AI Readiness: Using vector databases to catalog large language models

As businesses work toward integrating Artificial Intelligence into their operations, developers are increasingly excited about the potential of vector databases to fast-track AI readiness. Vector databases offer alternatives to data management, providing teams with a more streamlined, organized indexing process. 

We’ve compiled a comprehensive overview of vector databases. Let’s examine how vector databases can help your organization prepare for the AI revolution. 

What are vector databases? 

Vector databases store, manage, and index large quantities of high-dimensional vector data. When working with large language models, data is vector embeddings. Organizing, indexing, and accessing data in vector form is more challenging than the typical, scalar-based databases. 

The kind of data required for generative AI apps like Picasso GPT, or video and audio tools, requires the input of massive amounts of complex data; images, audio, and video data. This is represented as vector data. 

Unlike traditional relational databases with rows and columns, data points in a vector database are represented by vectors with a fixed number of dimensions clustered based on similarity. This design enables low-latency queries, making them ideal for AI-driven applications.

Vector databases versus traditional databases  

With the proliferation of artificial intelligence comes the transformation of data. Data is increasingly unstructured and is between 30 to 60% annually. If these large data points (audio clips, video footage, images) were used to train machine learning algorithms, the data would need to be input. Using a traditional database to store, manage, and prepare the data for AI would be labor-intensive and highly inefficient. 

This is where vector databases really shine. They’re best suited for unstructured datasets through high-dimensional vector embeddings.

What are vector embeddings? 

The volume of unstructured datasets your organization needs for AI will only continue to grow, so how do you handle millions of vectors? This is where vector embeddings and vector databases come into play. These vectors are represented in a continuous, multi-dimensional space known as an embedding, which is generated by embedding models, specialized to convert your vector data into an embedding. Vector databases serve to store and index the output of an embedding model. Vector embeddings are a numerical representation of data, grouping sets of data based on semantic meaning or similar features across virtually any data type.  

For example, take the words “car” and “vehicle.” They both have similar meanings even though they are spelled differently. For an AI application to enable effective semantic search, vector representations of “car” and “vehicle” must capture their semantic similarity. When it comes to machine learning, embeddings represent high-dimensional vectors that encode this semantic information. These vector embeddings are the backbone of recommendations, chatbots and generative apps like ChatGPT.  

How vector embeddings and vector databases work 

Enterprise vector data can be fed into an embedding model such as IBM’s watsonx.ai models or Hugging Face. These are specialized to convert your data into an embedding by transforming complex, high-dimensional vector data into numerical forms that computers can understand. These embeddings represent the attributes of your data used in AI tasks such as classification and anomaly detection.

Vector storage

Vector databases store the output of an embedding model algorithm, the vector embeddings. They also store each vector’s metadata, which can be queried using metadata filters. By ingesting and storing these embeddings, the database can then facilitate fast retrieval of a similarity search, matching the user’s prompt with a similar vector embedding. 

Vector indexing

Storing data as embeddings isn’t enough. The vectors need to be indexed to accelerate the search process. Vector databases create indexes on vector embeddings for search functionality. The vector database indexes vectors using a machine-learning algorithm. Indexing maps vectors to new data structures that enable faster similarity or distance searches, such as nearest neighbor search between vectors.

Figure depicting vector embeddings

Image sourced from elastic.co

Benefits

While it’s clear that vector database functionality is rapidly growing in interest and adoption to enhance enterprise AI-based applications, the following benefits have also demonstrated business value for adopters: 

Speed and performance: Vector databases use various indexing techniques to enable faster searching. Vector indexing, along with distance-calculating algorithms such as nearest neighbor search, is particularly helpful in searching for relevant results across millions, if not billions, of data points, with optimized performance. 

Scalability: Vector databases can store and manage massive amounts of unstructured data by scaling horizontally, maintaining performance as query demands and data volumes increase.

Cost of ownership: Vector databases are a valuable alternative to training foundation models from scratch or fine-tuning them. This reduces the cost and speed of inferencing of foundation models.

Flexibility: Whether you have images, videos, or other multi-dimensional data, vector databases are built to handle complexity. Given the multiple use cases ranging from semantic search to conversational AI applications, the use of vector databases can be customized to meet your business and AI requirements. 

Long-term memory of LLMs: Organizations can start with general-purpose models like IBM watsonx.ai’s Granite series models, Meta’s Llama-2, or Google’s Flan models. They can then provide their own data in a vector database to enhance the output of the models and AI applications critical to retrieval-augmented generation. 

Data management components: Vector databases also typically provide built-in features to easily update and insert new unstructured data.

Fast similarity search: Vector databases can quickly find the most similar data points to a given query point. This is because they use specialized indexing and search algorithms that are optimized for high-dimensional data.

Efficiency: Vector databases are designed to be efficient in terms of both storage and query performance. This is important for applications that need to process large amounts of data on a budget.

Best Use Cases for Vector Databases

Vector databases are well-suited for a variety of applications, including:

Recommendation systems:

Vector databases can be used to recommend products, movies, and other items to users based on their past preferences.


Natural language processing: 

Vector databases can be used to extract meaning from text and code. This can be used for tasks such as sentiment analysis, machine translation, and question-answering.

Computer vision:

Vector databases can be used to identify objects, classify images, and track motion. This can be used for tasks such as autonomous driving, facial recognition, and video surveillance.

Fraud detection:

Vector databases can be used to identify fraudulent transactions by comparing them to known patterns of fraud.

Anomaly detection:

Vector databases can be used to identify anomalies in data, such as suspicious activity or system failures. Vector databases are a powerful tool for a wide range of applications that require fast and accurate similarity search. If you are looking for a database that can handle high-dimensional data, a vector database is a good option to consider.

Data types and databases – A visual summary

To wrap up this overview, we’ve put together a table to illustrate vector databases’ versatility and how they can help your organization get AI-ready.