Azure OpenAI Service REST API reference

This article provides details on the inference REST API endpoints for Azure OpenAI.

Authentication

Azure OpenAI provides two methods for authentication. You can use either API Keys or Microsoft Entra ID.

  • API Key authentication: For this type of authentication, all API requests must include the API Key in the api-key HTTP header. The Quickstart provides guidance for how to make calls with this type of authentication.

  • Microsoft Entra ID authentication: You can authenticate an API call using a Microsoft Entra token. Authentication tokens are included in a request as the Authorization header. The token provided must be preceded by Bearer, for example Bearer YOUR_AUTH_TOKEN. You can read our how-to guide on authenticating with Microsoft Entra ID.

REST API versioning

The service APIs are versioned using the api-version query parameter. All versions follow the YYYY-MM-DD date structure. For example:

POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/completions?api-version=2024-02-01

Completions

With the Completions operation, the model generates one or more predicted completions based on a provided prompt. The service can also return the probabilities of alternative tokens at each position.

Create a completion

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/completions?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
deployment-id string Required The deployment name you chose when you deployed the model.
api-version string Required The API version to use for this operation. This follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
prompt string or array Optional <\|endoftext\|> The prompt or prompts to generate completions for, encoded as a string, or array of strings. <\|endoftext\|> is the document separator that the model sees during training, so if a prompt isn't specified the model generates as if from the beginning of a new document.
max_tokens integer Optional 16 The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).
temperature number Optional 1 What sampling temperature to use, between 0 and 2. Higher values mean the model takes more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both.
top_p number Optional 1 An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
logit_bias map Optional null Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.
user string Optional A unique identifier representing your end-user, which can help monitoring and detecting abuse
n integer Optional 1 How many completions to generate for each prompt. Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.
stream boolean Optional False Whether to stream back partial progress. If set, tokens are sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.
logprobs integer Optional null Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 10, the API will return a list of the 10 most likely tokens. The API will always return the logprob of the sampled token, so there might be up to logprobs+1 elements in the response. This parameter cannot be used with gpt-35-turbo.
suffix string Optional null The suffix that comes after a completion of inserted text.
echo boolean Optional False Echo back the prompt in addition to the completion. This parameter cannot be used with gpt-35-turbo.
stop string or array Optional null Up to four sequences where the API will stop generating further tokens. The returned text won't contain the stop sequence. For GPT-4 Turbo with Vision, up to two sequences are supported.
presence_penalty number Optional 0 Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
frequency_penalty number Optional 0 Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
best_of integer Optional 1 Generates best_of completions server-side and returns the "best" (the one with the lowest log probability per token). Results can't be streamed. When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n. Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop. This parameter cannot be used with gpt-35-turbo.

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/completions?api-version=2024-02-01\
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d "{
  \"prompt\": \"Once upon a time\",
  \"max_tokens\": 5
}"

Example response

{
    "id": "cmpl-4kGh7iXtjW4lc9eGhff6Hp8C7btdQ",
    "object": "text_completion",
    "created": 1646932609,
    "model": "ada",
    "choices": [
        {
            "text": ", a dark line crossed",
            "index": 0,
            "logprobs": null,
            "finish_reason": "length"
        }
    ]
}

In the example response, finish_reason equals stop. If finish_reason equals content_filter consult our content filtering guide to understand why this is occurring.

Embeddings

Get a vector representation of a given input that can be easily consumed by machine learning models and other algorithms.

Note

OpenAI currently allows a larger number of array inputs with text-embedding-ada-002. Azure OpenAI currently supports input arrays up to 16 for text-embedding-ada-002 (Version 2). Both require the max input token limit per API request to remain under 8191 for this model.

Create an embedding

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/embeddings?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
deployment-id string Required The name of your model deployment. You're required to first deploy a model before you can make calls.
api-version string Required The API version to use for this operation. This follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
input string or array Yes N/A Input text to get embeddings for, encoded as an array or string. The number of input tokens varies depending on what model you're using. Only text-embedding-ada-002 (Version 2) supports array input.
user string No Null A unique identifier representing your end-user. This will help Azure OpenAI monitor and detect abuse. Do not pass PII identifiers instead use pseudoanonymized values such as GUIDs
encoding_format string No float The format to return the embeddings in. Can be either float or base64. Defaults to float.

[Added in 2024-03-01-preview].
dimensions integer No The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

[Added in 2024-03-01-preview]

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/embeddings?api-version=2024-02-01 \
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d "{\"input\": \"The food was delicious and the waiter...\"}"

Example response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.018990106880664825,
        -0.0073809814639389515,
        .... (1024 floats total for ada)
        0.021276434883475304,
      ],
      "index": 0
    }
  ],
  "model": "text-similarity-babbage:001"
}

Chat completions

Create completions for chat messages with the GPT-35-Turbo and GPT-4 models.

Create chat completions

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/chat/completions?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
deployment-id string Required The name of your model deployment. You're required to first deploy a model before you can make calls.
api-version string Required The API version to use for this operation. This follows the YYYY-MM-DD or YYYY-MM-DD-preview format.

Supported versions

Request body

The request body consists of a series of messages. The model will generate a response to the last message, using earlier messages as context.

