Proximity between vector embeddings lets computers see the meaning and connection between the data they illustrate. For example, vector embeddings of “Husband” and “Wife” will be near each other and form a cluster together.
Embeddings make it easier to find patterns and similarities in data. However, if you plan to use them in applications, you need a vector database to store and retrieve vector embeddings. It will make your operations fast and efficient.
What are vector embeddings?
Vector embedding represents data as points in a multidimensional space where the exact location of these points makes sense semantically, that is, with regard to the meaning of words. For example, words like “dog,” “puppy,” and “labrador” will cluster together in the multidimensional space.
In the same way, embeddings for audio music will be grouped together with embeddings of songs that sound alike. Similar clustering occurs in semantically equivalent and contextually somewhat matching items.
Word embeddings can be 1D, 2D, 3D, or multi-dimensional. It’s tricky to picture it, considering the limitations of humans. As you input more complex data, like a sentence or a document, the embeddings start having higher dimensions.
However, to give you a picture, a vector embedding looks like [“0.2, 09, -0.4, 0.8…]. Each number represents dimensions that describe the specific feature of the data point and how they contribute to its actual meaning.
Understanding vector embeddings
Google invented a technique called Word2Vec in 2013 to take input as words and shoot out a vector (n-dimensional coordinate). Plotting these word vectors in space will give you synonymous clusters.
For example, if you input words like “computer,” “keyboard,” or “mouse” as input, their vector embedding will cluster closely in a multi-dimensional space. Suppose someone gives input as “computer devices,” its vector embedding will also join the cluster.
Vector embedding lets you compute similarity scores between different embedded data points. For example, you can calculate the distance between two data points to understand how similar they are. This is known as the Euclidean distance method.
You can also use other methods to calculate similarities:
- Cosine distance calculates the cosine of the angle between two vectors. It gives an output of -1 if vectors are diametrically opposed, 0 if orthogonal, or 1 if they’re identical.
- Dot product determines similarities in the range from minus infinity to infinity. It measures the product of the magnitude of two vectors and the cosine of angles between them.
These similarity scores are widely used in facial recognition technology and typo correction. For example, “Hi,” “Hiiii,” and “hiiiiiii” have the same contextual meaning and, therefore, higher similarity scores.
Are embeddings and vectors the same thing?
Embeddings and vectors are closely related but not the same. A vector is a general mathematical representation of data in a multidimensional space, consisting of an ordered list of numbers that can represent anything numerically, such as positions or directions.
In contrast, an embedding is a specific type of vector designed to encode complex data, such as words, images, or users, into a dense, numerical format that preserves meaningful relationships.
Embeddings are often created using machine learning models to map high-dimensional data into lower-dimensional spaces while retaining semantic or structural information. Thus, while all embeddings are vectors, not all vectors are embeddings.
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Types of vector embeddings
The various kinds of vector embeddings serve distinct purposes. Read about these common ones here.
Text embeddings
Text embeddings convert individual words into continuous vectors in a multidimensional space, where the relative distance or direction represents the semantic relationship between words. For example, words like “king” and “queen” would be close to each other, reflecting their similarity, while “king” and “car” would be farther apart.
In sentiment analysis, text embeddings help classify whether a review is positive or negative. If a user writes, “This product is amazing,” the embedding captures the sentiment for downstream tasks. Techniques like Word2Vec, GloVe, and FastText are commonly used for this purpose.
Sentence embeddings
Sentence embeddings capture the overall meaning of a sentence, considering both syntax and semantics. Unlike word embeddings, they aim to preserve the context of the entire sentence. These embeddings are crucial for categorizing text or retrieving relevant information from databases.
In customer support, when a user types “I’m having trouble logging in,” sentence embeddings can match it to related help articles, such as “How to reset your password.” Pre-trained models like Sentence-BERT (SBERT) are often used to generate such embeddings.
Document embeddings
Document embeddings represent an entire piece of text—such as a book, article, or research paper—as a single vector. They capture the overall theme, structure, and important features of the document.
Document embeddings help recommend papers in academic research. If a researcher is reading a paper on "neural networks for image classification," the system can suggest similar documents using embeddings derived from the paper’s content. Models like Doc2Vec are commonly used.
User profile vectors
User profile vectors encode user behaviors, preferences, and traits as vectors. These embeddings are created based on historical actions, such as purchases, likes, or search queries. Businesses use them to segment users and offer personalized experiences.
