Generate text embedding

Text embedding is a technique for representing text data as numerical vectors. It uses deep neural networks to learn the patterns in large amounts of text data and generate vector representations that capture the meaning and context of the text. These vectors are used for a variety of natural language processing tasks such as sentiment analysis, text classification, and text similarity. Generating text embedding is commonly used as a preliminary step before other machine learning tasks. Further information can be found here (opens new window).

Generate text embedding action

Input

Field	Description
Deployment ID	Enter the deployment ID of the model you plan to use. You can find the deployment ID in Azure AI Studio > Deployment.
Input	Enter the text for which to obtain an embedding. The input must not exceed 8192 tokens (approximately 6000 words).
User	A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.

Output

Field Description Object The type of object the data value is. Commonly this is a list with a single value. Model The model used for text embedding. Embedding A list containing the embedding scores for the text that you have inputted. This is generally used in tandem with another machine learning model. An array of embeddings is returned if an array of inputs was provided. Usage Prompt Tokens The number of tokens used by the prompt. Total Tokens The total number of tokens used by the prompt and response.