Building a PDF-driven RAG system with Weaviate

This repository contains materials for the online workshop "Building a PDF-driven RAG system with Weaviate".

You’ll learn how to:

• Extract and preprocess text and images from PDFs
• Chunk and embed document content
• Store and retrieve data using Weaviate
• Build Retrieval-Augmented Generation (RAG) pipelines that combine text and images

The workshop is organized as a series of Jupyter notebooks.

The notebooks are numbered, so you can follow them along in order.

Requirements: Python 3.10+, Weaviate, Cohere/Anthropic API keys (for embeddings and LLMs).

Setup instructions

Set up your Python environment

• Set up your preferred Python environment

  • e.g. Set up a virtual environment (optional but recommended):

  python -m venv .venv
  source venv/bin/activate  # On Windows use `venv\Scripts\activate`
  pip install -r requirements.txt

  • Or use uv, conda, or any other environment manager you prefer.

• Set up the .env file

  • Note: You ONLY need to do this if you do not have ANTHROPIC_API_KEY and COHERE_API_KEY set in your environment.
  1. Copy .env.example to .env
  2. Fill in the ANTHROPIC_API_KEY and COHERE_API_KEY with corresponding values.
  3. In the live session, the instructor may provide temporary keys.