Parameter Type Required? Default Description
messages array Yes N/A The series of messages associated with this chat completion request. It should include previous messages in the conversation. Each message has a role and content.
role string Yes N/A Indicates who is giving the current message. Can be system,user,assistant,tool, or function.
content string or array Yes N/A The content of the message. It must be a string, unless in a Vision-enabled scenario. If it's part of the user message, using the GPT-4 Turbo with Vision model, with the latest API version, then content must be an array of structures, where each item represents either text or an image:
  • text: input text is represented as a structure with the following properties:
    • type = "text"
    • text = the input text
  • images: an input image is represented as a structure with the following properties:
    • type = "image_url"
    • image_url = a structure with the following properties:
      • url = the image URL
      • (optional) detail = high, low, or auto
contentPart object No N/A Part of a user's multi-modal message. It can be either text type or image type. If text, it will be a text string. If image, it will be a contentPartImage object.
contentPartImage object No N/A Represents a user-uploaded image. It has a url property, which is either a URL of the image or the base 64 encoded image data. It also has a detail property which can be auto, low, or high.
enhancements object No N/A Represents the Vision enhancement features requested for the chat. It has grounding and ocr properties, each has a boolean enabled property. Use these to request the OCR service and/or the object detection/grounding service [This preview parameter is not available in the 2024-02-01 GA API].
dataSources object No N/A Represents additional resource data. Computer Vision resource data is needed for Vision enhancement. It has a type property, which should be "AzureComputerVision" and a parameters property, which has an endpoint and key property. These strings should be set to the endpoint URL and access key of your Computer Vision resource.

Example request

Text-only chat

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/chat/completions?api-version=2024-02-01 \
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d '{"messages":[{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},{"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},{"role": "user", "content": "Do other Azure AI services support this too?"}]}'

Chat with vision

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/chat/completions?api-version=2023-12-01-preview \
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d '{"messages":[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":[{"type":"text","text":"Describe this picture:"},{ "type": "image_url", "image_url": { "url": "https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png", "detail": "high" } }]}]}'

Enhanced chat with vision

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/extensions/chat/completions?api-version=2023-12-01-preview \
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d '{"enhancements":{"ocr":{"enabled":true},"grounding":{"enabled":true}},"dataSources":[{"type":"AzureComputerVision","parameters":{"endpoint":" <Computer Vision Resource Endpoint> ","key":"<Computer Vision Resource Key>"}}],"messages":[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":[{"type":"text","text":"Describe this picture:"},{"type":"image_url","image_url":"https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png"}]}]}'

Example response

{
    "id": "chatcmpl-6v7mkQj980V1yBec6ETrKPRqFjNw9",
    "object": "chat.completion",
    "created": 1679072642,
    "model": "gpt-35-turbo",
    "usage":
    {
        "prompt_tokens": 58,
        "completion_tokens": 68,
        "total_tokens": 126
    },
    "choices":
    [
        {
            "message":
            {
                "role": "assistant",
                "content": "Yes, other Azure AI services also support customer managed keys.
                    Azure AI services offer multiple options for customers to manage keys, such as
                    using Azure Key Vault, customer-managed keys in Azure Key Vault or
                    customer-managed keys through Azure Storage service. This helps customers ensure
                    that their data is secure and access to their services is controlled."
            },
            "finish_reason": "stop",
            "index": 0
        }
    ]
}

Output formatting adjusted for ease of reading, actual output is a single block of text without line breaks.

In the example response, finish_reason equals stop. If finish_reason equals content_filter consult our content filtering guide to understand why this is occurring.

Important

The functions and function_call parameters have been deprecated with the release of the 2023-12-01-preview version of the API. The replacement for functions is the tools parameter. The replacement for function_call is the tool_choice parameter. Parallel function calling which was introduced as part of the 2023-12-01-preview is only supported with gpt-35-turbo (1106) and gpt-4 (1106-preview) also known as GPT-4 Turbo Preview.

Parameter Type Required? Default Description
messages array Required The collection of context messages associated with this chat completions request. Typical usage begins with a chat message for the System role that provides instructions for the behavior of the assistant, followed by alternating messages between the User and Assistant roles.
temperature number Optional 1 What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
n integer Optional 1 How many chat completion choices to generate for each input message.
stream boolean Optional false If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message."
stop string or array Optional null Up to 4 sequences where the API will stop generating further tokens.
max_tokens integer Optional inf The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).
presence_penalty number Optional 0 Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
frequency_penalty number Optional 0 Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
logit_bias object Optional null Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect varies per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
user string Optional A unique identifier representing your end-user, which can help Azure OpenAI to monitor and detect abuse.
function_call Optional [Deprecated in 2023-12-01-preview replacement parameter is tools_choice]Controls how the model responds to function calls. "none" means the model doesn't call a function, and responds to the end-user. auto means the model can pick between an end-user or calling a function. Specifying a particular function via {"name": "my_function"} forces the model to call that function. "none" is the default when no functions are present. auto is the default if functions are present. This parameter requires API version 2023-07-01-preview
functions FunctionDefinition[] Optional [Deprecated in 2023-12-01-preview replacement paremeter is tools] A list of functions the model can generate JSON inputs for. This parameter requires API version 2023-07-01-preview
tools string (The type of the tool. Only function is supported.) Optional A list of tools the model can call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model can generate JSON inputs for. This parameter requires API version 2023-12-01-preview
tool_choice string or object Optional none is the default when no functions are present. auto is the default if functions are present. Controls which (if any) function is called by the model. None means the model won't call a function and instead generates a message. auto means the model can pick between generating a message or calling a function. Specifying a particular function via {"type: "function", "function": {"name": "my_function"}} forces the model to call that function. This parameter requires API version 2023-12-01-preview or later.

ChatMessage

A single, role-attributed message within a chat completion interaction.

Name Type Description
content string The text associated with this message payload.
function_call FunctionCall The name and arguments of a function that should be called, as generated by the model.
name string The name of the author of this message. name is required if role is function, and it should be the name of the function whose response is in the content. Can contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
role ChatRole The role associated with this message payload

ChatRole

A description of the intended purpose of a message within a chat completions interaction.

