Embeddings in OpenAI are numerical representations of text, code, or other data that capture semantic meaning in a format machines can process. They are generated by machine learning models, such as OpenAI’s text-embedding-ada-002, which convert input text into fixed-length vectors (arrays of numbers). Each vector dimension represents a latent feature of the input, allowing similar concepts—like “dog” and "puppy"—to have vectors that are mathematically closer in the embedding space. For example, the embeddings for “cat” and “kitten” would also be nearby, while “car” would be farther away. This enables algorithms to compare and analyze relationships between words, sentences, or documents efficiently.
To create embeddings, OpenAI’s models process input text through multiple neural network layers. The model first tokenizes the text (breaking it into smaller units like words or subwords), then computes contextual relationships between tokens. The final output is a dense vector, typically with hundreds or thousands of dimensions. For instance, text-embedding-ada-002 produces 1536-dimensional vectors. These embeddings are normalized, meaning their lengths are scaled to 1, making similarity calculations like cosine similarity straightforward. Developers can access this via OpenAI’s API by sending a text string and receiving the vector in response. For example, a query like GET https://api.openai.com/v1/embeddings with the input “machine learning” returns a vector that numerically represents the phrase.
Developers use embeddings for tasks like semantic search, clustering, or recommendation systems. In search, embeddings allow matching user queries to relevant documents even if keywords don’t overlap. For example, a search for “how to train a model” could surface articles about “neural network optimization” because their embeddings are similar. They’re also used in classification (e.g., sentiment analysis) by training models on top of precomputed embeddings. When using OpenAI’s API, developers should consider factors like input length limits (8,192 tokens for text-embedding-ada-002) and cost per request. While embeddings simplify semantic analysis, they require careful handling—like choosing appropriate similarity metrics or preprocessing text—to ensure accurate results.