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Last updated on November 17, 2023

We’re living in the age of AI. This technology has touched practically every industry, bringing about breakthroughs and, at the same time, introducing new challenges. Efficient data processing is very important for applications that involve AI/ML.

Today, they all rely on vector embeddings, which is a type of data representation that contains semantic data that AI engines need to develop knowledge and retain long-term memory used for performing complicated tasks.

AI models create embeddings that include a massive number of properties or features, making their representation difficult to manage. In the context of AI and machine learning, these characteristics represent the aspects of data that are critical for identifying patterns, correlations, and underlying structures.

That is why data practitioners require a special kind of database developed exclusively for dealing with this sort of data. This is where vector databases come in.

What is a vector database?

Traditional relational databases store strings, integers, and other data in rows and columns. When you query conventional databases, you look for rows that match your query. Vector databases, on the other hand, deal with vectors rather than strings and other such elements.

Vector databases are purpose-built to manage this sort of data while also providing the performance, scalability, and flexibility teams need to get the most out of this type of data. To allow rapid and reliable retrieval of high-dimensional vectors, such databases rely on sophisticated indexing and search algorithms.

Vector databases deliver efficient storage and query capabilities for the unique structure of vector embeddings. They open the door to simple search, high speed, scalability, and data retrieval by discovering similarities. 

Up until now, vector DBs were used by large organizations that had the resources to both create and manage them. Since vector databases are costly, teams need to make sure that they’re correctly calibrated to deliver excellent performance. 

Advantages of vector databases

  • Data type – unlike traditional relational databases like PostgreSQL, which stores tabular data in rows and columns, or NoSQL databases which store data in JSON documents, vector databases are only designed to handle one form of data: vector embeddings.
  • Scalability – vector DBs are built to manage massive amounts of data. They’re great for large-scale machine-learning applications because they can store and explore billions of high-dimensional vectors.
  • High-speed search performance – vector DBs use advanced indexing algorithms to enable the quick retrieval of related vectors in the vector space, even in large-scale datasets.
  • Similarity searches – vector DBs may do similarity searches to determine the greatest match between a user’s prompt and a certain vector embedding. This feature is very beneficial in the deployment of Large Language Models, where vector databases may hold billions of vector embeddings signifying substantial training.
  • Flexible data models – vector DBs They can handle both structured and unstructured data, making them useful for a wide range of applications, including text and picture searches and recommendation systems.
  • Managing high-dimensional data – dimensionality reduction methods are used to compress high-dimensional vectors into lower-dimensional spaces while retaining important information. As a result, they are efficient in terms of storage and computing.

How does a vector database work?  

A vector database indexes vectors, among other things. To understand how a vector database works, let’s take a look at the example of a large language model like GPT-4. This model contains a massive volume of data with a great deal of substance.

Here’s a sequence of steps that happen when you interact with a GPT4-powered application:

  1. You enter a query into the application as the user.
  2. The query is sent into the embedding model, which generates vector embeddings depending on the material you want to index. 
  3. The vector embedding is stored in the vector database, along with the content from which it was created. 
  4. The vector database generates the output and returns it to the user as a query result. 
  5. When the user makes further queries, it will use the same embedding model to generate embeddings to query the database for comparable vector embeddings. The similarities between vector embeddings are based on the original material from which the embedding was constructed. 
  6. Because the answers are dependent on how close or approximate they are to the query, the major factors here are accuracy and speed. The slower the query output, the more accurate the result. 

All in all, a vector database query goes through three major stages:

  • Indexation – Once vector embeddings are in the vector database, a number of techniques are used to translate a given vector embedding to data structures for a quicker search. 
  • Inquiry – After completing its search, the vector database compares the query vector to the indexed vectors, using the similarity metric to determine its nearest neighbor. 
  • Post-production – Depending on the vector database, the final nearest neighbor will be post-processed to generate a final output for the query. In addition, the nearest neighbors may be re-ranked for future reference. 

This embedding process is often carried out with the help of a neural network. Word embeddings, for example, turn words into vectors in such a way that words with similar meanings are closer together in the vector space.

How does vector search work?

In a classic vector search use case, a query vector is passed to a vector database, and the vector database returns a customizable list of vectors with the smallest distance (“most similar”) to the query vector.

