Artificial intelligence

Vector database is a new type of database for the age of AI

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Businesses in all industries increasingly understand that data-driven decision making is a necessity to be competitive now, in the next five years, in the next 20 and beyond. The growth of data – especially the growth of unstructured data – is out of the ordinary, and recent market research estimate the artificial intelligence (AI) The data-driven market “will grow at a compound annual growth rate (CAGR) of 39.4% to reach $422.37 billion by 2028.” There is no turning back from the flood of data and the era of AI that is upon us.

Implicit in this reality is that AI can sort and process the flood of data in meaningful ways – not just for tech giants like Alphabet, Meta and Microsoft with their huge R&D operations and custom AI tools, but for the medium-sized business and even SMEs.

Well-designed AI-based applications sift through hugely large datasets extremely quickly to generate new insights and ultimately fuel new revenue streams, creating real business value. But nothing of data growth is truly operationalized and democratized without the newcomer: vector databases. These mark a new category of database management and a paradigm shift for utilizing the exponential volumes of untapped unstructured data in object stores. Vector databases offer a mind-numbing new level of capability to search for unstructured data in particular, but can also process semi-structured and even structured data.

Unstructured data – such as images, video, audio, and user behaviors – generally do not fit the relational database model; it cannot be easily sorted into row and column relationships. Extremely time-consuming and haphazard methods of managing unstructured data often boil down to manually tagging data (think tags and keywords on video platforms).


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Tags can be filled with not-so-obvious classifications and relationships. Manual tagging lends itself to traditional lexical searching that matches words and strings exactly. But a semantic search that understands the meaning and context of an image or other unstructured piece of data, along with a search query, is nearly impossible with manual processes.

Enter embedding vectors, also known as embedding vectors, feature vectors, or simply embeddings. They are numeric values ​​- kinds of coordinates – representing objects or characteristics of unstructured data, such as a component of a photograph, part of a person’s shopping profile, selected images in a video, geospatial data or anything that doesn’t fit neatly into a relational database table. These integrations enable scalable “similarity search” in a fraction of a second. This means finding similar items based on closest matches.

Quality data — and insights

Integrations essentially appear as a computational by-product of an AI model, or more accurately, a machine or deep learning model trained on very large quality input datasets. To divide the important hair a little further, a model is the calculation production a machine learning (ML) algorithm (method or procedure) performed on data. Sophisticated and widely used algorithms include STEGO for computer vision, CNN for image processing and Google’s BERT for natural language processing. The resulting models transform each piece of unstructured data into a list of floating-point values ​​- our search-enabled integration.

Thus, a well-trained neural network model will produce embeddings that align to specific content and can be used to perform semantic similarity search. The tool for storing, indexing and searching these incorporations is a vector database — specifically designed to manage incorporations and their distinct structure.

What’s market-critical is that developers anywhere can now add Vector Database, with its production-ready capabilities and lightning-fast search for unstructured data, to desktop applications. AI. These are powerful applications that can help a business achieve its business goals.

Vector database strategy starts with use cases that make sense for your business

It’s increasingly common for a company’s overall data strategy to include AI, but determining which business units and use cases will benefit the most is critical. AI applications based on vector databases can analyze large unstructured data for marketing, sales, research and security purposes. Recommender systems – including recommendation of user-generated, personalized content e-commerce search, video and image analysis, targeted advertising, anti-virus cybersecurity, chatbots with enhanced language skills, drug discovery, protein search, and anti-bank fraud detection – are among the first important use cases well managed by vector databases with speed and accuracy.

Consider an e-commerce scenario where hundreds of millions of different products are available. An app developer building a recommendation engine wants to be able to recommend new types of products that appeal to individual consumers. Integrations capture profiles, products, and search queries, and searches will produce nearest-neighbor results, often aligning with consumer interests in an almost bizarre way.

Opt for purpose-built, open-source software

Some technologists have extended traditional relational databases to support embeddings. But this unique approach of adding a table of “vector columns” is not optimized for handling embeddings and therefore treats them like second-class citizens. Businesses benefit from purpose-built open-source vector databases that have matured to the point of offering higher-performance search of larger-scale vector data at a lower cost than other options.

These purpose-built vector databases should be designed to easily incorporate new indexes for emerging application scenarios and support flexible scalability to multiple nodes to accommodate ever-increasing data volumes.

When companies adopt an open source strategy, their developers see everything that happens with a tool. There are no hidden lines of code. There is community support. Milvus, a Linux Foundation AI and data project, for example, is a well-known vector database of choice among companies and easy to try out due to its dynamic open source development. It’s easier to envision it in a larger AI ecosystem and build integrated tools for it. Multiple SDKs and an API make the interface as easy as possible so developers can quickly integrate and test their ideas that use unstructured data.

Overcome the challenges ahead

Big new, paradigm-shifting technologies inevitably pose some challenges – both technical and organizational. Vector databases can search billions of embeds and their indexing is technically different from that of relational databases. Not surprisingly, developing vector indexes requires specialized expertise. Vector databases are also computationally heavy, given their genesis of AI and machine learning. Solving their large-scale IT challenges is an area of ​​continuous development.

Organizationally, helping business teams and management understand why and how vector databases are useful to them remains a key part of standardizing their use. Vector research itself has been around for a while, but on a very small scale. Many companies aren’t really used to having access to the kind of data search and data mining power that modern vector databases offer. Teams may not know where to start. So getting the message out about how they work and why they bring value remains a top priority for their creators.

Charles Xie is CEO of Zilliz


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