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.
For authentication, we recommend saving your
API key to
the OPENAI_API_KEY
environment variable in your .Renviron
file.
You can easily edit this file by calling usethis::edit_r_environ()
.
Arguments
- system_prompt
A system prompt to set the behavior of the assistant.
- turns
A list of Turns to start the chat with (i.e., continuing a previous conversation). If not provided, the conversation begins from scratch.
- base_url
The base URL to the endpoint; the default uses OpenAI.
- api_key
The API key to use for authentication. You generally should not supply this directly, but instead set the
OPENAI_API_KEY
environment variable.- model
The model to use for the chat. The default,
NULL
, will pick a reasonable default, and tell you about. We strongly recommend explicitly choosing a model for all but the most casual use.- 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.
- echo
One of the following options:
none
: don't emit any output (default when running in a function).text
: echo text 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_bedrock()
,
chat_claude()
,
chat_cortex_analyst()
,
chat_databricks()
,
chat_deepseek()
,
chat_gemini()
,
chat_github()
,
chat_groq()
,
chat_ollama()
,
chat_openrouter()
,
chat_perplexity()
Examples
chat <- chat_openai()
#> Using model = "gpt-4o".
chat$chat("
What is the difference between a tibble and a data frame?
Answer with a bulleted list
")
#> - **Printing Behavior**:
#> - Tibbles: Have an enhanced printing behavior. They show only the
#> first 10 rows and all columns that fit on the screen, making them more
#> user-friendly when working with large datasets.
#> - Data Frames: Display all rows and columns by default, which can be
#> overwhelming for large datasets.
#>
#> - **Column Data Types**:
#> - Tibbles: Can preserve list-columns and support non-standard column
#> names without issue.
#> - Data Frames: May not support list-columns as smoothly, and
#> non-standard names (e.g., names with spaces) can be problematic
#> without additional handling.
#>
#> - **Performance**:
#> - Tibbles: Introduced as part of the `tibble` package, they are
#> optimized for performance within the tidyverse suite of packages.
#> - Data Frames: Base R data structure for tables, may not have the
#> same performance optimizations for certain tidyverse operations.
#>
#> - **Subsetting Behavior**:
#> - Tibbles: Always return another tibble, making them predictable
#> when extracting subsets of data.
#> - Data Frames: May sometimes return simplified data structures
#> (e.g., vectors) depending on how data is subsetted.
#>
#> - **Conversion and Compatibility**:
#> - Tibbles: Are built on top of data frames and are fully compatible
#> with base R functions that operate on data frames, allowing smooth
#> transition and integration.
#> - Data Frames: Are the standard R data structure and are fully
#> compatible with base R and classical methods.
#>
#> - **Default Behavior**:
#> - Tibbles: Have stricter rules on data conversion and will not
#> automatically change data types, preventing some common data errors.
#> - Data Frames: Tend to convert strings to factors by default,
#> although this behavior can be controlled with settings.
#>
#> Overall, while both structures serve similar purposes, tibbles are
#> typically preferred in the tidyverse context due to their enhanced
#> features and better user experience.
chat$chat("Tell me three funny jokes about statistcians")
#> Certainly! Here are three light-hearted jokes about statisticians:
#>
#> 1. **Normal Walks into a Bar**:
#> A statistician walks into a bar and orders a drink. The bartender
#> asks, "What's your poison?" The statistician replies, "I'm here to
#> keep things normal!"
#>
#> 2. **Gaussian Band**:
#> Why don’t statisticians ever play hide and seek?
#> Because good luck hiding from someone who always assumes you’re
#> around the mean!
#>
#> 3. **Mid-Life Crisis**:
#> How does a statistician deal with a mid-life crisis?
#> They recalibrate their life expectations, center them around the
#> median, and eliminate a few outliers!
#>
#> I hope these bring a smile to your face!