The following is a step-by-step workflow:

An engineer runs a dataset of documents, photos, or logs in R2 through a machine learning model made for that type of data to turn it into a set of vector embeddings, which is a one-way representation.

The resulting embeddings are stored in a vector database index.

The same machine learning model can be used to handle a search query, a classification request, or an anomaly detection query. The result is a vector embedding representation of the query.

The vector database is queried with this embedding and provides a list of vector embeddings that are most comparable to the specified query.

In the absence of a vector database, you would have to provide your whole dataset alongside your query each time. This just isn’t realistic because models have input size constraints. It’s also not efficient, as it would use substantial resources and time. This use case alone shows why vector databases are such a welcome solution.

How vector databases power Retrieval Augmented Generation (RAG)

The retrieval augmented generation (RAG) method is used to give an LLM (Large Language Model) more information about the context it is given. It’s used in generative AI applications like chatbots and general question-answer apps. A vector database comes in handy here to supplement the query supplied to the LLM with additional context.

In the RAG technique, instead of providing the prompt straight to the LLM, engineers can create vector embeddings from an existing dataset or corpus – for example, one they wish to use to provide context to the LLM’s response. Product paperwork, research data, technical specs, or your product catalog and descriptions can all be included here. Output embeddings are saved in the vector database index.

Vector databases and Large Language Models

Large Language Models (LLMs) emerged as a disruptive force in artificial intelligence, allowing machines to interpret and create human-like prose. These models have been trained on huge amounts of data and can guess how likely a word is based on its position in a phrase. This helps with tasks like finishing texts, translating them, and summarizing them.

However, the sheer scale and complexity of these models provide distinct hurdles, particularly when handling and retrieving the high-dimensional data they generate. This is where vector databases come into play.

Vector databases, with their capacity to manage high-dimensional data and execute quick similarity searches, are well-suited to support the operations of LLMs. They give a structured way to store and get back the vector embeddings that these models create, which lets you do quick searches for similarity in space with many dimensions.

Why are vector databases important?

Data practitioners can index vectors made by embeddings into a vector database. This allows them to locate comparable assets by searching for surrounding vectors.

This is how vector DBs allow embedding models to be operationalized. Database characteristics such as resource management, security controls, scalability, fault tolerance, and rapid information retrieval via complex query languages make the development process (and data lifecycle management) more productive.

Vector databases also enable developers to build one-of-a-kind application experiences. For example, your users may use their smartphones to take photos and search for comparable images. 

Developers may use different types of machine learning models to automate the metadata extraction process from data such as scanned documents and photos. They can index information with vectors, allowing for hybrid searches that include both keywords and vectors. To improve search results, they can also combine semantic understanding with relevance ranking.

Innovations in generative artificial intelligence (GenAI) brought about new types of models, such as ChatGPT, that can produce text and handle complicated human-computer interactions. For example, some models allow users to describe a landscape and then create a picture that matches the description.

Note that generative models are prone to hallucinations when they provide incorrect information. Vector databases can help solve this. Data practitioners can use them to supplement generative AI models with an external knowledge base to make sure they offer reliable information. . 

How are vector databases used today?

Typically, vector DBs are used to power vector search scenarios such as visual, semantic, and multimodal search. 

More recently, they’re often combined with generative artificial intelligence (AI) text models to develop intelligent agents capable of providing conversational search experiences. They can also keep generative AI models from hallucinating, which can lead to bots providing nonsensical but plausible replies.

Vector databases are designed for engineers looking to create experiences that use vector search. To build embeddings and hydrate a vector database, an application developer can leverage open-source models, automated machine learning (ML) tools, and fundamental model services. This requires only a basic understanding of machine learning.

A team of data scientists and engineers can create highly adjusted embeddings and make them operational via a vector database. This will allow them to deploy artificial intelligence (AI) solutions faster.

