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Embeddings are crucial for a production-ready RAG system but often get overlooked. I cover the costs, storage considerations, and ways to reduce storage requirements using techniques like dimensionality reduction and quantization. Learn how these methods can improve speed and save costs without compromising too much on performance.
LINKS:
Blogpost: https://huggingface.co/blog/embedding-quantization
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00:00 Introduction to Embeddings in RAG Systems
00:47 Understanding Embedding Costs
01:17 Storage Costs and Considerations
03:32 Reducing Storage Needs
03:41 Dimensionality Reduction Techniques
04:24 Matrosha Representation Learning
05:14 Precision Reduction Techniques
06:28 Quantization Study by Hugging Face
10:07 Implementing Quantization in Your Pipelines
12:56 Using Open Source Vector Stores
15:01 Conclusion and Final Thoughts
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