End-to-End RAG with Weaviate

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อัปเดตแล้ว 03/2569

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

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ดำเนินการต่อด้วย Google

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เมื่อดำเนินการต่อ คุณยอมรับ ข้อกำหนดการใช้งาน ของเรา นโยบายความเป็นส่วนตัว ของเรา และยอมรับว่าข้อมูลของคุณจะถูกจัดเก็บในสหรัฐอเมริกา

คำอธิบายคอร์ส

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!

ข้อกำหนดเบื้องต้น

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

เริ่มบท

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!

RAG Workflows with Weaviate

50 XP

PDF text extraction and chunking

100 XP

Getting set up with Weaviate

100 XP

Queries in Weaviate

100 XP

RAG from top to bottom

100 XP

เริ่มบท

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.

RAG with text and images

50 XP

Preparing for Multi-Modal RAG

100 XP

Multi-Modal RAG with Weaviate

100 XP

Congratulations!

50 XP

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