How do embeddings work and how are they stored and used in an AI system (e.g. for semantic search)?

In the age of information overload, being able to retrieve meaningful information from huge datasets has become a cornerstone of modern technology. From search engines to recommendation systems, many AI system architectures rely on embeddings to represent data in a way that machines can understand. Embeddings are essentially mathematical representations of data (like text, images, or even user behavior) that capture the meaning or key features of that data. By converting complex information into numbers, AI systems can compare items by similarity of meaning rather than just matching keywords. This article will demystify how embeddings work, how they’re stored (e.g. in vector databases), and how they’re used in AI applications like semantic search. If you’re beginning your journey into AI fundamentals, read on – understanding embeddings will give you insight into how modern AI finds patterns and makes smart decisions.

What Are Embeddings in AI?

At its core, an embedding is a list of numbers (a vector) that represents an object in a multi-dimensional space such that the object’s meaning or context is preserved. In other words, embeddings are numerical representations that capture the relationships and meaning of data. For example, a machine learning model can take a word, sentence, image, or other data and generate an embedding vector for it. These vectors aren’t random – they are created so that similar pieces of information end up with vectors that are near each other in this vector space, while very different information ends up far apart. Essentially, embeddings act like coordinates on a “semantic map,” where related items cluster together and unrelated items are far apart.

To understand why embeddings are useful, consider how we might represent words. A simple approach is one-hot encoding, where each word is a unique binary vector (mostly zeros and a one at one position). However, one-hot vectors for “cat” and “dog” would be just as unrelated as “cat” and “car” in that scheme, even though cats and dogs are more semantically similar. Embeddings solve this by condensing high-dimensional information into dense vectors that capture semantic essence. For instance, an embedding might place “cat” and “dog” close together in the vector space (since both are animals/pets) but “car” far away (different category). In fact, well-designed word embeddings can even capture analogies: a famous example is king – man + woman = queen, meaning if you take the embedding for “king,” subtract the concept of “man,” and add “woman,” you get a vector close to the embedding for “queen”. This demonstrates that embeddings encode meaningful relationships in their numbers.

Embeddings aren’t limited to text. We have AI embeddings for images, audio, and more. No matter the data type, an embedding is a low-dimensional vector that represents that data’s important features. This allows machine learning models to find similar objects by comparing these vectors. In summary, an embedding is like a compact summary of an item – a numeric fingerprint that conveys its identity and context in a form computers can easily work with.

How Do Embeddings Work?

How are embeddings created? Typically, they are produced by training a neural network (or other machine learning model) to encode data into vectors. This falls under the field of representation learning, where the model learns to map input data to points in a vector space such that meaningful patterns are preserved. For example, a neural network can be trained on text (using techniques like Word2Vec or BERT) so that words with similar context end up with similar embeddings. Technically, embeddings are vectors created by machine learning models to capture meaningful information about each object. During training, the model adjusts the coordinates of each vector to reflect semantic similarities – effectively “learning” the language of the data. After training, the model can take a new piece of data (a word, an image, etc.) and output its embedding vector.

Because of this training process, embeddings have some powerful properties. As mentioned earlier, similar items get clustered in the vector space. This means the distance or angle between two embedding vectors indicates how related the two items are. A common way to measure similarity is cosine similarity, which checks the angle between vectors – if the angle is small (cosine close to 1), the vectors (and thus the items) are very similar. In practical terms, if you have an embedding for the sentence “I love cats” and another for “I like dogs,” a good embedding model will place those vectors relatively close, reflecting that the sentiments are related. The ability to quantify similarity in this manner is what allows AI systems to perform tasks like finding related images, grouping similar customers, or answering questions by finding relevant passages – all by operating on these numeric embeddings.

How Are Embeddings Stored in AI Systems?

Working with embeddings means dealing with potentially millions of high-dimensional vectors (for all the words in a vocabulary, all images in a database, etc.). Storing and searching through these efficiently is a challenge. This is where vector databases come in. A vector database is a specialized data store designed to handle large collections of embedding vectors. Unlike a traditional relational database that stores rows and columns of structured data, a vector database is optimized to store high-dimensional vectors and to perform quick similarity search on them. In essence, the database indexes the vectors in such a way that given a new vector (for example, an embedding of a query), it can rapidly find the most similar vectors among the stored data.

Storing embeddings in a vector database allows an AI system to remember and retrieve knowledge it has learned. The system typically saves each embedding alongside an identifier or metadata (for instance, which document or image it represents). When new data or a query comes in, the system doesn’t need to recompute embeddings for everything from scratch – it simply computes the embedding for the new input and searches for nearest matches in the database. This greatly speeds up tasks like semantic search or recommendation. The model’s embedding of the data is essentially cached in the database, so the heavy lifting (converting raw data into vectors) is done only once. For example, an e-commerce website might use a vector database to store product embeddings and user preference embeddings. When you view a product, the site finds similar product vectors in the database to recommend related items. Vector databases (or vector indexes like FAISS, Annoy, etc.) use clever algorithms – often Approximate Nearest Neighbor search – to find similar vectors fast, even if there are billions of items.

To tie it into the bigger system architecture: an AI system using embeddings will have a component that generates embeddings (the neural network model), and a storage component (the vector database or index) that holds all the embeddings for quick lookup. This setup is a key part of many modern semantic search AI systems and other AI applications. The stored vectors can always be mapped back to their original content (for example, storing a mapping from each vector to the document or image it came from), so when the system finds the nearest vectors, it can retrieve the actual results to return to the user.

