What are vector embeddings?
Whether you’re searching on Google or Amazon, talking with a voice assistant, using ChatGPT, letting Spotify suggest your next song, browsing Pinterest for inspiration, or translating a sentence with Google Translate; you’re interacting with systems powered by vector embeddings.
To be effective, these systems must be able to interpret intent and context. That’s how Spotify recommends your next song, or how ChatGPT responds with relevant answers. At the core of it all are vector embeddings: dense numerical representations that enable machines to understand the abstract patterns of human behavior and language in a form they can process (i.e. numbers).
Why do we need vector embeddings?
What makes these systems so powerful is their ability to translate complex, subtle requests into applicative insights. Spotify, for example, doesn’t just recommend songs that other users listen to; it learns your preferences and mood over time, creating a deeply personalized listening experience.
On Pinterest, a search like “cozy boho room decor ideas” doesn’t merely match keywords; it understands the underlying concepts, delivering results that align with your vision. This is the beauty of vector embeddings: they narrow the gap between human thought and machine processing, enabling applications that feel intuitive, personal, and almost human.
How do they help businesses drive conversions and sales?
Businesses are utilizing this approach to unlock real results. Spotify’s use of embeddings to model user preferences has driven higher conversion and retention by delivering music experiences tailored to individual users. Pinterest’s Search with Multi-Task Multi-Entity Embeddings has led to improvement in engagement (>8% relevance, >7% engagement, and >5% ads CTR). Amazon’s hybrid retrieval approach, blending lexical and semantic search, has improved product discovery and user satisfaction. Meanwhile, Alibaba integrated semantic search into Taobao, refining embeddings with behavioral data to improve relevance and lift both Gross Merchandise Volume (GMV +0.77%) and transactions by +0.33%.
Across industries, vector embeddings are transforming user interactions into smarter, more adaptive digital experiences, and driving measurable business outcomes along the way.
A deeper dive into vector embeddings
Helping machines understand the meaning hidden in unstructured data, such as text, images, and audio, has been one of AI’s most persistent challenges. Unlike structured rows in a database, this kind of data is full of nuance and context. It’s messy, unpredictable, and hard for traditional systems to process.
In today’s data-driven world, this understanding is essential. Applications like search engines, recommendation systems, and AI assistants must go beyond keyword matching or rule-based logic. They need to interpret user intent, extract semantic meaning, and adapt to context and user behaviour.
Vector embeddings and vector search are able to fill this gap.
Vector embeddings allow complex, abstract, and unstructured data (think of: words, sentences, images, audio clips, entire documents and more) to be translated into high-dimensional numerical representations that capture semantic meaning and can be understood and processed by machines.
Each vector consists of numbers representing the features and relationships within the original input. In this high-dimensional space, the distance between vectors reflects semantic similarity, allowing models to reason across different types of data in a unified, meaningful way. This allows applications to compare and retrieve information based on meaning rather than surface-level features, enabling powerful capabilities in search, recommendation, and AI reasoning.
Narrowing the gap between human and machine language
Human language is full of nuance, variations and shifting context. That’s why traditional information retrieval methods often struggle with the complexity and unpredictability of how we communicate and perceive meaning. Keyword-based search, for example, relies on exact word matches between queries and documents, making it difficult to capture synonyms, contextual meaning, or deeper semantic relationships. Likewise, early image retrieval systems depended on comparing low-level features like color histograms, which failed to recognize high-level concepts or the true content of an image, missing high-level semantic concepts.
Vector embeddings overcome these limitations by encoding meaning directly into vector space, where semantically similar items cluster together, even across different data modalities.
This approach signifies a major breakthrough over traditional methods, enabling more intelligent and relevant information retrieval across numerous applications and powering much of the AI we use daily:
- Natural Language Processing – Understanding, translating, summarizing, and generating human language.
- Recommendation Systems – Suggesting movies, music, products, and content based on user preferences and behavior.
- Semantic Search – Retrieving results based on intent and meaning, not just keywords.
- Computer Vision – Recognizing images, detecting objects, and understanding visual content.
- Advanced Data Analysis – Discovering patterns, correlations, and insights hidden in large datasets.
- Cross-Modal Understanding – Connecting text, images, audio, and more within a unified semantic space.
Product discovery beyond keywords
Traditional search would miss a query like “laptop for video editing” if the product titles don’t include those exact words.
