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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
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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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