How Embeddings Power Semantic Search

One of the most important uses of embeddings is in semantic search. Traditional search engines matched keywords literally – if you search for "best Italian restaurant in NYC," the engine would look for pages containing those exact words. Semantic search goes beyond that. Semantic search can be defined as search that considers the meaning of words and sentences, retrieving results that match the query’s meaning rather than just the exact words. Embeddings make this possible by translating text into a numerical form that captures its meaning.

Here’s how a semantic search system works with embeddings:

1. Embedding the Text: First, the system uses an embedding model (for text, this could be a language model like BERT) to convert all documents in the corpus into embedding vectors. The user’s query is also converted into an embedding vector in the same space.
2. Similarity Search: The query’s embedding is then compared to the embeddings of all the documents (usually via efficient vector search in a vector database). Instead of looking for exact keyword matches, the system looks for documents with vectors closest to the query’s vector using a similarity metric like cosine similarity.
3. Retrieve Relevant Results: The documents whose embeddings are nearest to the query’s embedding are returned as the top results. These are the documents that are most semantically similar to what the user asked.

For example, if the query is “best Italian restaurant in NYC,” a semantic search engine might return results like “Top-rated pasta places in Manhattan” or “Authentic Italian cuisine in New York” – even if those pages don’t use the exact phrasing of the query. This is because the embeddings of those result pages are very close to the embedding of the query, indicating they talk about similar concepts (great Italian food in the NYC area). In contrast, a keyword-based search might miss those pages if they don’t contain the specific words "best" or "restaurant." By using embeddings, semantic search AI systems better understand the intent behind your words and can find information that’s relevant in meaning.

Semantic search is a game-changer for dealing with natural language queries, and it’s increasingly used in everything from web search and chatbots to enterprise document search. Thanks to embeddings, the AI can handle synonyms, rephrasing, and context far more gracefully. Modern semantic search systems often use vector databases to handle the large volume of embeddings and employ algorithms like Approximate Nearest Neighbors to keep search fast. The result is an experience where the AI seems to "know" what you mean. Instead of treating words as unrelated tokens, the system uses the rich relational information in embedding vectors to deliver smarter, more relevant results.

Conclusion and Next Steps

Embeddings have quietly become the unsung heroes of many AI applications. They convert the messy complexity of human language and other data into tidy numerical vectors, allowing algorithms to measure similarity and meaning in a straightforward way. We saw how embeddings work as the language of data for AI, how they’re stored efficiently in vector databases, and how they power semantic search to go beyond simple keyword matching. This fundamental concept bridges the gap between raw data and intelligent behavior, making it indispensable in modern AI system design.

For those preparing for technical interviews or working on system design, understanding embeddings is more than just trivia – it’s often a technical interview tip to grasp such core concepts. You can even practice explaining how embeddings work as part of your mock interview practice, since it’s a topic that can showcase your understanding of AI fundamentals and system architecture.

If you’re excited to learn more and strengthen your AI foundation, consider taking the next step with our Grokking Modern AI Fundamentals course. It offers a beginner-friendly deep dive into concepts like embeddings, neural networks, and semantic search (and much more) with hands-on examples and expert guidance. Sign up for the Grokking Modern AI Fundamentals course at DesignGurus.io to continue your learning journey and unlock the skills that will help you build and understand modern AI systems.

FAQs

Q1: What is an embedding in AI? An embedding in AI is a numeric representation of data (such as a word, sentence, image, etc.) in a multi-dimensional vector form. Embeddings are designed so that they capture the meaning or key characteristics of the data. Similar items have embeddings that are close together in this vector space, which helps AI models understand relationships and context. In simple terms, an embedding is how a computer represents a piece of information in a way that makes semantic comparisons possible.

Q2: How do embeddings enable semantic search? Embeddings enable semantic search by allowing search systems to compare the meaning of queries and documents, rather than just matching keywords. In semantic search, both the user’s query and all candidate documents are converted into embedding vectors. The search system then finds documents with vectors most similar to the query’s vector (using measures like cosine similarity). Those documents likely have content that addresses the query’s intent. This way, even if the exact words differ, relevant results (meaning-wise) can be retrieved thanks to the embeddings.

Q3: What is a vector database, and why use one for embeddings? A vector database is a specialized database optimized for storing and retrieving high-dimensional vectors (embeddings). Traditional databases aren’t efficient at similarity searches on vectors, but a vector database can quickly find which vectors in a set are closest to a given query vector. Using a vector database allows an AI system to handle large volumes of embeddings and perform rapid nearest neighbor searches. In practice, this means if you have thousands or millions of embeddings (for example, representing all articles on a website), a vector database can efficiently retrieve the most similar entries to an input query’s embedding. This is crucial for applications like semantic search, recommendations, and anomaly detection.

Q4: Why are embeddings important in AI? Embeddings are important because they unlock a richer understanding of data for machines. By compressing complex data into a vector that preserves meaning, embeddings let AI models perform tasks like clustering, classification, and search with a semantic edge. They enable systems like neural networks to work with text, images, and more in a quantitative way – for example, allowing a recommendation system to suggest items similar to what you like, or a translation model to find relationships between words across languages. In essence, embeddings are a foundational technique in AI fundamentals that power many advanced capabilities, from powering semantic search AI to enhancing recommendations and understanding context in large language models. They are a key building block in modern AI systems and a must-know concept for anyone looking to design or work with intelligent systems.