Name Type Description
assistant string The role that provides responses to system-instructed, user-prompted input.
function string The role that provides function results for chat completions.
system string The role that instructs or sets the behavior of the assistant.
user string The role that provides input for chat completions.

Function

This is used with the tools parameter that was added in API version 2023-12-01-preview.

Name Type Description
description string A description of what the function does, used by the model to choose when and how to call the function
name string The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64
parameters object The parameters the functions accepts, described as a JSON Schema object. See the JSON Schema reference for documentation about the format."

FunctionCall-Deprecated

The name and arguments of a function that should be called, as generated by the model. This requires API version 2023-07-01-preview

Name Type Description
arguments string The arguments to call the function with, as generated by the model in JSON format. The model doesn't always generate valid JSON, and might fabricate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
name string The name of the function to call.

FunctionDefinition-Deprecated

The definition of a caller-specified function that chat completions can invoke in response to matching user input. This requires API version 2023-07-01-preview

Name Type Description
description string A description of what the function does. The model uses this description when selecting the function and interpreting its parameters.
name string The name of the function to be called.
parameters The parameters the functions accepts, described as a JSON Schema object.

Completions extensions

Extensions for chat completions, for example Azure OpenAI On Your Data.

Important

The following information is for version 2023-12-01-preview of the API. This is not the current version of the API. To find the latest reference documentation, see Azure OpenAI On Your Data reference.

Use chat completions extensions

POST {your-resource-name}/openai/deployments/{deployment-id}/extensions/chat/completions?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
deployment-id string Required The name of your model deployment. You're required to first deploy a model before you can make calls.
api-version string Required The API version to use for this operation. This follows the YYYY-MM-DD format.

Supported versions

Example request

You can make requests using Azure AI Search, Azure Cosmos DB for MongoDB vCore, Pinecone, and Elasticsearch. For more information, see Azure OpenAI On Your Data.

curl -i -X POST YOUR_RESOURCE_NAME/openai/deployments/YOUR_DEPLOYMENT_NAME/extensions/chat/completions?api-version=2023-06-01-preview \
-H "Content-Type: application/json" \
-H "api-key: YOUR_API_KEY" \
-d \
'
{
    "temperature": 0,
    "max_tokens": 1000,
    "top_p": 1.0,
    "dataSources": [
        {
            "type": "AzureCognitiveSearch",
            "parameters": {
                "endpoint": "YOUR_AZURE_COGNITIVE_SEARCH_ENDPOINT",
                "key": "YOUR_AZURE_COGNITIVE_SEARCH_KEY",
                "indexName": "YOUR_AZURE_COGNITIVE_SEARCH_INDEX_NAME"
            }
        }
    ],
    "messages": [
        {
            "role": "user",
            "content": "What are the differences between Azure Machine Learning and Azure AI services?"
        }
    ]
}
'
Azure Cosmos DB for MongoDB vCore
curl -i -X POST YOUR_RESOURCE_NAME/openai/deployments/YOUR_DEPLOYMENT_NAME/extensions/chat/completions?api-version=2023-06-01-preview \
-H "Content-Type: application/json" \
-H "api-key: YOUR_API_KEY" \
-d \
'
{
    "temperature": 0,
    "top_p": 1.0,
    "max_tokens": 800,
    "stream": false,
    "messages": [
        {
            "role": "user",
            "content": "What is the company insurance plan?"
        }
    ],
    "dataSources": [
        {
            "type": "AzureCosmosDB",
            "parameters": {
                "authentication": {
                    "type": "ConnectionString",
                    "connectionString": "mongodb+srv://onyourdatatest:{password}$@{cluster-name}.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"
                },
                "databaseName": "vectordb",
                "containerName": "azuredocs",
                "indexName": "azuredocindex",
                "embeddingDependency": {
                    "type": "DeploymentName",
                    "deploymentName": "{embedding deployment name}"
                },
                "fieldsMapping": {
                    "vectorFields": [
                        "contentvector"
                    ]
                }
            }
        }
    ]
}
'
Elasticsearch
curl -i -X POST YOUR_RESOURCE_NAME/openai/deployments/YOUR_DEPLOYMENT_NAME/extensions/chat/completions?api-version=2023-12-01-preview \
-H "Content-Type: application/json" \
-H "api-key: YOUR_API_KEY" \
-d \
{
  "messages": [
    {
      "role": "system",
      "content": "you are a helpful assistant that talks like a pirate"
    },
    {
      "role": "user",
      "content": "can you tell me how to care for a parrot?"
    }
  ],
  "dataSources": [
    {
      "type": "Elasticsearch",
      "parameters": {
        "endpoint": "{search endpoint}",
        "indexName": "{index name}",
        "authentication": {
          "type": "KeyAndKeyId",
          "key": "{key}",
          "keyId": "{key id}"
        }
      }
    }
  ]
}
Azure Machine Learning
curl -i -X POST YOUR_RESOURCE_NAME/openai/deployments/YOUR_DEPLOYMENT_NAME/extensions/chat/completions?api-version=2023-12-01-preview \
-H "Content-Type: application/json" \
-H "api-key: YOUR_API_KEY" \
-d \
'
{
  "messages": [
    {
      "role": "system",
      "content": "you are a helpful assistant that talks like a pirate"
    },
    {
      "role": "user",
      "content": "can you tell me how to care for a parrot?"
    }
  ],
  "dataSources": [
    {
      "type": "AzureMLIndex",
      "parameters": {
        "projectResourceId": "/subscriptions/{subscription-id}/resourceGroups/{resource-group-name}/providers/Microsoft.MachineLearningServices/workspaces/{workspace-id}",
        "name": "my-project",
        "version": "5"
      }
    }
  ]
}
'
Pinecone
curl -i -X POST YOUR_RESOURCE_NAME/openai/deployments/YOUR_DEPLOYMENT_NAME/extensions/chat/completions?api-version=2023-12-01-preview \
-H "Content-Type: application/json" \
-H "api-key: YOUR_API_KEY" \
-d \
'
{
  "messages": [
    {
      "role": "system",
      "content": "you are a helpful assistant that talks like a pirate"
    },
    {
      "role": "user",
      "content": "can you tell me how to care for a parrot?"
    }
  ],
  "dataSources": [
    {
      "type": "Pinecone",
      "parameters": {
        "authentication": {
          "type": "APIKey",
          "apiKey": "{api key}"
        },
        "environment": "{environment name}",
        "indexName": "{index name}",
        "embeddingDependency": {
          "type": "DeploymentName",
          "deploymentName": "{embedding deployment name}"
        },
        "fieldsMapping": {
          "titleField": "title",
          "urlField": "url",
          "filepathField": "filepath",
          "contentFields": [
            "content"
          ],
          "contentFieldsSeparator": "\n"
        }
      }
    }
  ]
}
'

