What are vector embeddings? A complete guide [2025]

Vector embeddings are numerical representations of text, images, audio, and other data types. They work by mapping complex, high-dimensional data into a lower-dimensional space using Machine Learning (ML) models. This enables computers to interpret unstructured data, identify patterns, and power tasks like semantic search.

Common types include word embeddings, image embeddings, and document embeddings. They are created using embedding algorithms, such as Word2Vec, Convolutional Neural Networks (CNNs), and Doc2Vec, respectively, and placed in a semantic space where proximity reflects conceptual similarity—e.g., "tree" and "plant" cluster near "nature."

Vector embeddings can be used in Retrieval Augmented Systems (RAG), search engines, and other applications. For that, a vector database is required to query high-dimensional data efficiently. These infrastructures require high engineering costs, maintenance, and technical expertise.

In the following piece, we’ll cover vector embeddings in detail and how they work, mentioning potential applications, benefits, and challenges associated with the technology.

What are vector embeddings?

Vector embeddings are numerical representations that convert complex data—such as text, images, and documents—into multidimensional arrays of floating-point numbers. They are usually represented as a sequence of numbers in a multidimensional space, where the combination of all values characterizes the data input.

These embeddings capture semantic relationships, allowing machines to process and compare data efficiently. By mapping similar data points closer together in a vector space, embeddings enable various applications, from Natural Language Processing (NLP) and recommendation systems to anomaly detection, RAGs, and question-answering systems.

Many AI applications are powered by vector embeddings, which convert complex data into compact, semantically rich representations.

How do vector embeddings work?

Vector embeddings are generated using ML models that take unstructured data inputs (e.g., text, images, documents, audio) and create continuous multidimensional vectors, also known as dense vectors.

The process starts by training an embedding model on datasets to identify patterns in the data. For text, this means analyzing word relationships and contextual sequences—examples of models are Bidirectional Encoder Representations from Transformers (BERT), Global Vectors (GloVe), and Word2Vec. In images, convolutional layers detect patterns at different levels, from edges to complex shapes—usually achieved with CNNs.

During training, the model adjusts the vector representations through continuous optimization, typically via gradient descent, to minimize a loss function. This ensures that semantically similar items are mapped closer together in the vector graph.

This multidimensional semantic space provides a structured way to measure relationships, using models such as K-Nearest Neighbors (KNNs) and metrics like cosine similarity to rank results.

The resulting vectors capture intricate details about the input, such as semantic and contextual meaning, depending on the embedding algorithm used in the process.

What are the benefits of vector embeddings?

Vector embeddings enable systems to process and understand complex data. Below are four key benefits of this technology:

• Enhanced results: Companies use vector embeddings to enhance their search engines and provide clients with more precise and contextually relevant results. According to a study published by Statista Research Department, 25% of adults in the United States say that AI-powered search engines have delivered more precise results, and 12% claim that the results are more trustworthy.
• Reduced bounce rate: One way to improve your bounce rate is through personalization. Businesses can use vector embeddings to provide optimized suggestions based on the customer’s historical actions within the platform (e.g., searches, saves, and purchases). This is particularly critical in industries such as healthcare, food, and eCommerce, which experience bounce rates of 40.94%, 38.94%, and 38.61%, respectively.
• Improved recommendation systems: The Recommendation Engine Market is projected to reach $38.18 billion by 2030, driven by the growing demand to improve customer experience. Vector embeddings capture nuanced patterns and customer preferences over time, improving the quality of the recommendation system.
• Better user experience: Voice assistants like Google Assistant leverage audio embeddings to improve speech recognition accuracy. According to this article by The Business Research Company, the voice assistant application market size has grown exponentially recently. Expecting a $7.26 billion growth in 2025 at a compound annual growth rate (CAGR) of 29.4%.

What are the different types of vector embeddings?

There are several types of vector embeddings, either generated from different data sources or created by distinct ML models.

