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Congratulations!

1. Congratulations!

Congratulations on making it to the end of the course! Let's recap everything you've learned and see where you can go from here.

2. Chapter 1 - RAG Fundamentals with Weaviate

In Chapter 1, you started putting the RAG pieces together: you used LLMs to generate text, create text embeddings as a means to compare the similarity between two pieces of text. This allowed you to, for a given user input, retrieve the most semantically similar information. Finally, you integrated this retrieved information with the user query into a prompt so that your LLM can answer questions beyond its training data.

3. Chapter 2 - End-to-End RAG with Weaviate

In Chapter 2, you used Weaviate to orchestrate your RAG workflow. You also went from embedding simple string to embedding PDF documents, which you processed using the docling library.

4. Chapter 3 - Multi-Modal RAG

Finally, in Chapter 3, you added images into the mix. You used a multi-modal embedding model to consider a PDF not as a document containing text and image elements, but as a single image. You used ColPali to embed these image documents and a text query to retrieve the most similar image documents. Then, you crafted a prompt to combine the retrieved image and text query and sent them to a multi-modal generative model from OpenAI.

5. What's next?

We're accomplished a lot in this course, but there are many avenues you can continue down. Agentic RAG, where AI agents are able to choose to trigger a RAG workflow is an exciting avenue to explore. This means your agent can perform RAG on different data sources depending on the users question.

6. What's next?

Additionally, the ColPali model isn't the only multi-modal embedding model out there. Other providers, such as Cohere, offer multi-modal models that are well worth checking out.

7. What's next?

Finally, to learn more about how Weaviate can support and orchestrate data sources for your AI applications, check out the following learning resources.

8. Congratulation!

Congratulations, again, and best of luck on the rest of your AI journey!

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