Vector Embeddings fix that by measuring how semantically close items in the catalog to the user query, enabling the system to find relevant product IDs.
So the system might show powerful laptops with dedicated GPUs (exactly what a video editor needs) even if the product titles don’t contain those exact words.
Likewise, a user searching for “ergonomic office chair for back pain” could get results like “adjustable mesh chair,” “lumbar support chair,” or even “standing desk mat.” Even if none of these products use the exact search terms, semantic embeddings understand the intent behind the query and retrieve relevant alternatives, making product discovery smarter and more intuitive.
Embeddings also support intelligent recommendations by matching a user’s profile with those of similar users. These recommendations are driven by shared patterns in browsing behavior, preferences, past purchases, and many other features, grouping users with similar buying intent.
Beyond text and product data, embeddings extend to visual content as well. For example, a picture of a sunset might be shown as similar to other natural landscapes, as embeddings of images capture visual features like textures, colors, and composition patterns. This enables powerful visual similarity search across large datasets.
Finally, embeddings enhance natural communication. In chatbots and voice assistants, they help models interpret context, tone, and meaning, leading to responses that feel more conversational and relevant to the user.
So, what can be embedded?
Virtually any kind of data can be embedded, for example:
- Text: words, phrases, sentences, entire documents, or even website domains
- Images: raw pixel data or feature representations from vision models
- Audio: voice recordings, sound effects, music
- User behavior: profiles, clicks, purchases, or sequences of actions
- Products: descriptions, categories, features
Vector Embedding models and their intricacies
Embedding models have come a long way.
While the idea of word vectors existed earlier, it was the release of the Word2Vec model (Google, 2013) that popularized and scaled this approach, marking a major breakthrough in modern NLP. It could represent words as vectors based on how they appeared in context. But it had clear limits: it couldn’t tell when a word like “bank” referred to money or a river, and it struggled with syntax.
GloVe, developed at Stanford, improved on this by looking at global word co-occurrence, but it still treated word meaning as fixed. FastText from Meta (previously Facebook) made progress by breaking words into subword units, which helped with complex or unfamiliar terms (especially in morphologically rich languages) but came with added computational cost.
The launch of BERT by Google AI in 2018 changed the game. Its bidirectional transformer architecture allowed models to understand a word based on its context, which significantly improved NLP performance. But it also introduced new challenges: higher compute requirements, limits on input length, and ongoing concerns about bias in the training data.
Today’s models keep advancing with new trade-offs. OpenAI’s text-embedding-3-small offers a strong balance of performance and efficiency. Cohere’s multilingual models enable cross-language use but can still struggle with low-resource languages. CLIP adds image-text understanding, though matching meaning across modalities isn’t always perfect. And open-source models like BGE increase accessibility but sometimes fall short in narrow domains.
What do vector embeddings look like?
Vectors live in a high-dimensional space, where the position of each point reflects the meaning or content of the original data. Similar inputs (e.g.: the words “happy” and “joyful”) end up close together in this space because their meanings are related, while a word like “angry” will be positioned farther away.
When we say that “happy” and “joyful” are related concepts, in the embedding space this translates to their vectors being close together:
“happy” → [ 0.097, 0.261, -0.148, 0.081, -0.295, …, 0.022, 0.208, -0.090 ]
“joyfull” → [ 0.103, 0.274, -0.156, 0.089, -0.312, …, 0.018, 0.211, -0.087 ]
“angry” → [-0.198, -0.135, 0.172, -0.244, 0.310, …, -0.215, -0.142, 0.193 ]
A vector analogy: color matching
Imagine trying to sort color dots in a plot so they would form a gradient. Colors that look similar, like pink and red, would be plotted close together, while completely different ones, like purple and neon green, would be far apart.
Vector embeddings work comparably, but instead of matching colors, they capture semantic meaning. So, if we used words to represent colors, we’d notice that they tend to group together in similar ways.
The same thing would happen if we used words related to shoes, clothing, automobiles, and gadgets. Shoe-related words might cluster in one region of the space, while words about clothing would appear nearby. In contrast, words related to electronics or automotive parts would be plotted farther away.
