tidyllm (version 0.3.1)

chat: Chat with a Language Model

Description

The chat() function sends a message to a language model via a specified provider and returns the response. It routes the provided LLMMessage object to the appropriate provider-specific chat function, while allowing for the specification of common arguments applicable across different providers.

Usage

chat(
  .llm,
  .provider = getOption("tidyllm_chat_default"),
  .dry_run = NULL,
  .stream = NULL,
  .temperature = NULL,
  .timeout = NULL,
  .top_p = NULL,
  .max_tries = NULL,
  .model = NULL,
  .verbose = NULL,
  .json_schema = NULL,
  .seed = NULL,
  .stop = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL
)

Value

An updated LLMMessage object containing the response from the language model.

Arguments

.llm

An LLMMessage object containing the message or conversation history to send to the language model.

.provider

A function or function call specifying the language model provider and any additional parameters. This should be a call to a provider function like openai(), claude(), etc. You can also set a default provider function via the tidyllm_chat_default option.

.dry_run

Logical; if TRUE, simulates the request without sending it to the provider. Useful for testing.

.stream

Logical; if TRUE, streams the response from the provider in real-time.

.temperature

Numeric; controls the randomness of the model's output (0 = deterministic).

.timeout

Numeric; the maximum time (in seconds) to wait for a response.

.top_p

Numeric; nucleus sampling parameter, which limits the sampling to the top cumulative probability p.

.max_tries

Integer; the maximum number of retries for failed requests.

.model

Character; the model identifier to use (e.g., "gpt-4").

.verbose

Logical; if TRUE, prints additional information about the request and response.

.json_schema

List; A JSON schema object as R list to enforce the output structure

.seed

Integer; sets a random seed for reproducibility.

.stop

Character vector; specifies sequences where the model should stop generating further tokens.

.frequency_penalty

Numeric; adjusts the likelihood of repeating tokens (positive values decrease repetition).

.presence_penalty

Numeric; adjusts the likelihood of introducing new tokens (positive values encourage novelty).

Details

The chat() function provides a unified interface for interacting with different language model providers. Common arguments such as .temperature, .model, and .stream are supported by most providers and can be passed directly to chat(). If a provider does not support a particular argument, an error will be raised.

Advanced provider-specific configurations can be accessed via the provider functions.

Examples

if (FALSE) {
# Basic usage with OpenAI provider
llm_message("Hello World") |>
   chat(ollama(.ollama_server = "https://my-ollama-server.de"),.model="mixtral")
   
   chat(mistral,.model="mixtral")

# Use streaming with Claude provider
llm_message("Tell me a story") |>
   chat(claude(),.stream=TRUE)
}