Example response

{
    "id": "12345678-1a2b-3c4e5f-a123-12345678abcd",
    "model": "",
    "created": 1684304924,
    "object": "chat.completion",
    "choices": [
        {
            "index": 0,
            "messages": [
                {
                    "role": "tool",
                    "content": "{\"citations\": [{\"content\": \"\\nAzure AI services are cloud-based artificial intelligence (AI) services...\", \"id\": null, \"title\": \"What is Azure AI services\", \"filepath\": null, \"url\": null, \"metadata\": {\"chunking\": \"orignal document size=250. Scores=0.4314117431640625 and 1.72564697265625.Org Highlight count=4.\"}, \"chunk_id\": \"0\"}], \"intent\": \"[\\\"Learn about Azure AI services.\\\"]\"}",
                    "end_turn": false
                },
                {
                    "role": "assistant",
                    "content": " \nAzure AI services are cloud-based artificial intelligence (AI) services that help developers build cognitive intelligence into applications without having direct AI or data science skills or knowledge. [doc1]. Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. [doc1].",
                    "end_turn": true
                }
            ]
        }
    ]
}
Parameters Type Required? Default Description
messages array Required null The messages to generate chat completions for, in the chat format.
dataSources array Required The data sources to be used for the Azure OpenAI On Your Data feature.
temperature number Optional 0 What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
top_p number Optional 1 An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
stream boolean Optional false If set, partial message deltas are sent, like in ChatGPT. Tokens are sent as data-only server-sent events as they become available, with the stream terminated by a message "messages": [{"delta": {"content": "[DONE]"}, "index": 2, "end_turn": true}]
stop string or array Optional null Up to two sequences where the API will stop generating further tokens.
max_tokens integer Optional 1000 The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return is 4096 - prompt_tokens.

The following parameters can be used inside of the parameters field inside of dataSources.

Parameters Type Required? Default Description
type string Required null The data source to be used for the Azure OpenAI On Your Data feature. For Azure AI Search the value is AzureCognitiveSearch. For Azure Cosmos DB for MongoDB vCore, the value is AzureCosmosDB. For Elasticsearch the value is Elasticsearch. For Azure Machine Learning, the value is AzureMLIndex. For Pinecone, the value is Pinecone.
indexName string Required null The search index to be used.
inScope boolean Optional true If set, this value limits responses specific to the grounding data content.
topNDocuments number Optional 5 Specifies the number of top-scoring documents from your data index used to generate responses. You might want to increase the value when you have short documents or want to provide more context. This is the retrieved documents parameter in Azure OpenAI studio.
semanticConfiguration string Optional null The semantic search configuration. Only required when queryType is set to semantic or vectorSemanticHybrid.
roleInformation string Optional null Gives the model instructions about how it should behave and the context it should reference when generating a response. Corresponds to the "System Message" in Azure OpenAI Studio. See Using your data for more information. There’s a 100 token limit, which counts towards the overall token limit.
filter string Optional null The filter pattern used for restricting access to sensitive documents
embeddingEndpoint string Optional null The endpoint URL for an Ada embedding model deployment, generally of the format https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/embeddings?api-version=2023-05-15. Use with the embeddingKey parameter for vector search outside of private networks and private endpoints.
embeddingKey string Optional null The API key for an Ada embedding model deployment. Use with embeddingEndpoint for vector search outside of private networks and private endpoints.
embeddingDeploymentName string Optional null The Ada embedding model deployment name within the same Azure OpenAI resource. Used instead of embeddingEndpoint and embeddingKey for vector search. Should only be used when both the embeddingEndpoint and embeddingKey parameters are defined. When this parameter is provided, Azure OpenAI On Your Data use an internal call to evaluate the Ada embedding model, rather than calling the Azure OpenAI endpoint. This enables you to use vector search in private networks and private endpoints. Billing remains the same whether this parameter is defined or not. Available in regions where embedding models are available starting in API versions 2023-06-01-preview and later.
strictness number Optional 3 Sets the threshold to categorize documents as relevant to your queries. Raising the value means a higher threshold for relevance and filters out more less-relevant documents for responses. Setting this value too high might cause the model to fail to generate responses due to limited available documents.

Azure AI Search parameters

The following parameters are used for Azure AI Search.

