Vector-Databases-Powering-the-Next-Generation-of-AI-Applications.pptx

Vector-Databases-Powering-the-Next-Generation-of-AI-Applications.pptx

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  Vector Databases: Powering theNext Generation of AI Applications Vector databases are becoming essential for modern AI applications. They enable efficient storage and retrieval of high-dimensional data. This presentation provides a comprehensive overview. We will cover the fundamental concepts and practical applications. by Dhruv
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  Presentation Overview Introduction FundamentalsArchitecture Key Features • Introduction to Vector Databases • Vector Fundamentals • Core Architecture • Key Features and Capabilities • Major Vector Database Solutions • Use Cases and Applications • Conclusion and Future Outlook
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  What is aVector Database? A vector database stores, indexes, and retrieves vector embeddings. It enables fast retrieval and similarity search. Vector DBs are optimized for similarity search. They handle high-dimensional data represented as vectors. Semantic Similarity Enables efficient retrieval of semantically similar information. AI Infrastructure Critical infrastructure for modern AI applications.
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  The Evolution ofDatabases Databases have evolved to meet changing data and application needs. Traditional databases used structured data in tables. NoSQL databases offer flexible schemas. Vector databases are optimized for high-dimensional vector data. 1 Relational Structured data 2 NoSQL Flexible schemas 3 Graph Relationship focused 4 Vector High-dimensional data
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  Why Vector DatabasesMatter Unstructured data is exploding. AI and machine learning applications are on the rise. Semantic search is needed beyond keyword matching. This enables similarity-based operations at scale. Unstructured Data Explosion of unstructured data (30-60% annual growth). Rise of AI Rise of AI and machine learning applications. Semantic Search Need for semantic search beyond keyword matching.
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  Vectors and Embeddings Vectorsare numerical representations of data. Embeddings are dense vector representations. Each dimension represents a feature. Similar items have vectors that are close in space. Machines can understand similarity. Vectors Numerical representations in multi-dimensional space. Embeddings Dense vector representations capturing semantic meaning.
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  Transforming Data intoVectors Language models transform text. Computer vision models transform images. Audio processing models transform audio. Embeddings preserve semantic relationships between data points. Text Language Models Images Vision Models Audio Audio Models
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  Vector Spaces andDimensions A vector space is a mathematical space. Data points are represented as vectors. Dimensions are values in each vector. High-dimensional spaces capture complex relationships. Dimensionality reduction techniques can help. 1 High Dimensions Complex relationships 2 Vector Space Mathematical space