Create a new LLM provider from an ellmer::chat() object

This function creates a llm_provider from an ellmer::chat() object. This allows the user to use the various LLM providers which are supported by the 'ellmer' R package, including respective configuration and features.

Please note that this function is experimental. This provider type may show different behavior than other LLM providers, and may not function optimally.

Usage

llm_provider_ellmer(
  chat,
  parameters = list(stream = getOption("tidyprompt.stream", TRUE)),
  verbose = getOption("tidyprompt.verbose", TRUE)
)

Arguments

chat

An ellmer::chat() object (e.g., ellmer::chat_openai())

parameters

A named list of parameters. See 'details' for supported parameters

verbose

A logical indicating whether the interaction with the llm_provider should be printed to the console. Default is TRUE

Value

An llm_provider with api_type = "ellmer"

Details

Unlike other LLM provider classes, most LLM provider settings need to be managed in the ellmer::chat() object (and not in the $parameters list). $get_chat() and $set_chat() may be used to manipulate the chat object. There are however some parameters that can be set in the $parameters list; these are documented below.

1. Streaming can be controlled through via the $parameters$stream parameter. If set to TRUE (default), streaming will be used if supported by the underlying ellmer::chat() object. If the underlying ellmer::chat() object does not support streaming, you may need to set this parameter to FALSE to avoid errors.

2. A special parameter $.ellmer_structured_type may also be set in the $parameters list; this parameter is used to specify a structured output format. This should be a 'ellmer' structured type (e.g., ellmer::type_object; see https://ellmer.tidyverse.org/articles/structured-data.html). answer_as_json() sets this parameter to obtain structured output (it is not recommended to set this parameter manually, but it is possible).

See also

Other llm_provider: llm_provider-class, llm_provider_google_gemini(), llm_provider_groq(), llm_provider_mistral(), llm_provider_ollama(), llm_provider_openai(), llm_provider_openrouter(), llm_provider_xai()

Examples

# Various providers:
ollama <- llm_provider_ollama()
openai <- llm_provider_openai()
openrouter <- llm_provider_openrouter()
mistral <- llm_provider_mistral()
groq <- llm_provider_groq()
xai <- llm_provider_xai()
gemini <- llm_provider_google_gemini()

# From an `ellmer::chat()` (e.g., `ellmer::chat_openai()`, ...):
if (FALSE) { # \dontrun{
ellmer <- llm_provider_ellmer(ellmer::chat_openai())
} # }

# Initialize with settings:
ollama <- llm_provider_ollama(
  parameters = list(
    model = "llama3.2:3b",
    stream = TRUE
  ),
  verbose = TRUE,
  url = "http://localhost:11434/api/chat"
)

# Change settings:
ollama$verbose <- FALSE
ollama$parameters$stream <- FALSE
ollama$parameters$model <- "llama3.1:8b"

if (FALSE) { # \dontrun{
# Try a simple chat message with '$complete_chat()':
response <- ollama$complete_chat("Hi!")
response
# $role
# [1] "assistant"
#
# $content
# [1] "How's it going? Is there something I can help you with or would you like
# to chat?"
#
# $http
# Response [http://localhost:11434/api/chat]
# Date: 2024-11-18 14:21
# Status: 200
# Content-Type: application/json; charset=utf-8
# Size: 375 B

# Use with send_prompt():
"Hi" |>
  send_prompt(ollama)
# [1] "How's your day going so far? Is there something I can help you with or
# would you like to chat?"
} # }