What is a Vector Database? Exploring AI Data Technology

In today’s era of big data, retrieving and analyzing information from unstructured data (such as text, images, or video) using traditional databases has many limitations. Querying this data requires heavy computation and doesn’t achieve the desired efficiency due to limitations in structure and storage methods. Vector databases were created to solve these issues. Let’s explore how they work!

1. Concept and Core Technology

A vector database is a type of database designed to store and manage “vector embeddings” (a method of converting data types into numbers to extract their semantics and relationships).

Unlike traditional relational databases that store data in a series of values in tables, rows, and columns, a vector database uses the core technology of “vector embeddings” to store data as vectors. Embeddings are representations of data as vectors in a multi-dimensional space, where similar or related attributes are stored near each other in spatial terms.

(Source: Pinecone)

This allows machine learning models and advanced search algorithms to assess the similarity or difference between query items based on content similarity rather than exact matches. This is a significant benefit for AI applications like image search or recommendation systems, where content and semantics are central to the search intent.

(Source: Weaviate)

Google’s documentation states:
“In the future, we believe that every database will be a vector database.”

2. Benefits and Real-World Applications of Vector Databases

With increasing data generated from sources like social media and IoT (Internet of Things) devices, the scalability of vector databases becomes the ideal solution for big data analytics and processing tasks.

Vector databases enable fast, accurate querying with complex queries, especially in handling big and unstructured data. This makes them ideal for applications requiring fast retrieval, such as semantic search, recommendation systems, and image recognition…

A notable example of the application of vector databases is in visual search in e-commerce platforms. For example, a customer may upload an image of an item they want to buy, and the system will use the vector database to accurately search for and recommend similar or related products. This is achieved by comparing the embeddings of the uploaded image with the product images stored in the database.

3. Tools and Technologies

There are several popular vector databases such as Pinecone, Faiss, and Milvus, each with its own unique features suited for the specific needs of various applications.

For those just getting started, setting up a vector database may involve selecting suitable software (like Milvus or Pinecone), installing it on a server or cloud environment, and then importing data converted into vector format into the database. Then, you can begin constructing queries to perform similarity searches or other analyses.

Additionally, Google’s AlloyDB AI, recently launched, provides excellent support for storing vector data with very fast query speeds.

(Source: Google)

Using Google’s AlloyDB is an effective solution for managing and analyzing large datasets. I tried creating an instance on Google Cloud Platform (GCP) to process and analyze product reviews data. After setting up the AlloyDB instance, I imported the product review data. To enhance analysis, these review comments were converted into vector format using the “textembedding-gecko” text embedding model.

Using these vectors allows for more accurate and efficient querying, enabling deeper analysis of customer sentiment and feedback on products, thereby supporting smarter business decisions.

4. Conclusion

Advances in computing technology and AI remain a hot topic that developers and data analysts continue to explore to expand their application capabilities. Among them, vector databases are becoming an indispensable part of the data infrastructure for many large companies, promising a future where data retrieval and analysis are done faster and more accurately than ever before.

We hope this article provides you with an overview of vector databases and why they are becoming increasingly popular in the tech world.

Biện Hoàng Thy PHP Developer