azure_openai_chat: Send LLM Messages to an Azure OpenAI Chat Completions...
In tidyllm: Tidy Integration of Large Language Models

azure_openai_chat

R Documentation

Send LLM Messages to an Azure OpenAI Chat Completions endpoint

Description

This function sends a message history to the Azure OpenAI Chat Completions API and returns the assistant's reply.

Usage

azure_openai_chat(
  .llm,
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .deployment = "gpt-4o-mini",
  .api_version = "2024-08-01-preview",
  .max_completion_tokens = NULL,
  .reasoning_effort = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = NULL,
  .top_p = NULL,
  .timeout = 60,
  .verbose = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .dry_run = FALSE,
  .logprobs = NULL,
  .top_logprobs = NULL,
  .tools = NULL,
  .tool_choice = NULL
)

Arguments

`.llm`	An `LLMMessage` object containing the conversation history.
`.endpoint_url`	Base URL for the API (default: Sys.getenv("AZURE_ENDPOINT_URL")).
`.deployment`	The identifier of the model that is deployed (default: "gpt-4o-mini").
`.api_version`	Which version of the API is deployed (default: "2024-08-01-preview").
`.max_completion_tokens`	An upper bound for the number of tokens that can be generated for a completion.
`.reasoning_effort`	How long should reasoning models reason (can either be "low","medium" or "high").
`.frequency_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency.
`.logit_bias`	A named list modifying the likelihood of specified tokens appearing in the completion.
`.presence_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.
`.seed`	If specified, the system will make a best effort to sample deterministically.
`.stop`	Up to 4 sequences where the API will stop generating further tokens.
`.stream`	If set to TRUE, the answer will be streamed to console as it comes (default: FALSE).
`.temperature`	What sampling temperature to use, between 0 and 2. Higher values make the output more random.
`.top_p`	An alternative to sampling with temperature, called nucleus sampling.
`.timeout`	Request timeout in seconds (default: 60).
`.verbose`	Should additional information be shown after the API call (default: FALSE).
`.json_schema`	A JSON schema object provided by tidyllm schema or ellmer schemata.
`.max_tries`	Maximum retries to perform request.
`.dry_run`	If TRUE, perform a dry run and return the request object (default: FALSE).
`.logprobs`	If TRUE, get the log probabilities of each output token (default: NULL).
`.top_logprobs`	If specified, get the top N log probabilities of each output token (0-5, default: NULL).
`.tools`	Either a single TOOL object or a list of TOOL objects representing the available functions for tool calls.
`.tool_choice`	A character string specifying the tool-calling behavior; valid values are "none", "auto", or "required".