sessions, analytics)
• Vector Database: Qdrant for semantic search and similarity retrieval
• Embeddings: Ollama with nomic-embed-text model
• Document Processing: UBC GenAI Toolkit modules

🛠️ Setup & Installation

Prerequisites

• Node.js v18.x or higher
• MongoDB instance
• Qdrant vector database (Docker recommended)
• Ollama with nomic-embed-text model

1. Clone and Install

git clone <repository-url>
cd tlef-biocbot
npm install

2. Environment Configuration

Create a .env file in the root directory with the following variables:

# MongoDB Connection
MONGO_URI=mongodb://localhost:27017/biocbot

# Server Port
TLEF_BIOCBOT_PORT=8080

# Qdrant Configuration
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=super-secret-dev-key

# Embeddings Provider Configuration
EMBEDDING_PROVIDER=ubc-genai-toolkit-llm

# LLM Provider Settings (for Embeddings)
LLM_PROVIDER=ollama
LLM_API_KEY=nokey
LLM_ENDPOINT=http://localhost:11434
LLM_EMBEDDING_MODEL=nomic-embed-text
LLM_DEFAULT_MODEL=llama3.1

3. Start Services

Start Qdrant (Docker)

docker run -p 6333:6333 qdrant/qdrant

Start Ollama with nomic-embed-text

ollama pull nomic-embed-text
ollama serve

Start BiocBot

npm run dev

🔍 Qdrant Integration

BiocBot now includes advanced vector search capabilities through Qdrant integration:

Features

• Automatic Document Processing: Documents are automatically chunked, embedded, and stored
• Semantic Search: Find relevant content using natural language queries
• Course-Aware Search: Filter results by course and lecture
• Real-time Indexing: New documents are immediately searchable

API Endpoints

• GET /api/qdrant/status - Check Qdrant service status
• POST /api/qdrant/process-document - Process and store document
• POST /api/qdrant/search - Semantic search across documents
• DELETE /api/qdrant/document/:id - Delete document chunks
• GET /api/qdrant/collection-stats - Get collection statistics

Testing the Integration

Visit /qdrant-test to test the Qdrant functionality:

• Process test documents
• Perform semantic searches
• View collection statistics

📚 Usage

For Instructors

1. Access: Navigate to /instructor
2. Onboarding: Complete course setup
3. Upload Documents: Add course materials to units
4. Create Questions: Build assessments for students
5. Publish Units: Make content available to students

For Students

1. Access: Navigate to /student
2. Course Selection: Choose your course
3. Assessment: Complete calibration questions
4. Chat Interface: Ask questions about course material
5. Semantic Search: Find relevant content using natural language

🔧 Development

Project Structure

tlef-biocbot/
├── public/                 # Frontend assets
│   ├── instructor/        # Instructor interface
│   ├── student/          # Student interface
│   └── qdrant-test.html  # Qdrant testing page
├── src/                   # Backend source
│   ├── models/           # Data models
│   ├── routes/           # API routes
│   ├── services/         # Business logic
│   └── server.js         # Main server file
└── documents/            # Course documentation

Key Components

• QdrantService: Handles vector database operations
• Document Processing: Automatic chunking and embedding
• Semantic Search: Vector similarity search
• Course Management: Structured content organization

🚧 Current Status

• ✅ Phase 1: Backend pipeline with Qdrant integration
• ✅ Document Upload: File and text document support
• ✅ Vector Search: Semantic document retrieval
• 🔄 Assessment System: Question creation and management
• 🔄 Student Interface: Chat-based learning experience

🤝 Contributing

This project follows clean architecture principles optimized for clarity, maintainability, and junior developer readability. All code should be:

• Modular: Single responsibility functions and classes
• Documented: Comprehensive docblocks and inline comments
• Accessible: Clear variable names and logical flow
• Secure: Input validation and error handling

📄 License

ISC License