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An embedding is a list of numbers—a vector—that a model produces to represent selected aspects of an input’s content or meaning. Software can compare these vectors to find related text, images, or other data. The numbers work together as coordinates in a learned space; they usually are not individually labeled, readable measures of qualities such as “happy” or “about dogs.”
What is an embedding?
An embedding is a numerical representation of data, such as a sentence or document. A model maps the input to a vector, a fixed-length list of values, designed to preserve aspects of its content or meaning. OpenAI describes an embedding as a vector representation “meant to preserve aspects of its content and/or its meaning” (OpenAI API: Key concepts).
You can picture the vector as coordinates on a map of learned relationships. Inputs that are related in ways the model has learned may land near one another. The real space has many dimensions, far more than a two-dimensional map can show, and the analogy describes relative position—not how the model reasons.
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How do embeddings represent meaning?
The model processes an input and emits a vector. During training, it learns a mapping that makes the resulting numbers useful for particular tasks. Their significance is generally in the pattern across the vector, rather than in any one value standing for a human-readable concept. 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 shorthand: an embedding can preserve useful aspects of content or meaning, but it is not a tidy dictionary in which each coordinate has a known label. What information is useful depends on the model and task.
How does semantic search use embeddings?
- Prepare the searchable text. Split documents into passages appropriate to the application, then use an embedding model to turn each passage into a vector.
- Embed the query. Convert the user’s search into a vector using a compatible model and setup.
- Compare vectors. Measure the query vector against passage vectors, then rank candidates by the selected similarity or distance measure.
- Return likely matches. Show the highest-ranked passages for further reading or use in another application.
This can find a passage about “ways to reduce a household’s power use” for a query such as “how can I lower my electricity bill,” even when the wording differs. The match is based on learned relationships, not just exact word overlap. OpenAI documents embeddings for search and related uses in its vector embeddings guide. Vector databases can store and index embeddings and support similarity searches using measures such as cosine similarity, dot product, or Euclidean distance (Google Cloud: What is a vector database?).
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What does embedding similarity mean?
A similarity measure or distance function compares vectors geometrically. Cosine similarity compares their orientation; dot product is another common comparison; Euclidean distance measures straight-line distance in the vector space. Which method is appropriate depends on the system and how its vectors are produced and stored. These measures are not interchangeable in every setup.
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A high similarity score means that the vectors are close according to the chosen measure. It does not prove that two passages say exactly the same thing, that one supports or entails the other, or that either is factually correct. It is a ranking signal for finding likely related material, not a fact-check or a guarantee of relevance to an individual user.
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Model compatibility matters, too: vectors from different models—or produced for different tasks—should not be assumed to share a space where their scores are meaningful. Follow the model and retrieval system’s conventions.
How many dimensions does an embedding have?
The dimension count is the number of values in the vector. There is no universal length: it varies by model and may be configurable. OpenAI’s current documentation lists these default lengths for its text embedding models:
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| OpenAI model | Default dimensions |
|---|---|
| text-embedding-3-small | 1,536 |
| text-embedding-3-large | 3,072 |
These are OpenAI product specifications, not a standard for all embedding models. OpenAI’s API also supports requesting shorter vectors with the dimensions parameter; reducing the length can involve an accuracy trade-off. Check the OpenAI embedding-model documentation for current details.
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Because vectors can be compared or grouped, embeddings can support more than search. Common uses include clustering related items, organizing topics, classification, and information retrieval. An embedding is a representation used by a larger system; it is not, by itself, a search engine, a classification decision, or an explanation of why two items match.
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What normalization changes in OpenAI embeddings
OpenAI states that its API embeddings are L2-normalized to length 1 by default, including when vectors are shortened. For those normalized vectors, dot product can be used to compute cosine similarity (OpenAI Help Center: Embeddings FAQ). This is specific to OpenAI API behavior; do not assume other providers or systems use the same normalization convention.
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