Parameters Type Required? Default Description
endpoint string Required null Azure AI Search only. The data source endpoint.
key string Required null Azure AI Search only. One of the Azure AI Search admin keys for your service.
queryType string Optional simple Indicates which query option is used for Azure AI Search. Available types: simple, semantic, vector, vectorSimpleHybrid, vectorSemanticHybrid.
fieldsMapping dictionary Optional for Azure AI Search. null defines which fields you want to map when you add your data source.

The following parameters are used inside of the authentication field, which enables you to use Azure OpenAI without public network access.

Parameters Type Required? Default Description
type string Required null The authentication type.
managedIdentityResourceId string Required null The resource ID of the user-assigned managed identity to use for authentication.
"authentication": {
  "type": "UserAssignedManagedIdentity",
  "managedIdentityResourceId": "/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{resource-name}"
},

The following parameters are used inside of the fieldsMapping field.

Parameters Type Required? Default Description
titleField string Optional null The field in your index that contains the original title of each document.
urlField string Optional null The field in your index that contains the original URL of each document.
filepathField string Optional null The field in your index that contains the original file name of each document.
contentFields dictionary Optional null The fields in your index that contain the main text content of each document.
contentFieldsSeparator string Optional null The separator for the content fields. Use \n by default.
"fieldsMapping": {
  "titleField": "myTitleField",
  "urlField": "myUrlField",
  "filepathField": "myFilePathField",
  "contentFields": [
    "myContentField"
  ],
  "contentFieldsSeparator": "\n"
}

The following parameters are used inside of the optional embeddingDependency parameter, which contains details of a vectorization source that is based on an internal embeddings model deployment name in the same Azure OpenAI resource.

Parameters Type Required? Default Description
deploymentName string Optional null The type of vectorization source to use.
type string Optional null The embedding model deployment name, located within the same Azure OpenAI resource. This enables you to use vector search without an Azure OpenAI API key and without Azure OpenAI public network access.
"embeddingDependency": {
  "type": "DeploymentName",
  "deploymentName": "{embedding deployment name}"
},

Azure Cosmos DB for MongoDB vCore parameters

The following parameters are used for Azure Cosmos DB for MongoDB vCore.

Parameters Type Required? Default Description
type (found inside of authentication) string Required null Azure Cosmos DB for MongoDB vCore only. The authentication to be used For. Azure Cosmos Mongo vCore, the value is ConnectionString
connectionString string Required null Azure Cosmos DB for MongoDB vCore only. The connection string to be used for authenticate Azure Cosmos Mongo vCore Account.
databaseName string Required null Azure Cosmos DB for MongoDB vCore only. The Azure Cosmos Mongo vCore database name.
containerName string Required null Azure Cosmos DB for MongoDB vCore only. The Azure Cosmos Mongo vCore container name in the database.
type (found inside ofembeddingDependencyType) string Required null Indicates the embedding model dependency.
deploymentName (found inside ofembeddingDependencyType) string Required null The embedding model deployment name.
fieldsMapping dictionary Required for Azure Cosmos DB for MongoDB vCore. null Index data column mapping. When you use Azure Cosmos DB for MongoDB vCore, the value vectorFields is required, which indicates the fields that store vectors.

The following parameters are used inside of the optional embeddingDependency parameter, which contains details of a vectorization source that is based on an internal embeddings model deployment name in the same Azure OpenAI resource.

Parameters Type Required? Default Description
deploymentName string Optional null The type of vectorization source to use.
type string Optional null The embedding model deployment name, located within the same Azure OpenAI resource. This enables you to use vector search without an Azure OpenAI API key and without Azure OpenAI public network access.
"embeddingDependency": {
  "type": "DeploymentName",
  "deploymentName": "{embedding deployment name}"
},

Elasticsearch parameters

The following parameters are used for Elasticsearch.

Parameters Type Required? Default Description
endpoint string Required null The endpoint for connecting to Elasticsearch.
indexName string Required null The name of the Elasticsearch index.
type (found inside of authentication) string Required null The authentication to be used. For Elasticsearch, the value is KeyAndKeyId.
key (found inside of authentication) string Required null The key used to connect to Elasticsearch.
keyId (found inside of authentication) string Required null The key ID to be used. For Elasticsearch.

The following parameters are used inside of the fieldsMapping field.

Parameters Type Required? Default Description
titleField string Optional null The field in your index that contains the original title of each document.
urlField string Optional null The field in your index that contains the original URL of each document.
filepathField string Optional null The field in your index that contains the original file name of each document.
contentFields dictionary Optional null The fields in your index that contain the main text content of each document.
contentFieldsSeparator string Optional null The separator for the content fields. Use \n by default.
vectorFields dictionary Optional null The names of fields that represent vector data
"fieldsMapping": {
  "titleField": "myTitleField",
  "urlField": "myUrlField",
  "filepathField": "myFilePathField",
  "contentFields": [
    "myContentField"
  ],
  "contentFieldsSeparator": "\n",
  "vectorFields": [
    "myVectorField"
  ]
}

The following parameters are used inside of the optional embeddingDependency parameter, which contains details of a vectorization source that is based on an internal embeddings model deployment name in the same Azure OpenAI resource.

Parameters Type Required? Default Description
deploymentName string Optional null The type of vectorization source to use.
type string Optional null The embedding model deployment name, located within the same Azure OpenAI resource. This enables you to use vector search without an Azure OpenAI API key and without Azure OpenAI public network access.
"embeddingDependency": {
  "type": "DeploymentName",
  "deploymentName": "{embedding deployment name}"
},

Azure Machine Learning parameters

The following parameters are used for Azure Machine Learning.

Parameters Type Required? Default Description
projectResourceId string Required null The project resource ID.
name string Required null The name of the Azure Machine Learning project name.
version (found inside of authentication) string Required null The version of the Azure Machine Learning vector index.

