Introduction

In recent years, vector databases have taken the tech industry by storm. These databases, such as Pinecone DB, Chroma, and Weaviate, have attracted significant funding and attention due to their ability to store and query complex objects represented as vectors. In this article, we will explore what vector databases are, how they work, and their impact on various AI-driven applications.

What is a Vector Database?

A vector database is a type of database that stores and retrieves arrays of numbers, known as vectors, based on their similarity. Vectors, in this context, are arrays of numbers that can represent various objects like words, sentences, images, or audio files in a high-dimensional space called an embedding. Think of embeddings as a way to map the semantic meaning or similar features of objects together.

Key Points:

  • Vectors are arrays of numbers that can represent complex objects.
  • Embeddings map the semantic meaning or similar features of objects together.
  • Vector databases store and query vectors based on similarity.

Use Cases of Vector Databases

Vector databases have gained popularity due to their applicability in various AI-driven applications. Some major use cases of vector databases include:

1. Recommendation Systems

Recommendation systems rely on understanding the similarities between different objects, such as products or content, to provide personalized recommendations. Vector databases, with their ability to store and query vectors based on similarity, offer an ideal solution for powering recommendation systems.

2. Search Engines

Search engines often benefit from the semantic meaning of words or objects. By leveraging vector databases’ capability to store embeddings, search engines can better understand the similarities between search queries and indexed objects, resulting in more relevant search results.

3. Text Generation

Text generation models, like ChatGPT, rely on embeddings to generate coherent and contextually relevant responses. Vector databases facilitate the storage and retrieval of embeddings, allowing text generation models to retrieve relevant historical data and customize responses.

Several vector databases have emerged in recent years, catering to different needs and preferences. Let’s take a look at some prominent examples:

1. Pinecone

Pinecone is a highly popular vector database known for its efficiency and ease of use. Although not open source, Pinecone offers a robust solution for storing and querying vectors, making it a preferred choice for many developers.

2. Chroma

Chroma is an open-source vector database built on top of ClickHouse. It provides ultra-low latency querying capabilities, making it suitable for real-time AI applications.

3. Weaviate

Weaviate is another open-source vector database written in Go. It offers efficient indexing and querying of vectors, making it well-suited for AI-driven applications.

Integration with AI Models

One of the reasons vector databases have gained significant attention is their ability to extend language models (LLMs) with long-term memory. By combining a general-purpose model like OpenAI’s GPT-4 or Google’s Lambda with a vector database, developers can enhance the model’s capabilities.

Steps to Extend LLMs with Vector Databases:

  1. Start with a pre-trained, general-purpose language model.
  2. Incorporate the model with a vector database to store relevant data points.
  3. When a user request is made, use the vector database to retrieve relevant documents from the stored data.
  4. Update the model’s context based on the retrieved data, resulting in more personalized and contextually relevant responses.
  5. The integration of vector databases with tools like Link Chain allows for combining multiple LLMs, further enhancing the model’s performance.

The Future of Vector Databases

As the demand for AI applications continues to grow, vector databases are expected to play a crucial role in powering these applications. Their ability to store and query complex embeddings opens up new possibilities for personalized recommendations, improved search engines, and advanced text generation models.

Conclusion

Vector databases have revolutionized the way we store and query complex objects in the AI era. By representing objects as vectors and leveraging the similarity-based querying capabilities of vector databases, AI-driven applications can achieve better performance and customization. With the increasing popularity of vector databases like Pinecone, Chroma, and Weaviate, we can expect further innovations in this space and advancements in AI applications.

Keywords: vector databases, embeddings, similarity, AI applications, recommendation systems, search engines, text generation, Pinecone, Chroma, Weaviate, LLMs, long-term memory, integration, future

Note: This article is a transcription of the YouTube video titled “Vector databases are so hot right now. WTF are they?” by Fireship.