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End-to-End RAG with Weaviate

中级技能水平
更新时间 2026年3月
Master RAG with Weaviate! Embed text and images for retrieval, and experiment with vector, BM25, and hybrid search.
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PythonArtificial Intelligence2 小时4 视频14 练习1,200 经验值成就声明

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课程描述

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!

先决条件

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.
开始章节
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!
开始章节
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.
开始章节
End-to-End RAG with Weaviate
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