Vector databases are the long term memory of your future AI projects. ChatGPT and all the AI bots are cool and all, but what if you could use this technology on your own data? A ChatGPT style interface that could read your company’s knowledge bases and assist your employees without being publicly available. Sound interesting? Now you understand why I am spending more and more time on this technology. No one is going to want to allow Microsoft, Amazon or Google’s systems to access their data. But what if you could use your implementation of their cloud resources (Azure, AWS, GCP) to house databases to index data for your own internal large language model (LLM). This is where you would need a vector database. I will have follow-up posts on the other parts of that solution, but today databases.
Vector Databases: The Next Generation of Data Storage
The way we store and process data is changing. Traditional relational databases are no longer sufficient for the demands of today’s data-driven world. Vector databases are a new breed of database that are designed to store and process large amounts of unstructured data.
A vector database is a type of database that stores data as high-dimensional vectors. Each vector has a certain number of dimensions, which can range from tens to thousands, depending on the complexity and granularity of the data. For example, a vector database could be used to store the text of a document, where each dimension represents a different word in the document.
Vector databases for AI and LLM
In today’s data-driven business environment, companies are collecting and analyzing large amounts of data to gain insights and make informed decisions. The data types supported by vector databases are commonly used in machine learning and artificial intelligence applications and especially large language models (LLM). Vector databases differ from traditional databases in that they are designed to store and query vector data efficiently. They also support complex data types such as matrices, tensors, and graphs. This makes them a valuable tool for companies that need to store and analyze large amounts of complex data.
Other Use Cases
Vector databases are used in a variety of industries. In finance, vector databases are used for fraud detection, risk analysis, and algorithmic trading. In healthcare, they are used for medical diagnosis, drug discovery, and personalized medicine. In retail, they are used for product recommendations, personalized marketing, and customer segmentation. The ability to store and analyze vector data efficiently is critical in these industries because the data is often high-dimensional and complex.
Vector databases are also being used in a variety of other fields , including:
Natural language processing: Used to improve the performance of natural language processing (NLP) tasks, such as machine translation and text summarization.
Computer vision: Used to improve the performance of computer vision tasks, such as object detection and image classification.
Recommendation systems: Used to improve the performance of recommendation systems, such as those used by Netflix and Amazon.
Summary on Vector Databases for LLM
Vector databases are an emerging technology that allows companies to store and analyze complex vector data with greater efficiency than traditional databases. They are particularly valuable for machine learning and artificial intelligence applications, which require high-dimensional and complex data. As companies continue to rely on AI and machine learning technologies, vector databases will become increasingly important for managing and analyzing large amounts of data.
Where will you store your LLM Data?
I think there will be a lot of concern around using hosted solutions for the level of data that will be in your LLM / AI models. Public Cloud, self hosted databases, are a great alternative to fully hosted solutions. This will enable scalable infrastructure while also maintaining the control needed for this sensitive data. Kinect Consulting’s guidance can assist you as you build on these emerging technologies.
