https://github.com/ksm26/retrieval-optimization-from-tokenization-to-vector-quantization

The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.
https://github.com/ksm26/retrieval-optimization-from-tokenization-to-vector-quantization

data-science embeddingmodels hnsw machine-learning machinelearning natural-language-processing rag rag-systems ragsystems retrieval-augmented-generation retrievaloptimization search-algorithm search-optimization searchoptimization tokenization vectorquantization vectorsearch

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The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.

• Host: GitHub
• URL: https://github.com/ksm26/retrieval-optimization-from-tokenization-to-vector-quantization
• Owner: ksm26
• Created: 2024-10-09T15:30:11.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-28T15:34:41.000Z (10 months ago)
• Last Synced: 2025-03-28T16:18:49.879Z (7 months ago)
• Topics: data-science, embeddingmodels, hnsw, machine-learning, machinelearning, natural-language-processing, rag, rag-systems, ragsystems, retrieval-augmented-generation, retrievaloptimization, search-algorithm, search-optimization, searchoptimization, tokenization, vectorquantization, vectorsearch
• Language: Jupyter Notebook
• Homepage: https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/
• Size: 4.25 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🔍 [Retrieval Optimization: From Tokenization to Vector Quantization](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/)

Welcome to the "Retrieval Optimization: From Tokenization to Vector Quantization" course! 🎓 The course teaches you how to optimize vector search in large-scale customer-facing RAG applications.

## 📘 Course Summary

In this course, you’ll dive deep into tokenization and vector quantization techniques, exploring how to optimize search in large-scale Retrieval-Augmented Generation (RAG) systems. Learn how different tokenization methods impact search quality and explore optimization techniques for vector search performance.

**What You’ll Learn:**

1. 🧠 **Embedding Models and Tokenization**: Understand the inner workings of embedding models and how text is transformed into vectors.

2. 🔍 **Tokenization Techniques**: Explore several tokenizers like Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece, and how they affect search relevancy.

3. 🚀 **Search Optimization**: Learn to tackle common challenges such as terminology mismatches and truncated chunks in embedding models.

4. 📊 **Search Quality Metrics**: Measure the quality of your search using various metrics and optimize search performance.

5. ⚙️ **HNSW Algorithm Tuning**: Adjust Hierarchical Navigable Small Worlds (HNSW) parameters to balance speed and relevance in vector search.

6. 💾 **Vector Quantization**: Experiment with major quantization methods (product, scalar, and binary) and understand their impact on memory usage and search quality.

## 🔑 Key Points

- 🧩 **Tokenization in Large Models**: Learn how tokenization works in large language models and how it affects search quality.

- 🛠️ **Training Tokenizers**: Explore how Byte-Pair Encoding, WordPiece, and Unigram are trained and function in vector search.

- 🔄 **Search Optimization**: Understand how to adjust HNSW parameters and vector quantizations to optimize your retrieval systems.

## 👨‍🏫 About the Instructor

- 👨‍💻 **Kacper Łukawski**: Developer Relations Lead at Qdrant, Kacper brings expertise in vector search optimization and teaches practical techniques to enhance search efficiency in RAG applications.

🔗 To enroll or learn more, visit 📚 [deeplearning.ai](https://www.deeplearning.ai/short-courses/).