Use cases of vector databases

Vector databases have several use cases:

  • Natural language processing (NLP) – vector databases are critical in NLP activities such as document similarity, sentiment analysis, and semantic search. They enable efficient indexing and retrieval of textual material encoded as word embeddings or sentence vectors.
  • Anomaly and fraud detection – a vector database can be used to detect abnormalities in a variety of fields, including network traffic analysis, fraud detection, and cybersecurity. Teams can use it to compare data points to normal behavior patterns to identify anomalies based on distance from the typical vectors.
  • Improving machine learning models – vector DBs can store and retrieve model embeddings that teams can use to enhance machine learning models and generative AI.
  • Similarity matching in recommendation systems – this allows them to deliver customized suggestions based on user preferences, item attributes, or content similarity.
  • Image recognition – with features abstracted from vector representations, vector databases excel at assisting users in identifying visually similar photos or films.
  • Personalized advertising – like recommendation systems, vector databases are also a good match for tailored advertising.
  • Clustering and classification – these are supported by vector DBs, as they enable quick similarity-based grouping of data points.
  • Graph analytics – this is another use case of vector databases and includes community recognition, connection prediction, and graph similarity matching. They provide efficient graph embedding storage and retrieval for improved results.

    Key challenges of vector databases

    Vector DBs share many of the issues that other database systems also face. The push to increase scalability, approximation accuracy, latency performance, and economics affects them as well.    

    As a relatively new technology, many vector databases must advance in key database skills such as security, robustness, operational support, and workload diversification. As artificial intelligence (AI) applications advance, they will demand more than just vector search.  

    How to choose the right vector database

    How do you pick a vector database that meets your needs and helps you achieve your data management and analytical objectives?

    Keep scalability, data model, and integration capabilities in mind as you set out to choose the best vector database for your specific needs.

    Here are some key points to consider when assessing different vector databases for your project:

    Scalability and performance 

    Check scalability in terms of the amount of data and the number of dimensions the database can successfully manage. Consider its performance metrics, including query response time and throughput, to make sure it meets your workload requirements.

    Data model and indexing methods 

    Explore the data model and indexing methods offered by the vector database. For example, check if it supports flexible schema designs.

    Examine the database’s indexing mechanisms to ensure efficient similarity search and retrieval operations. Tree-based structures, locality-sensitive hashing (LSH), and approximate nearest neighbor (ANN) algorithms are all common indexing strategies.

    Ease of use 

    The vector database’s ease of setup, configuration, and maintenance are critical features. A user-friendly design and good documentation may both add to and reduce learning curves.

    Integration

    Check the vector database’s integration with your existing systems, tools, and programming languages. See if the vector database has APIs, connectors, or SDKs to help with integration.

    Compatibility with common frameworks and data processing tools will guarantee a good experience.

    Community and support

    A vibrant community often acts as a source of useful information, discussion forums, and access to professional advice. Consider the level of support supplied by the database’s developers, such as tutorials, documentation, and prompt customer care.

    License cost

    Take into account any licensing or subscription fees involved with using the vector database. Compare the pricing structure to your budget and the advantages provided by the database to check how it fits your financial goals.

    The future of vector database

    As the need for managing machine learning vector data at scale expands, vector databases are expected to become more important. After all, they deliver the performance, scalability, and flexibility required by AI applications across sectors.

    Vector databases, as opposed to conventional databases, were designed precisely for vector embeddings and neural network applications. They present a vector-native data model and query language that extend beyond SQL or graphs, making vector search easier. Vector databases provide the data solution to acquire insights from machine learning, which enhances use cases that comprehend the world through vectors.

    Vector databases have traits of both commodities and innovative technologies. They are becoming more ubiquitous for businesses creating AI, but they represent a novel database with a vector-first design that no other technology offers at the moment.

    Wrap up

    The world of search and information retrieval is rapidly evolving, and vector databases will play an important role in every software sector for the foreseeable future. As we see greater use of ChatGPT – particularly as an embedded component within applications – vector databases are expected to become increasingly common. They’re expected to be used in every major application in the next few years, from search engines to accounting systems to meme generators. 

    Due to their outstanding capabilities for processing highly dimensional data and enabling sophisticated analysis, vector databases are game changers in data management. Benefits such as better similarity search and matching and query efficiency are invaluable to organizations across many industries.

    There are already a number of great solutions on the market, each with its own set of advantages and limitations. Stay tuned for the second part of this post, where we will review the key vector databases for 2023.

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