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In this video, I explore the powerful technique of late chunking in long context embedding models. By preserving contextual information across entire documents before chunking, this method offers more precise retrieval while minimizing storage needs. Join me as I compare it to other methods like Anthropic's contextual retrieval and provide insights for implementing it in your applications.

LINKS:

Colab: https://tinyurl.com/ynxjjyu8

https://jina.ai/news/late-chunking-in-long-context-embedding-models/

https://jina.ai/news/what-late-chunking-really-is-and-what-its-not-part-ii/

https://weaviate.io/blog/late-chunking

https://arxiv.org/pdf/2409.04701

https://www.anthropic.com/news/contextual-retrieval

https://github.com/jina-ai/late-chunking?tab=readme-ov-file

https://youtu.be/6efwN_US-zk

https://youtu.be/tmiBae2goJM

💻 RAG Beyond Basics Course:

https://prompt-s-site.thinkific.com/courses/rag

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00:00 Introduction to Contextual Retrieval and Late Chunking

00:28 Understanding Embedding Models and Their Parameters

00:55 Challenges with Standard RAG Pipelines

02:08 Late Chunking Explained

03:06 Implementation and Benefits of Late Chunking

05:21 Comparing Late Chunking with Other Techniques

10:51 Practical Implementation Guide

16:09 Conclusion and Further Resources

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