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
• Per-Course Retrieval Mode: Instructor-controlled additive vs single-unit retrieval for chat
• 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
6. Retrieval Mode: On the course Home page, toggle “Use additive retrieval” to allow chat to include earlier published units in addition to the selected unit. When off, chat uses only the selected unit.

For Students

1. Access: Navigate to /student
2. Course Selection: Choose your course
3. Assessment: Complete calibration questions
4. Chat Interface: Select a unit, then ask questions about course material. Chat retrieval respects the course’s retrieval mode.
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