The following parameters are used inside of the optional embeddingDependency parameter, which contains details of a vectorization source that is based on an internal embeddings model deployment name in the same Azure OpenAI resource.

Parameters Type Required? Default Description
deploymentName string Optional null The type of vectorization source to use.
type string Optional null The embedding model deployment name, located within the same Azure OpenAI resource. This enables you to use vector search without an Azure OpenAI API key and without Azure OpenAI public network access.
"embeddingDependency": {
  "type": "DeploymentName",
  "deploymentName": "{embedding deployment name}"
},

Pinecone parameters

The following parameters are used for Pinecone.

Parameters Type Required? Default Description
type (found inside of authentication) string Required null The authentication to be used. For Pinecone, the value is APIKey.
apiKey (found inside of authentication) string Required null The API key for Pinecone.
environment string Required null The name of the Pinecone environment.
indexName string Required null The name of the Pinecone index.
embeddingDependency string Required null The embedding dependency for vector search.
type (found inside of embeddingDependency) string Required null The type of dependency. For Pinecone the value is DeploymentName.
deploymentName (found inside of embeddingDependency) string Required null The name of the deployment.
titleField (found inside of fieldsMapping) string Required null The name of the index field to use as a title.
urlField (found inside of fieldsMapping) string Required null The name of the index field to use as a URL.
filepathField (found inside of fieldsMapping) string Required null The name of the index field to use as a file path.
contentFields (found inside of fieldsMapping) string Required null The name of the index fields that should be treated as content.
vectorFields dictionary Optional null The names of fields that represent vector data
contentFieldsSeparator (found inside of fieldsMapping) string Required null The separator for your content fields. Use \n by default.

The following parameters are used inside of the optional embeddingDependency parameter, which contains details of a vectorization source that is based on an internal embeddings model deployment name in the same Azure OpenAI resource.

Parameters Type Required? Default Description
deploymentName string Optional null The type of vectorization source to use.
type string Optional null The embedding model deployment name, located within the same Azure OpenAI resource. This enables you to use vector search without an Azure OpenAI API key and without Azure OpenAI public network access.
"embeddingDependency": {
  "type": "DeploymentName",
  "deploymentName": "{embedding deployment name}"
},

Start an ingestion job (preview)

Tip

The JOB_NAME you choose will be used as the index name. Be aware of the constraints for the index name.

curl -i -X PUT https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-your-data/ingestion-jobs/JOB_NAME?api-version=2023-10-01-preview \ 
-H "Content-Type: application/json" \ 
-H "api-key: YOUR_API_KEY" \ 
-H "searchServiceEndpoint: https://YOUR_AZURE_COGNITIVE_SEARCH_NAME.search.windows.net" \ 
-H "searchServiceAdminKey: YOUR_SEARCH_SERVICE_ADMIN_KEY" \ 
-H  "storageConnectionString: YOUR_STORAGE_CONNECTION_STRING" \ 
-H "storageContainer: YOUR_INPUT_CONTAINER" \ 
-d '{ "dataRefreshIntervalInMinutes": 10 }'

Example response

{ 
    "id": "test-1", 
    "dataRefreshIntervalInMinutes": 10, 
    "completionAction": "cleanUpAssets", 
    "status": "running", 
    "warnings": [], 
    "progress": { 
        "stageProgress": [ 
            { 
                "name": "Preprocessing", 
                "totalItems": 100, 
                "processedItems": 100 
            }, 
            { 
                "name": "Indexing", 
                "totalItems": 350, 
                "processedItems": 40 
            } 
        ] 
    } 
} 

Header Parameters

Parameters Type Required? Default Description
searchServiceEndpoint string Required null The endpoint of the search resource in which the data will be ingested.
searchServiceAdminKey string Optional null If provided, the key is used to authenticate with the searchServiceEndpoint. If not provided, the system-assigned identity of the Azure OpenAI resource will be used. In this case, the system-assigned identity must have "Search Service Contributor" role assignment on the search resource.
storageConnectionString string Required null The connection string for the storage account where the input data is located. An account key has to be provided in the connection string. It should look something like DefaultEndpointsProtocol=https;AccountName=<your storage account>;AccountKey=<your account key>
storageContainer string Required null The name of the container where the input data is located.
embeddingEndpoint string Optional null Not required if you use semantic or only keyword search. It's required if you use vector, hybrid, or hybrid + semantic search
embeddingKey string Optional null The key of the embedding endpoint. This is required if the embedding endpoint isn't empty.
url string Optional null If URL isn't null, the provided url is crawled into the provided storage container and then ingested accordingly.

Body Parameters

Parameters Type Required? Default Description
dataRefreshIntervalInMinutes string Required 0 The data refresh interval in minutes. If you want to run a single ingestion job without a schedule, set this parameter to 0.
completionAction string Optional cleanUpAssets What should happen to the assets created during the ingestion process upon job completion. Valid values are cleanUpAssets or keepAllAssets. keepAllAssets leaves all the intermediate assets for users interested in reviewing the intermediate results, which can be helpful for debugging assets. cleanUpAssets removes the assets after job completion.
chunkSize int Optional 1024 This number defines the maximum number of tokens in each chunk produced by the ingestion flow.

