A Comprehensive Guide to Vector Stores in Generative AI

Author(s): Mdabdullahalhasib

Originally published on Towards AI.

Understand the concept of Vector Store, Importance & Use Cases, Various Vector Store provides, and implementation with LangChain

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Traditional databases (MySQL, PostgreSQL, MongoDB, and others) work with structured queries and exact matching on predefined keys or values. But, They struggle with semantic understanding which means can’t capture the meaning behind data.

Finding similar documents, images, audio or any unstructured data is hard in traditional databases. They can’t adopt high-dimensional data for performing different operations.

Vector databases have come into place to overcome these issues. Let’s dive deep into this topic.

What are Vector Databases and Use Cases of it?Basic Elements of Vector DatabasesVector Database in RAGDifferent Types of Vector Databases & Implementation with LangChainWhich Vector Database I should Choose?

Today we will cover these topics and expect that you will get a comprehensive idea of vector database/store to apply in your projects.

A vector store is a database that is designed to store and manage vector embeddings. Vector Embeddings are high-dimensional numerical representations of any type of data such as text, images, audio, video, or others. It stores both content & vector embeddings.

Vector databases can capture the semantic meaning of data. It is useful for similarity searches by enabling an efficient retrieval system. It extracts the… Read the full blog for free on Medium.

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Published via Towards AI