> ## Documentation Index > Fetch the complete documentation index at: https://azure-foundry.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Creates an embedding vector representing the input text. ## OpenAPI ````yaml api-reference/openai-v1.yaml post /embeddings openapi: 3.0.0 info: title: Azure AI Foundry Models Service license: name: MIT url: https://github.com/openai/openai-openapi/blob/master/LICENSE version: v1 servers: - url: '{endpoint}/openai/v1' description: Azure AI Foundry Models APIs variables: endpoint: default: '' description: >- A supported Azure AI Foundry Models APIs endpoint, including protocol and hostname. For example: https://westus.api.cognitive.microsoft.com). security: - ApiKeyAuth: [] - ApiKeyAuth_: [] - OAuth2Auth: - https://cognitiveservices.azure.com/.default tags: - name: Chat - name: Embeddings - name: Evals - name: Files - name: Fine-tuning - name: Models - name: Responses - name: Vector Stores paths: /embeddings: post: tags: - Embeddings summary: Creates an embedding vector representing the input text. operationId: createEmbedding parameters: - name: api-version in: query required: false description: >- The explicit Azure AI Foundry Models API version to use for this request. `v1` if not otherwise specified. schema: $ref: '#/components/schemas/AzureAIFoundryModelsApiVersion' default: v1 requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/AzureCreateEmbeddingRequest' responses: '200': description: The request has succeeded. headers: apim-request-id: required: false description: A request ID used for troubleshooting purposes. schema: type: string content: application/json: schema: $ref: '#/components/schemas/OpenAI.CreateEmbeddingResponse' default: description: An unexpected error response. headers: apim-request-id: required: false description: A request ID used for troubleshooting purposes. schema: type: string content: application/json: schema: $ref: '#/components/schemas/AzureErrorResponse' components: schemas: AzureAIFoundryModelsApiVersion: type: string enum: - v1 - preview AzureCreateEmbeddingRequest: type: object required: - model - input properties: model: type: string description: The model to use for the embedding request. input: anyOf: - type: string - type: array items: type: string - type: array items: type: integer format: int32 - type: array items: type: array items: type: integer format: int32 description: >- Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. In addition to the per-input token limit, all embedding models enforce a maximum of 300,000 tokens summed across all inputs in a single request. encoding_format: type: string enum: - float - base64 description: >- The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/). default: float dimensions: type: integer format: int32 minimum: 1 description: >- The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models. user: type: string description: >- A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices#end-user-ids). OpenAI.CreateEmbeddingResponse: type: object required: - data - model - object - usage properties: data: type: array items: $ref: '#/components/schemas/OpenAI.Embedding' description: The list of embeddings generated by the model. model: type: string description: The name of the model used to generate the embedding. object: type: string enum: - list description: The object type, which is always "list". usage: type: object properties: prompt_tokens: type: integer format: int32 description: The number of tokens used by the prompt. total_tokens: type: integer format: int32 description: The total number of tokens used by the request. required: - prompt_tokens - total_tokens description: The usage information for the request. AzureErrorResponse: type: object properties: error: type: object properties: code: type: string description: The distinct, machine-generated identifier for the error. message: type: string description: A human-readable message associated with the error. param: type: string description: >- If applicable, the request input parameter associated with the error type: type: string enum: - error description: The object type, always 'error.' inner_error: {} description: The error details. OpenAI.Embedding: type: object required: - index - embedding - object properties: index: type: integer format: int32 description: The index of the embedding in the list of embeddings. embedding: anyOf: - type: array items: type: number - type: string description: >- The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the [embedding guide](/docs/guides/embeddings). object: type: string enum: - embedding description: The object type, which is always "embedding". description: Represents an embedding vector returned by embedding endpoint. securitySchemes: ApiKeyAuth: type: apiKey in: header name: api-key ApiKeyAuth_: type: apiKey in: header name: authorization OAuth2Auth: type: oauth2 flows: implicit: authorizationUrl: https://login.microsoftonline.com/common/oauth2/v2.0/authorize scopes: https://cognitiveservices.azure.com/.default: '' ````