Definition:
An embedding is a representation of a data item as a vector of numbers within a mathematical space. It expresses characteristics and relationships of elements such as words, documents, images, audio or graph nodes for use in artificial intelligence systems.
Many applications use dense vectors learned by models. The proximity of two representations can express similarity in meaning, appearance or function, depending on how the space was constructed. This does not mean that a machine understands the data as a person does.
Table of contents
Main characteristics of embeddings
The properties of an embedding depend on the model and the task for which it is used. Common characteristics include the following:
- Compact representation: it can replace sparse representations with many dimensions with more manageable vectors. Low dimensionality is a comparison with the original representation; it does not necessarily mean two or three numbers.
- Learned relationships: the distribution of vectors can reflect similarities relevant to the task. Not all human relationships are represented, nor does every dimension have a clear individual interpretation.
- Variety of data: embeddings exist for text, images, audio and graphs. Comparing different modalities requires a model that places them in compatible spaces.
- Model dependence: training, data and objectives influence the representation. Embeddings are not necessarily generated using a single neural network architecture.
- Use in large collections: they allow datasets to be compared and organized using vector operations and indexes. Efficiency also depends on storage, the index and query volume.
- Visualization: they can be projected into two or three dimensions to explore clusters. This projection simplifies the original space and can distort distances or relationships.
How embeddings work
A model transforms the input into a sequence of numerical values. During training, it learns representations useful for specific objectives, such as relating words through their context or bringing queries and relevant documents closer together.
In language, techniques such as Word2Vec assign static representations to words. Contextual models can produce different representations depending on the sentence. For example, the Spanish word "banco" does not have the same meaning in a conversation about finance as in a description of a park, where it refers to a bench.
The proximity of "puppy" and "canine" illustrates a relationship a model might learn, not a guaranteed result for every system. Image models can use convolutional networks or transformers, while techniques such as Node2Vec and DeepWalk generate node representations from the structure of a graph.
Measures such as cosine similarity, dot product or Euclidean distance are used to compare vectors. The choice should match the model and objective. The Sentence Transformers documentation shows how to calculate similarities between text representations.
Vectors must belong to compatible spaces. Two different models can produce vectors with the same number of dimensions without their coordinates being comparable. Changing the embedding model may require regenerating the stored representations.
Applications and use cases of embeddings
Embeddings serve as an input or component in different machine learning tasks:
- Semantic search: comparing queries and content to retrieve related results even when they do not share exactly the same words.
- Recommendation systems: representing users, products or content to study affinities and suggest potentially relevant items.
- Language processing: providing representations to classification, translation, sentiment analysis and other systems. Generating an embedding does not produce a conversational response by itself.
- Computer vision: comparing images and providing features to classification, visual search or other recognition systems.
- Clustering and segmentation: organizing items by similarity, for example to explore topics in a document collection or patterns in customer data.
- Graph representation: using relationships between nodes for tasks such as node classification or link prediction.
A vector database can store these representations and facilitate similarity searches. The embedding is the representation; the database is the system that stores it and allows it to be queried.
In a retrieval-augmented generation system, or RAG, embeddings can help locate passages that are subsequently provided as context to a generative model. These are different functions: retrieving information and composing a response. A RAG system can also combine vector search with other methods.
Advantages and limitations of embeddings in AI models
Embeddings allow numerical representations to be reused in search, classifiers and other systems. Compared with encoding that treats each item as an isolated category, they can express learned relationships and facilitate work with unstructured data.
Improvements in accuracy or resource savings are not automatic. They depend on the model, language, domain, data preparation and search method. A model suited to general text may not represent specialized vocabulary equally well.
Similarity does not demonstrate equivalence, truthfulness or relevance to every objective. Two contradictory statements about the same topic can have nearby representations. Training data and objectives can also introduce biases into comparisons.
Reusing embeddings can reduce some development work, but their suitability must be evaluated for the specific task. A vector is not a readable copy of a document or a complete explanation of its meaning; it is a representation designed to operate within a system.