Vector databases are so hot right now. WTF are they?

Introduction

As of April 7, 2023, the Vector database industry is buzzing with recent funding rounds for companies like Weeviate, Pinecone DB, and Chroma. These companies are at the forefront of utilizing vector databases to store and query complex objects like words, sentences, images, and audio files in high-dimensional spaces called embeddings. But what exactly are vector databases, and how do they work?

A vector is essentially an array of numbers that can represent various data types in a continuous space. Embeddings, which are created using vectors, map the semantic meaning of objects together based on similarity. Vector databases store these embeddings and allow for quick and efficient querying, making them ideal for AI-driven applications.

In this article, we'll delve into the world of vector databases, exploring their functionality, usage in AI applications, and the current landscape of the industry.

Keywords

• Vector databases
• Embeddings
• AI applications
• Funding rounds
• Industry landscape

FAQ

Q: What is a vector database and how does it differ from traditional databases? A: A vector database stores arrays of numbers clustered based on similarity, allowing for efficient querying of complex objects represented as vectors. This differs from traditional databases like relational or document databases, as it focuses on high-dimensional embeddings.

Q: How are vector databases utilized in AI applications? A: Vector databases play a crucial role in AI applications by storing embeddings that capture semantic similarities between objects. These embeddings can be used for recommendation systems, search engines, text generation, and extending large language models with long-term memory.

Q: What are some popular vector database options in the market today? A: Companies like Weeviate, Pinecone DB, and Chroma offer vector database solutions, each with its own unique features and capabilities. Open-source options like Weeviate and Milvus cater to developers looking for flexible solutions, while Pinecone and Chroma provide more specialized offerings for specific use cases.

Q: How do vector databases integrate with large language models like GPT? A: Vector databases can extend large language models like GPT by providing long-term memory and context. By storing relevant data in vector form, these databases can enhance the performance of AI models by enabling personalized responses and retrieval of historical information.