The vector space
The above illustrations provide an over simplified view of how data objects might appear when represented as vector embeddings in a vector space. For clarity, each object is shown as a two-dimensional vector. However, in practice, embeddings typically use hundreds or even thousands of dimensions to capture the nuances of language and other complex data. Each dimension represents a latent feature or attribute of the data, and depending on the model, for example, OpenAI’s text-embedding-3-small model generates 1,536-dimensional embeddings, BERT Large produces 1,024-dimensional embeddings, and models like all-MiniLM-L6-v2 generate 384-dimensional embeddings.
High-dimensional vectors are hard to visualize and compute with directly. This is where dimensionality reduction techniques (PCA, t-SNE) come into play—they project these vectors into 2D or 3D space to make them easier to inspect, allow us to peer into high-dimensional vector spaces, debug models, and uncover emerging patterns in the data.
The ins and outs of vector search
Once your data is transformed into Vector Embeddings, search becomes a geometry problem: Which vectors are closest to your query in semantic space?
This process is known as vector search. And it powers some of the most advanced applications today, from intelligent recommendations to semantic search engines and generative AI pipelines.
What is vector search?
Vector search is the process of retrieving the most similar vectors to a given query vector from a vector space. Unlike traditional keyword search, which relies on exact matches, vector search retrieves results based on their position within a semantic space. If two items share similar meanings, even with different words or formats, their vectors will be positioned closely together.
For instance, a search for “Bluetooth headphones” might return items labeled “wireless earbuds” or “noise-canceling headset,” even if those exact terms don’t appear in the product description.
Exact vs approximate search: How do we calculate the similarity of vectors?
Once we have our data as vectors, how do we find what’s similar? The beauty of embeddings is that similar items cluster together in vector space, but how we calculate this “closeness” depends on the chosen method, and that choice heavily affects the final results along with the chosen embeddings models. In the vector space, the distance or angle between vectors indicates how similar two items are:
- Cosine similarity measures the angle between two vectors, regardless of their length and magnitude, which makes it perfect for textual data where length varies but meaning stays consistent. Think of it as asking: “Do these vectors point in the same direction?” Best for text search, semantic similarity, and most NLP tasks.
- Euclidean distance, however, considers both direction and magnitude, measuring the straight-line distance between points in space; just like measuring distance with a ruler. Often used in image embeddings, clustering, or spatial problems.
- Dot product is a simple multiplication of corresponding vector values. Frequently utilized in attention mechanisms in transformers and recommender systems to gauge alignment and relevance.
- Manhattan distance, also called L1 distance, counts coordinate-wise differences, resembling block-based navigation (imagine walking along city blocks rather than cutting diagonally). Especially useful with sparse embeddings or clearly separated features.
These distance metrics are the backbone of K-Nearest Neighbors (k-NN) algorithms, which retrieve the top-k (most similar vectors) based on a chosen similarity measure. This guarantees perfect results but quickly becomes impractical at scale. Imagine searching through millions of product embeddings in real-time!
As datasets grow, comparing every vector becomes computationally expensive. That’s where Approximate Nearest Neighbor (ANN) algorithms come in: HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), Product Quantization, ANNOY (from Spotify) and ScaNN (from Google). They use clever indexing structures to find “close enough” results without scanning the entire dataset, trading a bit of accuracy for dramatic speed improvements.
Similarity spaces (and the way distance metrics are calculated within them) can vary significantly between embedding models. This means that the same pair of inputs might appear closely related in one model but not in another, depending on how each model captures and prioritizes semantic features.
Evaluation metrics of vector search
How do we know if our vector search is working well? Common metrics include:
- Recall@k: The number of relevant items found in the top k results, out of all relevant items in the entire dataset
- Precision@k: The number of relevant results in the top k
- Latency: How quickly results are returned
- Throughput: The number of queries that can be handled per second
But the right balance depends on specific application needs and desired business results.
This all forms the backbone of AI-applications like semantic search, recommendation engines, Gen-AI, Cross-Modal Understanding and many more.
Hybrid Search
Modern search systems typically follow multi-stage cascaded pipelines. They start with fast, high-recall lexical filters to generate a broad set of candidates. From there, they layer on deeper semantic understanding using hybrid approaches, blending traditional signals with vector embeddings through techniques like score fusion or sequential refinement. The final stage involves re-ranking using machine-learned models that analyze rich, complex features. This ensures the results are optimized for high precision across key relevance factors such as semantic meaning, contextual fit, and freshness, ultimately delivering the most relevant answers to users.
The power of vector search in e-commerce
Today, vector search powers a broad spectrum of AI systems across industries.
