Workshop: Improving R in RAG

This workshop focuses on enhancing the Retrieval component in Retrieval Augmented Generation (RAG) systems using Qdrant vector database. Through hands-on Jupyter notebooks, you'll learn advanced techniques for vector search, multiple embedding models, hybrid retrieval strategies, and practical optimizations to improve RAG system performance.

Prerequisites

• Python 3.12 or higher
• uv - Ultra-fast Python package manager
• Docker (optional) - For running Qdrant locally
• LLM API access - Anthropic, OpenAI, or Hugging Face account

Tip

🎉 Free LLM Access Available! HuggingFace offers a generous free tier for their Inference API with access to thousands of specialized models across multiple AI tasks. Perfect for experimenting with this workshop without upfront costs!

Installation

Clone the repository

git clone <repository-url>
cd workshop-improving-r-in-rag

Install uv

On macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows:

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Alternative (using pip):

pip install uv

Install dependencies

uv sync

Qdrant Setup

You can use either a local Qdrant instance or Qdrant Cloud's free tier.

Option 1: Local Docker Container

docker run -p "6333:6333" -p "6334:6334" -v "$(pwd)/qdrant_storage:/qdrant/storage:z" "qdrant/qdrant:v1.15.4"

Option 2: Qdrant Cloud Free Tier

Sign up for Qdrant Cloud and use the free 1GB cluster.

Environment Configuration

Create a .env file in the notebooks/ directory with your API credentials:

Option 1: HuggingFace (Free Tier Recommended) 🆓

# LLM provider settings
LLM_PROVIDER="huggingface"
HF_TOKEN="your-huggingface-token"

# Qdrant settings
QDRANT_URL="http://localhost:6333"
# QDRANT_API_KEY="your-cloud-api-key" # Only needed for Qdrant Cloud

To get your HuggingFace token:

1. Create a free account at huggingface.co
2. Go to Token Settings
3. Create a new token with "Read" permissions
4. Copy the token to your .env file

Option 2: Other Providers

# For Anthropic
LLM_PROVIDER="anthropic"
ANTHROPIC_API_KEY="your-api-key-here"

# For OpenAI
LLM_PROVIDER="openai"
OPENAI_API_KEY="your-api-key-here"

# Qdrant settings
QDRANT_URL="http://localhost:6333"
# QDRANT_API_KEY="your-cloud-api-key" # Only needed for Qdrant Cloud

All Supported LLM providers:

• 🆓 Hugging Face (Recommended): Generous free tier with thousands of models - Set LLM_PROVIDER="huggingface" and HF_TOKEN
• Anthropic: Set LLM_PROVIDER="anthropic" and ANTHROPIC_API_KEY
• OpenAI: Set LLM_PROVIDER="openai" and OPENAI_API_KEY

Workshop Structure

This workshop consists of 4 progressive notebooks:

📓 00-set-up-environment.ipynb

• Environment setup and dependency installation
• Qdrant connectivity testing
• Introduction to the tech stack (Qdrant, FastEmbed, any-llm-sdk)

📓 01-initial-data-load.ipynb

• Loading HackerNews dataset into Qdrant
• Building a basic RAG pipeline with dense vectors
• Implementing payload-based filtering
• Testing retrieval quality and LLM integration

📓 02-different-representation-models.ipynb

• Setting up multiple vector representations per document
• Implementing sparse vectors (BM25) for keyword matching
• Multi-vector embeddings with ColBERT
• Hybrid search strategies: retrieval + reranking, and fusion methods

📓 03-low-hanging-fruits.ipynb

• Search result diversification using Maximal Marginal Relevance
• Applying business rules and constraints
• Additional optimization techniques for production RAG systems

Running the Workshop

Start Jupyter Lab:

uv run jupyter lab

Or Jupyter Notebook:

uv run jupyter notebook

Then navigate through the notebooks in order, starting with 00-set-up-environment.ipynb.

Technical Stack

• Qdrant - High-performance vector database for dense, sparse, and multi-vector retrieval
• FastEmbed - Efficient text-to-vector conversion with multiple embedding models
• any-llm-sdk - Unified interface for multiple LLM providers

Dataset

The workshop uses a curated HackerNews submissions dataset containing tech discussions, startup ideas, and programming topics - perfect for demonstrating various retrieval challenges and solutions.

Learning Outcomes

By completing this workshop, you'll understand:

• How to set up and configure Qdrant for production RAG systems
• Different embedding models and when to use each approach
• Hybrid retrieval strategies combining dense and sparse vectors
• Advanced reranking and fusion techniques
• Practical optimizations for improving retrieval relevance
• How to handle real-world RAG challenges like search diversity and business constraints