Connect to Azure® OpenAI® Services Chat Completion API from MATLAB®
model = azureChat
model = azureChat(systemPrompt)
model = azureChat(___,Name=Value)
Connect to the Azure OpenAI Services Chat Completion API to generate text using large language models developed by OpenAI using Azure OpenAI Services.
To connect to the Azure OpenAI API, you need a valid API key and Azure endpoint. You also need to choose the OpenAI model deployment by specifying the deployment ID. You can specify the API key, endpoint, and deployment ID either by using name-value arguments or by specifying the corresponding environment variables. For more information, see Azure OpenAI Services API.
model = azureChat creates an azureChat object.
model = azureChat(systemPrompt) creates an azureChat object with the specified system prompt.
model = azureChat(___,Name=Value) specifies additional options using one or more name-value arguments.
character vector | string scalar
Specify the system prompt and set the SystemPrompt property. The system prompt is a natural-language description that provides the framework in which a large language model generates its responses. The system prompt can include instructions about tone, communications style, language, etc.
Example: "You are a helpful assistant who provides answers to user queries in iambic pentameter."
character vector | string scalar
Azure OpenAI API key to access OpenAI models using Azure.
To keep sensitive information out of code, instead of using the APIKey name-value argument, you can also set the environment variable AZURE_OPENAI_API_KEY.
For more information on connecting to the Azure OpenAI API, see Azure OpenAI Services API.
For more information on keeping sensitive information out of code, see Keep Sensitive Information Out of Code.
openAIFunction object | array of openAIFunction objects
Information about tools available for function calling, specified as openAIFunction objects.
For an example of OpenAI function calling using the OpenAI Chat Completion API, see Analyze Scientific Papers Using ChatGPT Function Calls.
function handle
Specify a custom streaming function to process the generated output as it is being generated, rather than having to wait for the end of the generation. For example, you can use this function to print the output as it is generated.
Example: @(token) fprint("%s",token)
Optionally specify these properties at construction using name-value arguments. Specify PropertyName1=PropertyValue1,...,PropertyNameN=PropertyValueN, where PropertyName is the property name and PropertyValue is the corresponding value.
character vector | string scalar
After construction, this property is read-only.
Network address used to communicate with Azure.
Instead of using the Endpoint name-value argument, you can also set the environment variable AZURE_OPENAI_ENDPOINT. For more information, see Azure OpenAI Services API.
For more information on how to obtain an Azure endpoint, see https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=rest-api#set-up.
character vector | string scalar
After construction, this property is read-only.
The deployment ID, also known as deployment name, specifies the Azure OpenAI deployment to use for generation.
Instead of using the DeploymentID name-value argument, you can also set the environment variable AZURE_OPENAI_DEPLOYMENT. For more information, see Azure OpenAI Services API.
Example: "my-gpt-35-turbo-deployment"
"2024-10-21" (default) | "2024-06-01" | "2024-02-01" | "2025-02-01-preview" | "2025-01-01-preview" | ...
After construction, this property is read-only.
Specify the Azure OpenAI API version. For more information, see https://learn.microsoft.com/en-us/azure/ai-services/openai/api-version-deprecation.
1 (default) | numeric scalar between 0 and 2
Temperature value for controlling the randomness of the output. Higher temperature increases the randomness of the output.
1 (default) | numeric scalar between 0 and 1
Top probability mass for controlling the diversity of the generated output. Higher top probability mass corresponds to higher diversity.
[] (default) | string array with between 0 and 4 elements
Sequences that stop generation of tokens.
Example: ["The end.","And that is all she wrote."]
0 (default) | numeric scalar between -2 and 2
Penalty value for using a token that has already been used at least once in the generated output. Higher values reduce the repetition of tokens. Negative values increase the repetition of tokens.
The presence penalty is independent of the number of incidents of a token, so long as it has been used at least once. To increase the penalty for every additional time a token is generated, use the FrequencyPenalty name-value argument.
0 (default) | numeric scalar between -2 and 2
Penalty value for repeatedly using the same token in the generated output. Higher values reduce the repetition of tokens. Negative values increase the repetition of tokens.
The frequency penalty increases with every instance of a token in the generated output. To use a constant penalty for a repeated token, independent of the number of instances that token is generated, use the PresencePenalty name-value argument.
10 (default) | nonnegative numeric scalar
After construction, this property is read-only.
If the server does not respond within the timeout, then the generate function throws an error during text generation.
"text" (default) | "json" | string scalar | structure array
After construction, this property is read-only.
Format of the generatedOutput output argument of the generate function. You can request unformatted output, JSON mode, or structured output.
If you set the response format to "text", then the generated output is an unformatted string.
If you set the response format to "json", then the generated output is a formatted string containing JSON encoded data.
To configure the format of the generated JSON file, describe the format using natural language and provide it to the model either in the system prompt or as a user message. The prompt or message describing the format must contain the word "json" or "JSON".
The JSON response format is not supported for these models:
"gpt-4""gpt-4-0613""o1-preview""o1-mini"
To ensure that the model follows the required format, use structured output. To do this, set ReponseFormat to:
- A string scalar containing a valid JSON Schema.
- A structure array containing an example that adheres to the required format, for example:
ResponseFormat=struct("Name","Rudolph","NoseColor",[255 0 0])
Structured output is only supported for models "gpt-4o-mini", "gpt-4o-mini-2024-07-18", "gpt-4o-2024-08-06" and later.
character vector | string scalar
This property is read-only.
The system prompt is a natural-language description that provides the framework in which a large language model generates its responses. The system prompt can include instructions about tone, communications style, language, etc.
Set the SystemPrompt property during construction using the systemPrompt input argument.
Example: "You are a helpful assistant who provides answers to user queries in iambic pentameter."
string array
This property is read-only.
Names of the custom functions specified in the Tools name-value argument.
generate — Generate output from large language models
First, specify the Azure OpenAI API key, endpoint, and deployment as environment variables and save them to a file called ".env", as described in Azure OpenAI Services API. Next, load the environment file using the loadenv function.
loadenv(".env")Connect to the Azure OpenAI Chat Completion API.
model = azureChat("You are a helpful assistant awaiting further instructions.");Generate text using the generate function.
generate(model,"Why is a raven like a writing desk?",MaxNumTokens=50)ans = "The question "Why is a raven like a writing desk?" is a famous nonsensical riddle from Lewis Carroll's book "Alice's Adventures in Wonderland." When Carroll originally wrote the riddle in 1865, it was meant to be puzz"
generate | openAIChat | ollamaChat
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