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 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=2023-05-15

Completions

With the Completions operation, the model will generate 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

• 2022-12-01 Swagger spec
• 2023-03-15-preview Swagger spec
• 2023-05-15 Swagger spec
• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec
• 2023-09-01-preview Swagger spec

Request body

Parameter	Type	Required?	Default	Description
prompt	string or array	Optional	<\|endoftext\|>	The prompt(s) to generate completions for, encoded as a string, or array of strings. Note that <\|endoftext\|> is the document separator that the model sees during training, so if a prompt isn't specified the model will generate 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 means the model will take 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 will vary 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 will be 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.
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=2023-05-15\
  -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

• 2022-12-01 Swagger spec
• 2023-03-15-preview Swagger spec
• 2023-05-15 Swagger spec
• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec

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 are 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

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/embeddings?api-version=2023-05-15 \
  -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 format.

Supported versions

• 2023-03-15-preview Swagger spec
• 2023-05-15 Swagger spec
• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec
• 2023-12-01-preview Swagger spec

Example request

curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/chat/completions?api-version=2023-05-15 \
  -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?"}]}'

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}]}

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.

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

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.\nWe 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 will vary 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 paremeter is tools_choice]Controls how the model responds to function calls. "none" means the model does not 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 may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may 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 will not 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

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. Note that the model does not 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 will use 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.

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

• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec

Example request

You can make requests using Azure AI Search and Azure Cosmos DB for MongoDB vCore.

Azure AI Search

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"
                    ]
                }
            }
        }
    ]
}
'

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 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 message "messages": [{"delta": {"content": "[DONE]"}, "index": 2, "end_turn": true}]
stop	string or array	Optional	null	Up to 2 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.
indexName	string	Required	null	The search index to be used.
fieldsMapping	dictionary	Optional for Azure AI Search. Required for Azure Cosmos DB for MongoDB vCore.	null	Index data column mapping. When using Azure Cosmos DB for MongoDB vCore, the value vectorFields is required, which indicates the fields that store vectors.
inScope	boolean	Optional	true	If set, this value will limit 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 will 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.

The following parameters are used for Azure AI Search only

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 will be used for Azure AI Search. Available types: simple, semantic, vector, vectorSimpleHybrid, vectorSemanticHybrid.

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.

Start an ingestion job

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 
            } 
        ] 
    } 
} 

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.
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 will be 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 is 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 is not empty.

List ingestion jobs

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

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

• 2023-12-01-preview

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. 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).

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)

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

• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec

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)

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

• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec

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)

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

• 2023-06-01-preview Swagger spec
• 2023-07-01-preview Swagger spec
• 2023-08-01-preview Swagger spec

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

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

• 2023-09-01-preview

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. 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

• 2023-09-01-preview

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?"
}

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 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.