Vector_Databases_Presentation_in_modern_era.pptx
- 1. Vector Databases • An Overview of Vector Databases and Their Applications
- 2. What are Vector Databases? • - Specialized databases designed to store, index, and query high-dimensional vector data • - Ideal for similarity search, machine learning, and AI applications
- 3. Why Do We Need Vector Databases? • - Traditional databases struggle with high- dimensional similarity search • - Essential for use-cases like image search, recommendation systems, NLP, etc.
- 4. How Vector Databases Work • - Store vector embeddings instead of traditional scalar data • - Perform similarity search using metrics like cosine similarity, Euclidean distance
- 5. Architecture of Vector Databases • - Components: • * Vector Indexing Engine • * Metadata Storage • * Search API Layer • - Optimized for fast indexing, retrieval, and scalability
- 6. What are Vector Embeddings? • - Numerical representations of data (text, images, etc.) in high-dimensional space • - Preserve semantic or contextual meaning
- 7. Vector Embedding Techniques • - Text: Word2Vec, GloVe, BERT, OpenAI Embeddings • - Images: CNN features, ResNet, CLIP • - Audio: Spectrogram-based embeddings
- 8. Similarity Search Techniques • - Brute Force Search (Linear Scan) • - Approximate Nearest Neighbor (ANN) techniques: • * HNSW • * FAISS • * Annoy • * ScaNN
- 9. Applications in the Modern World • - Recommendation Systems • - Semantic Search • - Image and Video Search • - Fraud Detection • - Personalized Content Delivery • - Chatbots and Virtual Assistants