In e-commerce, if a user frequently buys fitness gear, their profile vector may recommend related items like yoga mats or protein powders. Platforms like Netflix and Amazon heavily rely on user profile embeddings for personalized recommendations.
Image vectors
Image embeddings represent visual data, such as photos or video frames, as vectors. They are generated using deep learning models like Convolutional Neural Networks (CNNs), which enable machines to identify patterns and features within images.
In object recognition, an app like Pinterest uses image embeddings to recommend visually similar items. For instance, if a user uploads a photo of a red dress, the app might suggest dresses in similar styles or colors. Models like ResNet or VGG create these embeddings.
Product vectors
Product vectors represent items as vectors by analyzing their features, such as price, category, or description. These embeddings help systems classify products and identify similarities.
In retail, a search for "wireless headphones" in an online store generates a product vector. The system then recommends similar items like “Bluetooth earbuds” or “noise-canceling headphones.” These vectors improve search accuracy and personalization in platforms like Shopify or Flipkart.
How to create vector embeddings
Vector embeddings are created either through a pre-trained model route or a DIY, train-your-own model route. Here’s an overview of the process.
Data collection and preparation
Begin by gathering a large dataset that aligns with the type of data you want to create embeddings for, such as text or images. It’s essential to clean and prepare the data—remove noise, normalize text, and address any inconsistencies to ensure quality inputs.
Choosing a model
Next, select an artificial neural network (ANN) model suitable for your data and goals. This could be a deep learning model like a convolutional neural network (CNN) for images or a recurrent neural network (RNN) for text. Once chosen, feed the prepared data into the network for training.
Training the model
During the training phase, the model learns to recognize patterns and relationships in the data. For instance, it might learn which words frequently appear together or how certain features are represented in images. As the model trains, it generates numerical vector embeddings that capture the essence of each data point. Each data item will be assigned a unique vector.
Evaluating embedding quality
After training, assess the quality of the embeddings by applying them to specific tasks. This could involve evaluating how well the model performs in tasks like classification, clustering, or recommendation. Your team should review the results to ensure the embeddings meet the intended objectives.
Deploying the embeddings
If the embeddings perform well and meet quality standards, they can be applied to real-world tasks such as search, recommendation, or natural language understanding. With successful validation, you can confidently implement the embeddings wherever they’re needed in your applications.
Vector embeddings applications
Vector embeddings are used in many fields. Explore their common applications.
Natural language processing (NLP)
Vector embedding lets models recognize the semantic relationships between different words. Advanced embedding techniques like Word2Vec, GloVe, and, more recently, contextual embeddings from models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformers (GPT) allow technology to understand the context in which the words are used.
In NLP tasks, it becomes easier to distinguish between different meanings of the same word based on context. For example, the “bank” in “river bank” is different from the “bank” in “bank account.”
Moreover, embeddings support NLP tasks with sentiment analysis and named entity recognition.
Search engines
Vector embeddings improve search engines’ performance and accuracy. It lets them understand the context and meaning of words in a query so they move beyond looking for exact word matches.
This improves the rankings because they’re based on semantic similarity rather than the frequency of the keyword. It means that pages that are contextually similar to the query are prioritized to deliver more accurate results.
Moreover, when people enter queries with multiple meanings, vector embeddings let search engines absorb the context and return results according to the closest interpretations.
Personalized recommendation systems
Vector embeddings depict both users and items in a common latent space. For example, user embeddings show preferences and behaviors, and item embeddings include an item's characteristics and attributes. The system computes the distance between user embeddings and item embeddings while measuring the angle between their cosines. Based on this analysis, the system suggests items that are nearest to the users.
Vector embeddings also incorporate contextual information, such as device type or time of day, to ensure that the recommendations are relevant to the current user and their environment.
Top vector database software solutions
Vector database software is essential for efficiently storing, managing, and querying high-dimensional embeddings. These tools power fast similarity searches and seamless integration with AI workflows. Here are some of the best solutions available today.
*These are the five leading vector database software solutions from G2's Winter 2024 Grid® Report.
Start working with vector embeddings
You need the right technology to equip your application and models with semantic search capabilities or personalized product recommendations. Think of a vector database to store data and access them based on similarities.
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Consider these free vector databases to experience them on a trial or a free plan.
Sagar Joshi
Sagar Joshi is a former content marketing specialist at G2 in India. He is an engineer with a keen interest in data analytics and cybersecurity. He writes about topics related to them. You can find him reading books, learning a new language, or playing pool in his free time.
