Vector Databases. From old tech to industry standard

Understanding Vector Databases

Vector databases have become increasingly significant with the rise of large language models and generative AI. They provide a solution for managing unstructured data that doesn't easily fit into traditional relational databases like Postgres or MySQL.

Introduction to Vector Databases

Take a normal 2-dimensional system of coordinates. Then we could name the axes and sort objects based on these parameters. Now, if you wanted to position a Rhino along those axes, you could estimate that it is grey and big, so somewhere close to the elephant.

Now in real life, we need many more parameters. If you want to train a model for animal recognition in a jungle, you'd need parameters for closeness to a tree, speed of movement, and so on.

So the parameters for a Rhino would evolve to look more like this:

Size [0.7]

Color [0.6]

Speed [0.4]

....

Eyes [0.9],

Now this is many more parameters than a simplified 2D drawing can hold. To store this, we consider each parameter's vector representation. Of course comparing thousands of parameters, even conveniently packed as vectos, is not too fast. But computer science comes to the rescue with heuristic approaches. They allow us to find close matches very quickly, even if this result is not perfectly deterministic - simply put we would get the answer in the top 10% of choices 90% of the time. That's enough for LLMs so we can feed it an analysis of multiple objects with parameters important to us only - and we've got ourselves a much more precise tool than initially. Here is a good article if you crave the full technical details.

Importance of Vector Embeddings

Vector embeddings are the backbone of vector databases, which are the industry standard. They convert data into numerical vectors that represent the semantic meaning of the data. This process allows machine learning models to understand and interpret the relationships between different data points.

One of the key advantages of vector embeddings is their ability to handle unstructured high-dimensional data like images, videos, emails, and social media posts - improving inference. These types of data don't fit well into the rigid schemas of relational databases. By converting this unstructured data into vectors, vector databases enable more effective and efficient data retrieval and analysis. Traditional databases struggle with high-dimensional data because they rely on fixed schemas. In contrast, vector databases can efficiently manage and query high-dimensional vectors, making them the go-to tool for applications that require complex data analysis.

How to create vector embeddings?

UAE large V1

UAE-Large-V1 is an AI Model that shines by generating detailed and accurate embeddings that capture the underlying structure and relationships within your data. These high-quality embeddings empower machine learning models to perform significantly better in various tasks. The API for UAE-Large-V1 offers a wide range of functionalities, allowing you to generate embeddings for individual data points or process massive datasets. This flexibility makes it easy to integrate UAE-Large-V1 into different stages of your machine learning workflow, from data pre-processing and feature engineering to model training and evaluation. Here is a good tutorial also using UAE-Large-V1 in a RAG creation process.
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Author: Sergey Nuzhnyy.