Vector databases overview

1. Vector Databases
An Overview of Vector Databases and
Their Applications

2. What are Vector Databases?
• • Specialized databases for high-dimensional
vector data.
• • Enable efficient similarity search and
retrieval.
• • Used in AI/ML applications like image
search, NLP, recommendations.

3. Why Do We Need Vector
Databases?
• • Traditional databases are inefficient for high-
dimensional data.
• • Need for semantic search, fast similarity
matching.
• • Applications in real-time personalization and
intelligent systems.

4. How Vector Databases Work
• • Store vector embeddings instead of scalar
values.
• • Use similarity metrics like cosine similarity
and Euclidean distance.
• • Search retrieves nearest neighbors of a
query vector.

5. Architecture of Vector Databases
• • Components:
• - Vector Indexing Engine
• - Metadata Store
• - API & Query Interface
• • Designed for scalability, indexing, and
efficient retrieval.

6. What are Vector Embeddings?
• • Numerical representation of data (text,
images, audio).
• • Preserve semantic/contextual relationships.
• • Enable machine understanding of
unstructured data.

7. Vector Embedding Techniques
• • Text: Word2Vec, GloVe, BERT, OpenAI
Embeddings
• • Image: CNN, ResNet, CLIP
• • Audio: Spectrogram-based embeddings

8. Similarity Search Techniques
• • Brute Force (linear scan): Accurate but slow.
• • ANN Methods:
• - HNSW (Hierarchical Navigable Small World)
• - FAISS (Facebook AI)
• - Annoy (Spotify)
• - ScaNN (Google)

9. Applications in the Modern World
• • Recommendation Engines
• • Semantic Search
• • Image and Video Retrieval
• • Personalized Content Delivery
• • Fraud Detection
• • Virtual Assistants and Chatbots

10. Thank You
• • Questions?
• • Let's discuss more about use-cases and
implementations!