azure_openai_chat: 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
)

Value

A new LLMMessage object containing the original messages plus the assistant's response.

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".