List ingestion jobs (preview)

curl -i -X GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-your-data/ingestion-jobs?api-version=2023-10-01-preview \ 
-H "api-key: YOUR_API_KEY"

Example response

{ 
    "value": [ 
        { 
            "id": "test-1", 
            "dataRefreshIntervalInMinutes": 10, 
            "completionAction": "cleanUpAssets", 
            "status": "succeeded", 
            "warnings": [] 
        }, 
        { 
            "id": "test-2", 
            "dataRefreshIntervalInMinutes": 10, 
            "completionAction": "cleanUpAssets", 
            "status": "failed", 
            "error": { 
                "code": "BadRequest", 
                "message": "Could not execute skill because the Web Api request failed." 
            }, 
            "warnings": [] 
        } 
    ] 
} 

Get the status of an ingestion job (preview)

curl -i -X GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-your-data/ingestion-jobs/YOUR_JOB_NAME?api-version=2023-10-01-preview \ 
-H "api-key: YOUR_API_KEY"

Example response body

{ 
    "id": "test-1", 
    "dataRefreshIntervalInMinutes": 10, 
    "completionAction": "cleanUpAssets", 
    "status": "succeeded", 
    "warnings": [] 
} 

Image generation

Request a generated image (DALL-E 3)

Generate and retrieve a batch of images from a text caption.

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/images/generations?api-version={api-version} 

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
deployment-id string Required The name of your DALL-E 3 model deployment such as MyDalle3. You're required to first deploy a DALL-E 3 model before you can make calls.
api-version string Required The API version to use for this operation. This follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
prompt string Required A text description of the desired image(s). The maximum length is 4000 characters.
n integer Optional 1 The number of images to generate. Only n=1 is supported for DALL-E 3.
size string Optional 1024x1024 The size of the generated images. Must be one of 1792x1024, 1024x1024, or 1024x1792.
quality string Optional standard The quality of the generated images. Must be hd or standard.
response_format string Optional url The format in which the generated images are returned Must be url (a URL pointing to the image) or b64_json (the base 64-byte code in JSON format).
style string Optional vivid The style of the generated images. Must be natural or vivid (for hyper-realistic / dramatic images).
user string Optional A unique identifier representing your end-user, which can help to monitor and detect abuse.

Example request

curl -X POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/images/generations?api-version=2023-12-01-preview \
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d '{
    "prompt": "An avocado chair",
    "size": "1024x1024",
    "n": 3,
    "quality": "hd", 
    "style": "vivid"
  }'

Example response

The operation returns a 202 status code and an GenerateImagesResponse JSON object containing the ID and status of the operation.

{ 
    "created": 1698116662, 
    "data": [ 
        { 
            "url": "url to the image", 
            "revised_prompt": "the actual prompt that was used" 
        }, 
        { 
            "url": "url to the image" 
        },
        ...
    ]
} 

Request a generated image (DALL-E 2 preview)

Generate a batch of images from a text caption.

POST https://{your-resource-name}.openai.azure.com/openai/images/generations:submit?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
api-version string Required The API version to use for this operation. This follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
prompt string Required A text description of the desired image(s). The maximum length is 1000 characters.
n integer Optional 1 The number of images to generate. Must be between 1 and 5.
size string Optional 1024x1024 The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.

Example request

curl -X POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/images/generations:submit?api-version=2023-06-01-preview \
  -H "Content-Type: application/json" \
  -H "api-key: YOUR_API_KEY" \
  -d '{
"prompt": "An avocado chair",
"size": "512x512",
"n": 3
}'

Example response

The operation returns a 202 status code and an GenerateImagesResponse JSON object containing the ID and status of the operation.

{
  "id": "f508bcf2-e651-4b4b-85a7-58ad77981ffa",
  "status": "notRunning"
}

Get a generated image result (DALL-E 2 preview)

Use this API to retrieve the results of an image generation operation. Image generation is currently only available with api-version=2023-06-01-preview.

GET https://{your-resource-name}.openai.azure.com/openai/operations/images/{operation-id}?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
operation-id string Required The GUID that identifies the original image generation request.

Supported versions

Example request

curl -X GET "https://{your-resource-name}.openai.azure.com/openai/operations/images/{operation-id}?api-version=2023-06-01-preview"
-H "Content-Type: application/json"
-H "Api-Key: {api key}"

Example response

Upon success the operation returns a 200 status code and an OperationResponse JSON object. The status field can be "notRunning" (task is queued but hasn't started yet), "running", "succeeded", "canceled" (task has timed out), "failed", or "deleted". A succeeded status indicates that the generated image is available for download at the given URL. If multiple images were generated, their URLs are all returned in the result.data field.

{
  "created": 1685064331,
  "expires": 1685150737,
  "id": "4b755937-3173-4b49-bf3f-da6702a3971a",
  "result": {
    "data": [
      {
        "url": "<URL_TO_IMAGE>"
      },
      {
        "url": "<URL_TO_NEXT_IMAGE>"
      },
      ...
    ]
  },
  "status": "succeeded"
}

Delete a generated image from the server (DALL-E 2 preview)

You can use the operation ID returned by the request to delete the corresponding image from the Azure server. Generated images are automatically deleted after 24 hours by default, but you can trigger the deletion earlier if you want to.

DELETE https://{your-resource-name}.openai.azure.com/openai/operations/images/{operation-id}?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI Resource.
operation-id string Required The GUID that identifies the original image generation request.

Supported versions

Example request

curl -X DELETE "https://{your-resource-name}.openai.azure.com/openai/operations/images/{operation-id}?api-version=2023-06-01-preview"
-H "Content-Type: application/json"
-H "Api-Key: {api key}"

Response

The operation returns a 204 status code if successful. This API only succeeds if the operation is in an end state (not running).

Speech to text

You can use a Whisper model in Azure OpenAI Service for speech to text transcription or speech translation. For more information about using a Whisper model, see the quickstart and the Whisper model overview.

Request a speech to text transcription

Transcribes an audio file.

