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An embedding is a list of numbers—a vector—that a model produces from text or other data to preserve selected aspects of its content or meaning. Software can compare these vectors to find related items, which is why embeddings are central to semantic search. The numbers work as a whole: they are not usually a set of individually labeled, human-readable meaning dimensions.
What is an embedding?
An embedding is a numerical representation of an input, such as a word, sentence, document passage, image, or other data. A model maps the input to a vector: an ordered list of values that represents learned patterns in the input. OpenAI defines an embedding as a vector representation intended to preserve aspects of data’s content or meaning (OpenAI API: Key concepts).
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The vector is useful because software can calculate how close it is to other vectors. That makes embeddings practical for finding related items, grouping data, and other tasks where relationships among inputs matter.
How do embeddings represent meaning?
During training, a model learns to map inputs into a high-dimensional space. You can picture each vector as coordinates on a map of learned relationships: inputs that share relevant patterns may land near one another. The map has many axes, however, and an axis generally does not correspond to a simple, named quality such as “happy,” “formal,” or “about dogs.” Google’s Machine Learning Crash Course cautions that “Sometimes what the dimensions represent is not intuitively interpretable” (Google for Developers: Embedding space and static embeddings).
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So “meaning as numbers” is a useful shorthand, not a claim that each number has a clear dictionary definition. The model’s learned representation is useful as a whole; looking at one coordinate usually does not tell you exactly what concept it encodes.
How semantic search uses embeddings
Traditional keyword search looks for matching words. Semantic search uses embeddings to find passages whose learned representations are close to the query’s, even when the wording differs. A typical retrieval flow is:
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- Prepare searchable passages. Break documents into passages and create an embedding for each passage using a selected model.
- Store the vectors. Keep the vectors alongside passage identifiers and text, often in a vector database that can index and search them.
- Embed the query. Convert the user’s question or search phrase into a vector using a compatible model and task setup.
- Compare and rank. Calculate similarity between the query vector and stored passage vectors, then return passages with the strongest scores.
Vector systems can use measures such as cosine similarity, dot product, or Euclidean distance. The suitable measure depends on the model’s vector conventions and the retrieval system. OpenAI describes cosine similarity for retrieval and notes that its API embeddings are L2-normalized by default; for those normalized vectors, dot product can calculate cosine similarity (OpenAI API: Vector embeddings; OpenAI Help Center: Embeddings FAQ). Vector databases may support different distance metrics, including dot product and Euclidean distance (Google Cloud: What is a vector database?).
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Vectors from unrelated models should not be assumed to share a common space. A query and its candidates generally need embeddings made with compatible models and conventions for their similarity scores to be meaningful.
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What does embedding similarity tell you?
A high similarity score means that two vectors are close according to the chosen measure. In search, that closeness is a useful signal for ranking candidates. It does not prove that two passages mean exactly the same thing, that one entails the other, or that either passage is factually correct. Nor does it establish that a result is relevant to a particular user’s intent. Treat similarity as a retrieval signal, not a truth check or a guarantee of interchangeable wording.
How many dimensions does an embedding have?
A vector’s number of dimensions is simply its number of values. There is no universal length: it depends on the model, and some APIs allow the caller to request a shorter vector.
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| OpenAI model | Default dimensions in OpenAI documentation accessed in 2026 | Can the vector be shortened? |
|---|---|---|
| text-embedding-3-small | 1,536 | Yes; OpenAI documents a dimensions parameter. Shortening can involve an accuracy trade-off. |
| text-embedding-3-large | 3,072 | Yes; OpenAI documents a dimensions parameter. Shortening can involve an accuracy trade-off. |
These are specifications for the named OpenAI models, not a standard for all embedding models. OpenAI’s documentation describes both the default dimensions and the option to reduce vector length (OpenAI API: Vector embeddings).
What else are embeddings used for?
Similarity-based retrieval is one common application, but embeddings can also support clustering, topic modeling, classification, and information retrieval. In each case, the vector gives software a representation it can compare or use as input to another procedure; the usefulness of the result depends on the model and task.
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