Chat with an OpenAI model

OpenAI provides a number of chat-based models, mostly under the ChatGPT brand. Note that a ChatGPT Plus membership does not grant access to the API. You will need to sign up for a developer account (and pay for it) at the developer platform.

Usage

chat_openai(
  system_prompt = NULL,
  base_url = "https://api.openai.com/v1",
  api_key = openai_key(),
  model = NULL,
  params = NULL,
  seed = lifecycle::deprecated(),
  api_args = list(),
  api_headers = character(),
  echo = c("none", "output", "all")
)

models_openai(base_url = "https://api.openai.com/v1", api_key = openai_key())

Arguments

system_prompt

A system prompt to set the behavior of the assistant.

base_url

The base URL to the endpoint; the default uses OpenAI.

api_key

API key to use for authentication.

You generally should not supply this directly, but instead set the OPENAI_API_KEY environment variable. The best place to set this is in .Renviron, which you can easily edit by calling usethis::edit_r_environ().

model

The model to use for the chat (defaults to "gpt-4.1"). We regularly update the default, so we strongly recommend explicitly specifying a model for anything other than casual use. Use models_openai() to see all options.

params

Common model parameters, usually created by params().

seed

Optional integer seed that ChatGPT uses to try and make output more reproducible.

api_args

Named list of arbitrary extra arguments appended to the body of every chat API call. Combined with the body object generated by ellmer with modifyList().

api_headers

Named character vector of arbitrary extra headers appended to every chat API call.

echo

One of the following options:

• none: don't emit any output (default when running in a function).

• output: echo text and tool-calling output as it streams in (default when running at the console).

• all: echo all input and output.

Note this only affects the chat() method.

Value

A Chat object.

See also

Other chatbots: chat_anthropic(), chat_aws_bedrock(), chat_azure_openai(), chat_cloudflare(), chat_databricks(), chat_deepseek(), chat_github(), chat_google_gemini(), chat_groq(), chat_huggingface(), chat_mistral(), chat_ollama(), chat_openrouter(), chat_perplexity(), chat_portkey()

Examples

chat <- chat_openai()
#> Using model = "gpt-4.1".
chat$chat("
  What is the difference between a tibble and a data frame?
  Answer with a bulleted list
")
#> - **Printing behavior**:  
#>   - Tibbles print a preview (first 10 rows and columns that fit on 
#> screen), whereas data frames print ALL data unless told otherwise.
#> 
#> - **Data type preservation:**  
#>   - Tibbles never convert strings to factors by default, whereas 
#> data.frames (in base R <4.0) automatically convert strings to factors 
#> unless instructed not to.
#> 
#> - **Subsetting output:**  
#>   - Tibbles always return another tibble when subsetting with `[ ]`, 
#> while data frames may simplify the output (e.g., as a vector) 
#> depending on usage.
#> 
#> - **Column name handling:**  
#>   - Tibbles allow non-syntactic column names (e.g., names with spaces 
#> or special characters) without backticks, whereas data frames require 
#> syntactic names or backticks.
#> 
#> - **Partial matching:**  
#>   - Tibbles do **not** support partial column name matching, while 
#> data frames do.
#> 
#> - **Enhanced printing:**  
#>   - Tibbles display data types next to column names when printed; data
#> frames do not.
#> 
#> - **Creation:**  
#>   - Tibbles are created with `tibble()` or `as_tibble()`, while data 
#> frames use `data.frame()`.
#> 
#> - **Row names:**  
#>   - Tibbles do not support row names; data frames do.
#> 
#> - **Package:**  
#>   - Tibbles are part of the tidyverse (`tibble` package); data frames 
#> are a base R structure.

chat$chat("Tell me three funny jokes about statisticians")
#> Sure! Here are three funny jokes about statisticians:
#> 
#> 1. **Why did the statistician bring a ladder to the bar?**  
#>    Because he heard the drinks were on the house!
#> 
#> 2. **How do you tell the difference between a statistician and an 
#> accountant?**  
#>    Ask them to calculate the average salary: the accountant will give 
#> you the mean, the statistician will ask “what do you want it to be?”
#> 
#> 3. **Why do statisticians love the jungle?**  
#>    Because of all the *sample* trees!