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This article provides details on the inference REST API endpoints for Azure OpenAI.
Azure OpenAI provides two methods for authentication. you can use either API Keys or Azure Active Directory.
Azure Active Directory authentication
: You can authenticate an API call using an Azure Active Directory 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 Azure Active Directory
.
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
Required?
Description
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
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
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
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
Optional
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.
string
Optional
A unique identifier representing your end-user, which can help monitoring and detecting abuse
integer
Optional
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
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 may be up to logprobs+1 elements in the response. This parameter cannot be used with gpt-35-turbo.
suffix
string
Optional
The suffix that comes after a completion of inserted text.
boolean
Optional
False
Echo back the prompt in addition to the completion. This parameter cannot be used with gpt-35-turbo.
string or array
Optional
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
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
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
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.
We currently do not support batching of embeddings into a single API call. If you receive the error InvalidRequestError: Too many inputs. The max number of inputs is 1. We hope to increase the number of inputs per request soon., this typically occurs when an array of embeddings is attempted to be passed as a batch rather than a single string. The string can be up to 8191 tokens in length when using the text-embedding-ada-002 (Version 2) model.
Create an embedding
POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/embeddings?api-version={api-version}
Path parameters
Parameter
Required?
Description
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
2022-12-01 Swagger spec
2023-05-15 Swagger spec
Request body
Parameter
Required?
Default
Description
Input text to get embeddings for, encoded as a string. The number of input tokens varies depending on what model you are using.
Unless you're embedding code, we suggest replacing newlines (\n) in your input with a single space, as we have observed inferior results when newlines are present.
string
A unique identifier representing for 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
Required?
Description
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
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
Required?
Default
Description
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
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.
integer
Optional
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."
string or array
Optional
Up to 4 sequences where the API will stop generating further tokens.
max_tokens
integer
Optional
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
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
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
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.
string
Optional
A unique identifier representing your end-user, which can help Azure OpenAI to monitor and detect abuse.
function_call
Optional
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
A list of functions the model may generate JSON inputs for. This parameter requires API version 2023-07-01-preview
ChatMessage
A single, role-attributed message within a chat completion interaction.
Description
function_call
FunctionCall
The name and arguments of a function that should be called, as generated by the model.
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. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
ChatRole
The role associated with this message payload
ChatRole
A description of the intended purpose of a message within a chat completions interaction.
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.
string
The role that provides input for chat completions.
FunctionCall
The name and arguments of a function that should be called, as generated by the model. This requires API version 2023-07-01-preview
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 may fabricate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
string
The name of the function to call.
FunctionDefinition
The definition of a caller-specified function that chat completions may invoke in response to matching user input. This requires API version 2023-07-01-preview
Description
description
string
A description of what the function does. The model will use this description when selecting the function and interpreting its parameters.
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
Required?
Description
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
Example request
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" \
-H "chatgpt_url: YOUR_RESOURCE_URL" \
-H "chatgpt_key: YOUR_API_KEY" \
"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?"
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
Optional
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
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}]
string or array
Optional
Up to 2 sequences where the API will stop generating further tokens.
max_tokens
integer
Optional
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
Required?
Default
Description
Required
The data source to be used for the Azure OpenAI on your data feature. For Azure Cognitive search the value is AzureCognitiveSearch.
endpoint
string
Required
The data source endpoint.
string
Required
One of the Azure Cognitive Search admin keys for your service.
indexName
string
Required
The search index to be used.
fieldsMapping
dictionary
Optional
Index data column mapping.
inScope
boolean
Optional
If set, this value will limit responses specific to the grounding data content.
topNDocuments
number
Optional
Number of documents that need to be fetched for document augmentation.
queryType
string
Optional
simple
Indicates which query option will be used for Azure Cognitive Search.
semanticConfiguration
string
Optional
The semantic search configuration. Only available when queryType is set to semantic.
roleInformation
string
Optional
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. There’s a 100 token limit, which counts towards the overall token limit.
Image generation
Request a generated image
Generate a batch of images from a text caption. Image generation is currently only available with api-version=2023-06-01-preview.
POST https://{your-resource-name}.openai.azure.com/openai/images/generations:submit?api-version={api-version}
Path parameters
Parameter
Required?
Description
Required
A text description of the desired image(s). The maximum length is 1000 characters.
integer
Optional
The number of images to generate. Must be between 1 and 5.
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
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
Required?
Description
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
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
Required?
Description
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).
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.
