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    HomeLarge Language ModelsA Practical Introduction to Vector Databases

    A Practical Introduction to Vector Databases

    A course by
    Data Science Dojo
    Nov/2024 6 lessons English

    A Practical Introduction to Vector Databases provides a deep understanding of vector databases, essential for efficient storage and retrieval of vector-based data in modern large language models (LLMs). The course covers key concepts such as vector search, hybrid search, and various indexing techniques like Product Quantization and Locality Sensitive Hashing. You’ll also explore advanced retrieval methods and Retrieval Augmented Generation (RAG) techniques, learning to optimize performance, scalability, and reliability in production environments. Through hands-on exercises, you’ll gain practical experience in implementing vector databases for high-efficiency search and retrieval tasks.

    What you'll learn

    • Understand the role and importance of vector and semantic search in LLMs.
    • Implement vector, text, and hybrid search for effective data retrieval.
    • Master advanced indexing techniques like Product Quantization and HNSW.
    • Apply retrieval methods like cosine similarity and nearest neighbor search.
    • Explore semantic search and how embeddings improve search relevance.
    • Optimize production systems with advanced RAG techniques and scalability.

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