Let’s take a look at them and their differences:

• User embeddings: These are generated by analyzing user interactions, such as clicks, purchases, and session duration, through collaborative filtering or neural networks. They often power recommendation systems. A good example is Netflix, which uses user embeddings to help display content based on viewing history.
• Product embeddings: They are typically generated from transactional data and product metadata. Like user embeddings, they power recommendation systems in eCommerce websites, such as Amazon. Then, the website can show products based on the user’s previous purchases.
• Image embeddings: These represent visual features such as shapes, colors, and textures, enabling machines to understand images numerically. These embeddings are generated using convolutional neural networks (CNNs) like ResNet or Vision Transformers (ViT). They power applications like image search (e.g., Google Lens) and object detection.
• Word embeddings: Vector representations of words that capture semantic meaning and contextual relationships. They are trained on large text data using models like Word2Vec, GloVe, or BERT. Word embeddings are essential for tasks such as sentiment analysis, where they help classify reviews as positive or negative.
• Sentence embeddings: These extend word embeddings to represent entire sentences or phrases, capturing their contextual meaning. These embeddings are generated using transformer models like Sentence-BERT or Universal Sentence Encoder. Key applications include semantic search, which is used in search engines like Spotify.
• Document embeddings: Numerical representations of entire documents, such as articles or PDFs. They are built by aggregating word or sentence embeddings (e.g., Doc2Vec) or using transformer-based models. These embeddings are widely used for RAG systems.

How to create vector embeddings

The process of creating vector embeddings takes the following key steps:

1. Select your data type: Choose between text, images, documents, or other formats. Regardless of the data source, ensure you have sufficient training data to avoid model overfitting.
2. Preprocess the data: Different applications require different preprocessing techniques. This might include removing punctuation, emojis, or irrelevant terms for text embeddings to reduce noise. Preprocessing could involve resizing or applying data augmentation to improve model performance for images.
3. Generate vector embeddings: Apply an appropriate embedding model to the preprocessed data, such as BERT for text or CNNs for images. The generated vector embeddings are then indexed within a vector graph for efficient retrieval.
4. Evaluate embedding quality: When a search query is processed, retrieval models like Approximate Nearest Neighbors (ANN) or KNN are used for information retrieval. If the retrieved results maintain semantic or contextual integrity, no further adjustments are required.
5. Optimize as Needed: If results are not optimal, revisit the training data, refine preprocessing methods, or experiment with alternative embedding models to enhance the quality of the vector embeddings.

This process can be time-consuming and requires a certain level of expertise. The latest and most advanced models don’t always generate the best vector embeddings, making it crucial to ensure proper data preprocessing, cleaning, and continuous database monitoring.

What is the semantic space?

Semantic space represents vector embeddings derived from high-dimensional data, such as words, phrases, and images. The embedding models generate vector embeddings clustered in a multidimensional vector space, capturing relationships between units based on their meanings and patterns.

By translating language into mathematical coordinates, semantic space enables machines to analyze context, similarity, and analogy in ways that mimic humans.

The semantic space should compare apples with apples. Therefore, a vector graph generated for images differs from one derived from words or sentences. However, they both serve the same finality, easily retrieving information and semantics.

Illustrating the semantic space

We can illustrate the semantic space with a simple example. Consider a diagram with three axes corresponding to the following semantic properties: feline, juvenile, and canine.

• On the feline axis, we have the cat
• On the juvenile axis, the baby
• On the canine axis, we have the dog

By combining these axes, we can find intersections that give us more specific entities:

• Feline and juvenile combined give us the kitten
• Juvenile and canine combined give us the puppy

By assigning numerical vector values to these properties, we can construct a simple semantic space:

Embedding vectors in the semantic space

In other words, the images are mathematical representations with float numbers (vector embeddings) placed according to their similarity in the vector space. That’s why if a user queries “Show me a baby dog,” the system can retrieve a “puppy” even if the right keyword wasn’t used.

The semantic space is way more complex than the previous example, and we can’t even represent it graphically because it is an n-dimensional space.

For instance, the properties are not always clearly defined. We don’t know if this is actually the canine property, but it’s correlated to something canine, and the dog ranks very high on this property. The numbers are not 1 or 0 but some real numbers.

