What is a Vector Database? Powering Semantic Search & AI Applications

Summary: The video discusses vector databases and their utility in storing and retrieving unstructured data. Traditional relational databases are limited in their ability to represent the semantic context of data, leading to what is known as the semantic gap. Vector databases, on the other hand, represent data as mathematical vector embeddings, enabling similarity searches and capturing the nuanced meanings of items, making them ideal for handling various types of unstructured data like images, text, and audio.

Keypoints:

Introduction to the concept of vector databases using an example of a digital image.

Traditional relational databases can store binary data and basic metadata but struggle with semantic context.

The semantic gap refers to the disconnect between computer data storage and human understanding.

Vector databases represent data as mathematical vector embeddings, making it easier to perform similarity searches.

Vector embeddings are arrays of numbers capturing the semantic essence of data, with similar items close together in vector space.

Unstructured data types such as images, text, and audio can be transformed into vector embeddings for storage in vector databases.

The video explains how to interpret vector embeddings using the mountain image’s dimensions as an example.

Embedding models, trained on vast datasets, create vector embeddings; examples include Clip for images and GloVe for text.

Higher-level layers of embedding models capture more abstract features of the data.

Vector indexing, using approximate nearest neighbor algorithms, improves search speed for millions of vectors stored in a database.

Methods like HNSW and IVF trade slight accuracy for faster search results in vector databases.

Vector databases are integral to retrieval-augmented generation (RAG), retrieving relevant text chunks to enhance responses from language models.