End-to-End RAG with Weaviate

중급스킬 수준

업데이트됨 2026. 3.

Master RAG with Weaviate! Embed text and images for retrieval, and experiment with vector, BM25, and hybrid search.

강의 설명

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!

선수 조건

Working with the OpenAI API

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.

From RAGs to Riches!

50 XP

Text generation with LLMs

100 XP

Creating text embeddings

100 XP

Comparing text embeddings

100 XP

Putting together the "R", "A", "G"

100 XP

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