This complexity allows for a nuanced understanding of how words and concepts relate to each other. The actual semantic space could look like the following:

From these detailed values, vector embeddings are created, capturing the essence of each word in a multidimensional vector, such as [0.959, 0.0032, 0.022] for “dog.” These vectors do more than just position words in a space; they build a detailed network of meanings, with each aspect designed to reveal a bit of what the word means. The specific dimensions and what they represent can vary from one model to another, reflecting the complexity of the semantic meanings they encapsulate.

Where are vector embeddings used in real-world applications?

Vector embeddings have become the core elements to power modern artificial intelligence systems, enabling machines to process unstructured data with human-like understanding. Below, we explore some real-world applications across several industries:

Search engines

• Semantic search: Vector embeddings power semantic search, allowing engines to interpret user intent rather than relying solely on keyword matching. For instance, Google Search uses embeddings to map queries and documents into a shared vector space, retrieving results based on semantic relevance.
• Relevance ranking: You can use vector search and neural search systems to rank vector embeddings based on their semantic similarity to the user’s search query. This is crucial to presenting the most accurate results to the user based on the search query.

Recommendation systems

• Personalized content delivery: Streaming services like Netflix use vector embeddings to represent movies based on genres, actors, and user interactions, enabling real-time suggestions. In the eCommerce industry, vector embeddings represent product metadata, providing users with items related to their previous views and orders.
• Collaborative filtering: This assumes that users with similar past behaviors will have similar future preferences. By crossing vector embeddings of products ordered by two different customers with similar interests, the system can enhance the recommendation results for both.

Natural language processing (NLP)

• Text understanding in large language models (LLMs): Chatbots like those in customer support systems convert queries into vectors with LLMs (e.g., “How do I reset my password?”) and retrieve pre-trained responses from semantically similar embeddings (e.g., “Steps for password change”).
• Machine translation: Models like LASER from Facebook and Multilingual Unsupervised or Supervised Embeddings (MUSE) generate multilingual sentence embeddings, allowing direct cross-lingual retrieval and language translation.

Fraud and anomaly detection

• Identifying unusual patterns: Financial institutions use embeddings to encode transactional patterns into vectors, flagging unusual behaviors in real time. For example, a digital banking platform like Revolut can detect fraud when a user’s transaction vector (e.g., small, local purchases) suddenly shifts to an anomalous vector (e.g., large international transfers).
• Behavioral analysis: Vector embeddings capture historical user activity, such as transaction frequency, login times, device usage, and browsing patterns. By embedding these behaviors into a vector space, fraud detection models can compare new user actions against normal patterns to flag suspicious deviations.

Image and video analysis

• Content-based retrieval: Platforms like Google Lens and Pinterest Lens leverage convolutional neural networks (CNNs) to generate embeddings from images. When a user uploads a photo, the system maps it into an embedding space and finds the closest matches in the database.
• Facial recognition: Instead of relying on exact matching, smartphones (Apple’s Face ID) and computers use vector embeddings to map facial patterns. This enables accurate recognition despite variations in hairstyle, lighting, makeup, glasses, or other physical changes.

How are vector databases used with vector embeddings?

Vector databases are robust architectures that efficiently store and retrieve high-dimensional data representations in the form of vector embeddings. Instead of handling raw data, these databases index compact numerical representations generated by machine learning and deep learning (DL) models—ranging from text and images to audio—capturing the semantic essence of the underlying information.

By organizing data into this high-dimensional space, vector databases enable rapid similarity searches, making it possible to quickly identify and retrieve items.

Imagine vector embeddings as stars scattered across a vast cosmic expanse. In this analogy, similarity search is used to locate the nearest stars to your current position in the universe. In practical terms, this translates to identifying the most relevant documents, images, or products based on a search query.

To achieve this, the system calculates the distance between the query vector and other vectors stored in the database, often using methods like cosine similarity or Euclidean distance. These techniques measure how close or far apart data points are from the query, similar to determining the relative positions of stars in the night sky.

Vector databases, like Meilisearch, are designed to address the unique demands of vector embedding applications such as personalized recommendations, content-based retrieval, and fraud detection.

