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Learn how different tokenization methods impact search quality and explore optimization techniques for vector search performance.\n\n**What You’ll Learn:**\n1. 🧠 **Embedding Models and Tokenization**: Understand the inner workings of embedding models and how text is transformed into vectors.\n2. 🔍 **Tokenization Techniques**: Explore several tokenizers like Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece, and how they affect search relevancy.\n3. 🚀 **Search Optimization**: Learn to tackle common challenges such as terminology mismatches and truncated chunks in embedding models.\n4. 📊 **Search Quality Metrics**: Measure the quality of your search using various metrics and optimize search performance.\n5. ⚙️ **HNSW Algorithm Tuning**: Adjust Hierarchical Navigable Small Worlds (HNSW) parameters to balance speed and relevance in vector search.\n6. 💾 **Vector Quantization**: Experiment with major quantization methods (product, scalar, and binary) and understand their impact on memory usage and search quality.\n\n## 🔑 Key Points\n- 🧩 **Tokenization in Large Models**: Learn how tokenization works in large language models and how it affects search quality.\n- 🛠️ **Training Tokenizers**: Explore how Byte-Pair Encoding, WordPiece, and Unigram are trained and function in vector search.\n- 🔄 **Search Optimization**: Understand how to adjust HNSW parameters and vector quantizations to optimize your retrieval systems.\n\n## 👨‍🏫 About the Instructor\n- 👨‍💻 **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.\n\n🔗 To enroll or learn more, visit 📚 [deeplearning.ai](https://www.deeplearning.ai/short-courses/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fksm26%2Fretrieval-optimization-from-tokenization-to-vector-quantization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fksm26%2Fretrieval-optimization-from-tokenization-to-vector-quantization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fksm26%2Fretrieval-optimization-from-tokenization-to-vector-quantization/lists"}