Skip to main content
HomePython

Course

End-to-End RAG with Weaviate

IntermediateSkill Level
4.7+
14 reviews
Updated 03/2026
Master RAG with Weaviate! Embed text and images for retrieval, and experiment with vector, BM25, and hybrid search.
Start Course for Free
PythonArtificial Intelligence2 hr4 videos14 Exercises1,200 XPStatement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Group

Training 2 or more people?

Try DataCamp for Business

Course Description

Learn how to go from simple LLM calls to multi-modal RAG workflows with Weaviate! You'll learn how to process PDF documents to extract key text content like paragraphs, headings, and tables. You'll embed and store this data for retrieval with Weaviate. Finally, you'll craft effective retrieval prompts to pass to generative models. To cap this all off, you'll treat PDFs as images to allow you to capture context lost from images and plots. You'll use the ColPali multi-modal embedding model with a multi-modal generative model from OpenAI to begin having conversations with images and documents!

Prerequisites

Working with the OpenAI API
1

RAG Fundamentals with Weaviate

Discover how Weaviate enables RAG applications! You'll build a RAG workflow end-to-end by-hand to get familiar with the Retrieval-Augmentation-Generation steps. This understanding will enable robust and optimized RAG workflows in Chapter 2 using Weaviate.
Start Chapter
2

End-to-End RAG with Weaviate

Although coding out RAG workflows by-hand is fun, you may be missing out on optimizations provided by tools like Weaviate. In this chapter, you'll embed, store, retrieve, and generate responses all using Weaviate!
Start Chapter
3

Multi-Modal RAG

In the last chapter, you used the text content from the PDF documents to build your document chunks, but left the image content behind. This results in a lot of lost context that might be useful for retrieval and generation! In this chapter, you'll use ColPali multi-modal models to embed and generate text and images to provide more context for your model responses.
Start Chapter
End-to-End RAG with Weaviate
Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.7
from 14 reviews
86%
7%
0%
7%
0%
  • 202321020
    yesterday

  • Nataliia
    5 weeks ago

  • Julia
    5 weeks ago

    It took me two days to finish because the submission of the code took so long to load and crashed so many times.

  • Andrew
    2 months ago

  • Arun
    3 months ago

  • Doreen
    3 months ago

202321020

Nataliia

Andrew

FAQs

What is RAG and what will I build with Weaviate?

RAG stands for retrieval-augmented generation, where relevant documents are retrieved and fed to a language model. You will build a complete RAG pipeline using Weaviate as the vector database.

Does the course cover multi-modal RAG with images?

Yes. Chapter 3 teaches you to use ColPali multi-modal embedding models to embed and retrieve both text and images from PDF documents for richer context in model responses.

What document types will I process in this course?

You will process PDF documents, extracting paragraphs, headings, tables, and images. You will also treat entire PDF pages as images for multi-modal retrieval.

What prerequisites do I need for this course?

You need Intermediate Python and experience with the OpenAI API. The course is beginner-level for RAG concepts and introduces Weaviate from scratch.

How does Weaviate improve on a hand-coded RAG workflow?

Weaviate provides built-in optimizations for embedding, storing, retrieving, and generating responses. Chapter 2 shows the performance gains compared to coding each RAG step manually.

    Join over 19 million learners and start End-to-End RAG with Weaviate today!

    Create Your Free Account

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

    Grow your data skills with DataCamp for Mobile

    Make progress on the go with our mobile courses and daily 5-minute coding challenges.