What are some of the challenges of using vector embeddings?

While vector embeddings have versatile applications in modern times, they still have significant challenges. Below, we explore three key drawbacks:

Scalability issues

As datasets grow, managing and querying billions of high-dimensional embeddings becomes increasingly complex. Vector databases must handle massive volumes of data while maintaining low latency for real-time applications like recommendation systems or fraud detection.

Traditional indexing methods struggle with the "curse of dimensionality," where the efficiency of search algorithms degrades as the number of dimensions increases.

A good example is in document retrieval applications, such as a large repository of scientific articles, where each paper is represented as a high-dimensional vector, sometimes with hundreds or even thousands of dimensions. As more documents are added, points tend to be equidistant from one another, making it difficult to retrieve relevant scientific results efficiently. This leads to slower query times and reduced accuracy.

Solution: Advanced techniques like Hierarchical Navigable Small-World (HNSW) graphs help mitigate this.

Semantic drift

Vector embeddings are trained on specific datasets, and their performance can degrade over time due to changes in language, user behavior, or domain-specific contexts. This phenomenon, known as semantic drift, occurs when the relationships captured by embeddings no longer align with real-world usage. For instance, a word like "virus" might shift meaning during a pandemic, affecting search results or recommendations.

This is particularly common in fashion and eCommerce because users may change their lifestyles and trends over time, resulting in recommendations that no longer align with the customer’s tastes.

On streaming platforms, users are presented with series and movies that align with their past views and searches. However, if their tastes change drastically, they must spend time researching until they find what they’re looking for.

Solution: To maintain relevance, models must be regularly retrained and fine-tuned. However, this process requires high computational cost and continuous monitoring to ensure embeddings remain accurate and up to date.

Computational cost

Generating and processing vector embeddings demands computational power, especially for large-scale or real-time applications. Training models such as BERT or Contrastive Language-Image Pre-Training (CLIP) require high-performance GPUs and large datasets, costing thousands of dollars in cloud computing.

Even after training, real-time querying can put significant pressure on infrastructure, particularly in applications like autonomous driving. Self-driving cars rely on continuous sensor input—cameras, LiDAR, and radar—to generate embeddings for objects in their environment.

These embeddings help vehicles recognize pedestrians, road signs, and other vehicles in real time. Since every millisecond counts, the system must process embeddings at high speed while maintaining accuracy, requiring powerful onboard computing hardware and efficient optimization techniques. These resource requirements make embedding-based solutions expensive to deploy and maintain.

Solution: Cloud providers like AWS, Google Cloud, and Azure offer scalable, on-demand access to GPUs and TPUs, enabling cost-effective scaling based on workload demands.

Get started with vector embeddings

While vector embeddings are nowadays an indispensable technology for power applications, they are also complex, computationally demanding, and expensive to engineer. Success begins with selecting the right vector database—one that optimizes indexing in the semantic space and offers seamless integration, monitoring, and analytics.

Frequently asked questions (FAQs)

Let’s list the most common questions concerning vector embeddings below.

What are the disadvantages of vector embeddings?

The disadvantages of vector embeddings are the scalability issues that arise from extremely large databases, which make information retrieval inefficient. There’s also semantic drift, often related to changes in user behavior or the semantic meaning of certain words. Finally, there are the computational costs associated with training the data, notably for real-time use cases.

What types of data can be converted into vector embeddings?

Vector embeddings can be applied to a variety of data types. These include:

• Product metadata, commonly found in eCommerce platforms;
• User behavior data, such as historical views from streaming services;
• Images, which are embedded using convolutional neural networks (CNNs);
• Individual words, often utilized in translation systems, sentences, which offer more contextual information than single words;
• Documents, which can include entire files like PDFs.

How do vector embeddings differ from one-hot encoding?

Vector embeddings differ from one-hot encoding by representing data as dense, low-dimensional vectors that capture semantic relationships. In contrast, one-hot encoding uses sparse vectors with no inherent meaning. The latter represent categorical variables, where each unique category is assigned a binary vector with a single "1" at the position corresponding to the category and "0"s in all other positions. Thus, the vectors are sparse.