How it works
An embedding model transforms an item, such as text, an image or a code snippet, into a list of numbers called a vector. The vector represents features learned by the model. The idea is that items related by the learned criterion have representations that can be compared mathematically.
For text, the system can generate a vector for a sentence, a paragraph or a document. A search mechanism then compares that vector with the vectors of other content and ranks candidates by a proximity measure. The chosen measure and the model used influence the results; mathematical proximity does not guarantee that two texts are equivalent in every sense.
A typical flow involves preparing documents, splitting them into parts, calculating embeddings and storing them alongside metadata. When a query arrives, the system calculates its representation and finds similar parts. The application can deliver those snippets to a person or use them as context for a language model.
Quality depends on language, domain, chunk size, preprocessing and model. In systems that change models, you should not assume that old vectors remain compatible: it may be necessary to recalculate the representations. Evaluate search with real queries and expected results.
Practical example
A team maintains internal documentation about installation, authentication and troubleshooting. In a traditional keyword search, a question like "I can't log into my account" might not find a page that uses the expression "authentication failure". An embedding model can represent those expressions close to each other if it learned the relevant semantic relationship.
The application calculates vectors for the documentation snippets and also stores the title, source and update date. When someone asks about access, the system finds the most related snippets and shows links to the corresponding pages. If using an LLM to answer, it should keep references to the retrieved snippets and signal when there is not enough information.
The team needs to test ambiguous questions, outdated documents and queries in Portuguese. A seemingly smooth search can still retrieve the wrong page. Relevance metrics and example reviews help identify failures before scaling usage.
What it's for
Embedding models are used in semantic search, recommendation, clustering, deduplication and context retrieval for language model applications. They help compare items by a numeric representation, instead of relying only on literal word matching.
An embedding by itself is not a search engine nor a database. It produces the representation; another component indexes, stores and queries the vectors. It also does not replace access rules: before returning a document, the application must check whether the person is permitted to see it.
Choose the model based on languages, data and task. Test relevant examples, observe result quality and consider privacy and operational cost in the solution design. If search has important consequences, include review mechanisms and paths to correct documents and inadequate results.
Difference between embedding and embedding model
An embedding is the vector produced for an item. An embedding model is the trained system that calculates those vectors. The same application can generate embeddings with different models, but their representations should not be mixed without checking compatibility.