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Evolution of Vector Databases and Semantic Search in Machine Learning
The chapter explores the evolution of vector databases and their role in representing semantic meaning for efficient search capabilities in machine learning. It discusses the significance of semantic search engines, their advantages over traditional search engines, and the integration of vector embeddings and language models for high-quality search results. Furthermore, it delves into the application of retrieval augmented generation, explaining how it combines vector databases with language models to provide natural language responses, particularly beneficial for building chatbots in customer support scenarios.