Why are Vector Databases so Hot in Today's AI Landscape?

In this talk, we delve into the concepts and applications of vector databases. We also explore their significant role in advancing AI applications.

References & Citation:
Roie Schwaber-Cohen, What is a Vector Database?
Arvind Neelakantan, Lilian Weng, Boris Power, Joanne Jang, Introducing text and code embeddings
Dale Markowitz, Meet AI’s multitool: Vector embeddings
Sam Partee, Vector Embeddings: From the Basics to Production
Tuhin Mukherjee, Different types of Distances used in Machine Learning Explained!
ritvikmath on YouTube, Every Distance in Data Science
Dmitry Kan, Not All Vector Databases Are Made Equal
Prashanth Rao, Vector Databases,
Part 1: What makes each one different?
Part 2: Understanding their internals
Part 3: Not all indexes are created equal
Christian Bagaya & Saeedeh Afshari, Recommending news articles using Vertex AI Matching Engine
Fatih Karabiber, Cosine Similarity
Vector Databases Reviewed:
Pinecone, Milvus, Weaviate, Chroma, Qdrant, LanceDB, Vespa, Vald

Script used based on @samwitteveenai's video LangChain Retrieval QA with Instructor Embeddings & ChromaDB for PDFs