Vector databases arrived on the scene a few years ago and enhance the capacity of today’s search engines, image recognition tools, recommendation systems, and several other tools. As generative tools like Bard and ChatGPT gain traction, the term 'vector databases' is popping up everywhere.
As large language models fuel the AI revolution, vector databases are emerging as crucial tools due to their unique ability to perform fast and accurate similarity searches on high-dimensional vectors. This has sparked a surge in investment, with companies like Pinecone and Milvus raising millions to develop and scale their vector database solutions, positioning them as key players in the next wave of AI innovation.
So, what exactly are these vector databases, and how do they differ from traditional ones? This article will focus on the key concepts related to vector databases. We will specifically highlight some of the best vector databases in the blog.
A vector database stores complex data as mathematical representations in a multi-dimensional vector space. This is referred to as vector embedding. The vector embedding is generated through machine learning models. These vectors capture the semantic relationships and similarities between data, making them incredibly useful in machine learning and artificial intelligence.
As AI takes center stage for major companies, traditional databases struggle to handle the complex and often unstructured data of images, text, audio, and video. This has ignited a need for crucial tools like vector databases, which excel at efficiently storing and retrieving these complex data types.
Whether you are developing a large language model or utilizing pre-trained models, Vector databases can provide long-term memory and can store and retrieve from multi-dimensional vectors.
They can deliver better performance than traditional ones when it comes to handling high-dimensional data for performing complex similarity searches, pattern recognition, etc.
Data comes in various forms, from text and images to videos and audio. Today's AI models are trained on and designed to handle this increasingly unstructured data. Vector databases bridge the gap by converting these datasets into mathematical representations called vector embeddings. This allows for efficient storage, retrieval, and manipulation of unstructured data.
Vector embedding is what makes these databases highly potent for working with unstructured data. As stated above, these vector embeddings are an array of numbers that are generated through embedding or machine-learning models.
The image below demonstrates how it works:
Once our data is converted into vector embeddings, the data is organized in a multi-dimensional vector space for efficient similarity search. The similarity search is a central concept that involves finding the most similar vectors using distance metrics such as Euclidean distance, Cosine similarity, etc.
The below image represents a vector space; when the query is converted to vectors, the database computes the similarity between the search query and the collection of data points. For example, the vector for bananas (both the text and the image) is located near apples and not cats.
Have you ever wondered how your favorite streaming platform recommends the perfect movie for you or how search engines understand the nuances of your queries, even when they involve multiple meanings?
Take a look at the following example: how Google can differentiate between searches for "apple taste" and "apple valuation."
The big drawback with traditional databases is that they rely on keyword matching. In other words, traditional databases might not understand the intent behind keywords. On the other hand, vector databases can understand the semantic relationship encoded in vector embeddings.
Due to their efficient handling of complex datasets, vector databases are ideal solutions for developing various systems, including:
It is an open-source vector database that stores both vectors and objects. This allows developers to handle both structured and unstructured data in one place, unique from other databases.
Qdrant's API allows easy integration with your preferred programming language. It lets you either build your own code for interaction with API or utilize pre-built libraries for simpler implementation. This cloud-native platform utilizes the HNSW algorithm for accurate nearest-neighbor search, ensuring fast and reliable results.
In this article, we've cracked the code on vector databases and explained what they are, how they work, and why they're crucial in the AI revolution. The rise of AI and machine learning, along with large language models, will propel the growth of databases as surely as the future of the upcoming tech era.
If you are struggling with complex datasets like text, images, and code, and you want to provide a more personalized and engaging experience to your customers, consider entering the world of vector databases to enhance your business.
Brilworks is a renowned company that provides cost-effective AI-powered solutions, from consultation to deployment, so businesses can leverage cutting-edge technology to improve their services. Contact us today if you are looking for cost-effective AI solutions.
Vector databases are modern databases used to store, index, and retrieve high-dimensional data points. These data points are referred to as vectors. These vectors store data in a multi-dimensional space, where each dimension represents specific features or characteristics.
Traditional databases are not suitable for handling complex or unstructured data, leading to slow and inaccurate searches. In contrast, vector databases excel in searching and understanding semantic nuances, making them an excellent choice for modern AI applications.
Vector databases can be utilized in the development of large language models, computer vision, recommendation systems, fraud detection, genomics, etc, and employed across other industries to develop cutting-edge modern applications.
They are relatively new, and setting up these databases can be challenging compared to traditional databases. Additionally, they are still evolving and haven't been thoroughly battle-tested, indicating that they are yet to become a crucial component in the upcoming AI era. The relevance of these databases heavily depends on the quality and relevance of the data used in training.
The popular vector databases include Pinecone, Chroma, Qdrant, Weaviate, and others.
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