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/audio/transcriptions?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI resource.
deployment-id string Required The name of your Whisper model deployment such as MyWhisperDeployment. You're required to first deploy a Whisper model before you can make calls.
api-version string Required The API version to use for this operation. This value follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
file file Yes N/A The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.

The file size limit for the Whisper model in Azure OpenAI Service is 25 MB. If you need to transcribe a file larger than 25 MB, break it into chunks. Alternatively you can use the Azure AI Speech batch transcription API.

You can get sample audio files from the Azure AI Speech SDK repository at GitHub.
language string No Null The language of the input audio such as fr. Supplying the input language in ISO-639-1 format improves accuracy and latency.

For the list of supported languages, see the OpenAI documentation.
prompt string No Null An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.

For more information about prompts including example use cases, see the OpenAI documentation.
response_format string No json The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.

The default value is json.
temperature number No 0 The sampling temperature, between 0 and 1.

Higher values like 0.8 makes the output more random, while lower values like 0.2 make it more focused and deterministic. If set to 0, the model uses log probability to automatically increase the temperature until certain thresholds are hit.

The default value is 0.

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/audio/transcriptions?api-version=2023-09-01-preview \
  -H "Content-Type: multipart/form-data" \
  -H "api-key: $YOUR_API_KEY" \
  -F file="@./YOUR_AUDIO_FILE_NAME.wav" \
  -F "language=en" \
  -F "prompt=The transcript contains zoology terms and geographical locations." \
  -F "temperature=0" \
  -F "response_format=srt"

Example response

1
00:00:00,960 --> 00:00:07,680
The ocelot, Lepardus paradalis, is a small wild cat native to the southwestern United States,

2
00:00:07,680 --> 00:00:13,520
Mexico, and Central and South America. This medium-sized cat is characterized by

3
00:00:13,520 --> 00:00:18,960
solid black spots and streaks on its coat, round ears, and white neck and undersides.

4
00:00:19,760 --> 00:00:27,840
It weighs between 8 and 15.5 kilograms, 18 and 34 pounds, and reaches 40 to 50 centimeters

5
00:00:27,840 --> 00:00:34,560
16 to 20 inches at the shoulders. It was first described by Carl Linnaeus in 1758.

6
00:00:35,360 --> 00:00:42,880
Two subspecies are recognized, L. p. paradalis and L. p. mitis. Typically active during twilight

7
00:00:42,880 --> 00:00:48,480
and at night, the ocelot tends to be solitary and territorial. It is efficient at climbing,

8
00:00:48,480 --> 00:00:54,480
leaping, and swimming. It preys on small terrestrial mammals such as armadillo, opossum,

9
00:00:54,480 --> 00:00:56,480
and lagomorphs.

Request a speech to text translation

Translates an audio file from another language into English. For the list of supported languages, see the OpenAI documentation.

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/audio/translations?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI resource.
deployment-id string Required The name of your Whisper model deployment such as MyWhisperDeployment. You're required to first deploy a Whisper model before you can make calls.
api-version string Required The API version to use for this operation. This value follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
file file Yes N/A The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.

The file size limit for the Azure OpenAI Whisper model is 25 MB. If you need to transcribe a file larger than 25 MB, break it into chunks.

You can download sample audio files from the Azure AI Speech SDK repository at GitHub.
prompt string No Null An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.

For more information about prompts including example use cases, see the OpenAI documentation.
response_format string No json The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.

The default value is json.
temperature number No 0 The sampling temperature, between 0 and 1.

Higher values like 0.8 makes the output more random, while lower values like 0.2 make it more focused and deterministic. If set to 0, the model uses log probability to automatically increase the temperature until certain thresholds are hit.

The default value is 0.

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/audio/translations?api-version=2023-09-01-preview \
  -H "Content-Type: multipart/form-data" \
  -H "api-key: $YOUR_API_KEY" \
  -F file="@./YOUR_AUDIO_FILE_NAME.wav" \
  -F "temperature=0" \
  -F "response_format=json"

Example response

{
  "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}

Text to speech

Synthesize text to speech.

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/audio/speech?api-version={api-version}

Path parameters

Parameter Type Required? Description
your-resource-name string Required The name of your Azure OpenAI resource.
deployment-id string Required The name of your text to speech model deployment such as MyTextToSpeechDeployment. You're required to first deploy a text to speech model (such as tts-1 or tts-1-hd) before you can make calls.
api-version string Required The API version to use for this operation. This value follows the YYYY-MM-DD format.

Supported versions

Request body

Parameter Type Required? Default Description
model string Yes N/A One of the available TTS models: tts-1 or tts-1-hd
input string Yes N/A The text to generate audio for. The maximum length is 4096 characters. Specify input text in the language of your choice.1
voice string Yes N/A The voice to use when generating the audio. Supported voices are alloy, echo, fable, onyx, nova, and shimmer. Previews of the voices are available in the OpenAI text to speech guide.

1 The text to speech models generally support the same languages as the Whisper model. For the list of supported languages, see the OpenAI documentation.

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/audio/speech?api-version=2024-02-15-preview \
 -H "api-key: $YOUR_API_KEY" \
 -H "Content-Type: application/json" \
 -d '{
    "model": "tts-hd",
    "input": "I'm excited to try text to speech.",
    "voice": "alloy"
}' --output speech.mp3

Example response

The speech is returned as an audio file from the previous request.

Management APIs

Azure OpenAI is deployed as a part of the Azure AI services. All Azure AI services rely on the same set of management APIs for creation, update, and delete operations. The management APIs are also used for deploying models within an Azure OpenAI resource.

Management APIs reference documentation

Next steps

Learn about Models, and fine-tuning with the REST API. Learn more about the underlying models that power Azure OpenAI.