For e-commerce vector embeddings enhance product discovery and personalization. By transforming products, user queries, and preferences into numerical representations, these embeddings enable systems to understand and predict user needs.
Applications of Vector embeddings for e-commerce:
- Semantic Product Discovery: allows for semantic understanding, enabling the system to grasp the concepts within a search. For example, when a shopper searches for a “casual summer outfit,” the system interprets the query’s intent and retrieves relevant items like sundresses, shorts, or light shirts, even if those exact terms aren’t in the product descriptions.
- Recommendations: By analyzing the similarity between product embeddings, e-commerce platforms can generate effective cross-sell recommendations. When a user views a specific product, the system can suggest complementary items by assessing embedding similarities. For example, viewing a smartphone might prompt recommendations for compatible accessories like cases or chargers, enhancing the shopping experience and increasing sales. Another way is to compare user preference vectors with product embeddings to surface relevant recommendations.
- Personalized User Experiences: Embedding techniques also support personalization by mapping user behavior and preferences into the same vector space as products. This alignment allows for recommendations tailored to individual users, improving engagement and satisfaction. For instance, if a user frequently purchases athletic wear, the system can prioritize showing similar items or new arrivals in that category.
- Visual Search Capabilities: Visual search lets users find products by uploading images. “I want something like this” becomes as easy as taking a photo. This approach translates visual information into vector representations, enabling the system to locate products that closely match the uploaded image. For instance, a user can upload a photo of a dress they like, and the system will retrieve similar dresses from the catalog, making the search process intuitive and user-friendly.
- Сhatbots and generative AI applications: vector search is central to retrieval-augmented generation (RAG), where external information is retrieved and injected into a model’s context to enrich its responses with factual or domain-specific content.
- Multimodal AI systems: these are underpinned by vector search, enabling seamless interaction across different types of data. For example, models like CLIP operate within a shared vector space for both text and images, allowing users to find relevant images from text queries or generate textual descriptions for images, bridging vision and language with a common semantic representation.
However, while dense vector search excels at capturing semantic similarity and context, it often struggles with exact keyword matches, which are essential for precision in certain queries. That’s where hybrid search comes in.
Datos Vector Embeddings
While Vector Embeddings are essential for many AI applications they are computationally expensive and resource-intensive to train and run.
Datos’ embeddings introduce two powerful solutions:
- Datos out-of-the-box model: For custom-built Vector Embeddings built on your data. Run Datos embedding models directly, without needing to connect to an external provider. Our out-of-the-box model enables rapid deployment for immediate improvements in performance and relevance.
- Pre-built Vector Embeddings trained on massive clickstream corpora: Customized for specific business needs and industry verticals, adaptable to diverse use cases, product catalogs, and niche markets.
How do we train our model? What kind of data do we use?
Our vector embeddings are trained on massive, daily-refreshed clickstream data using advanced modeling techniques, including transformer-based architectures, contrastive learning, and domain-specific fine-tuning. This training leverages:
- A panel of tens of millions active global users
- 15Bn+ URLs processed monthly
- Data from 185 countries
- Billions of user queries and actions
- Domain and industry categorization
Our clickstream data provides complex insights into anonymous user behavior across the web. We capture and analyze user queries and actions, uncovering how search behavior varies across platforms, whether it’s Google, e-commerce sites, or AI agents. We trace comprehensive user journeys, from the initial search to conversion, in order to reveal unique patterns and opportunities.
We also detect emerging trends through search activity, categorize domains, and uncover deep behavioral patterns, enabling a more sophisticated and scalable understanding of behavioral signals.
All of this is powered by vector embeddings and vector search. With our extensive clickstream data, our vector embeddings capture and reflect the complexity of real-world and ever-changing shopper behavior.
Learn more about vector embeddings with our team of experts
We deliver vector embeddings that capture deep semantic understanding, empowering our clients to drive key performance metrics such as relevance, engagement, and conversion.
Our pipelines incorporate daily updates to capture evolving shopper behaviors, new product trends, and seasonal variations. Our models are pre-trained on an extensive global dataset from our clickstream data, fine-tuned on domain-specific corpora and continually learn from fresh clickstream data, capturing subtle user behavior to drive conversion rates and user satisfaction. Versioned data snapshots support model retraining and continuous improvement. We invest in ongoing R&D to stay at the forefront of embedding techniques, ensuring our models are state-of-the-art.
