rgpt3

Making requests from R to the GPT-3 API

Note: this is a "community-maintained” package (i.e., not the official one). For the official OpenAI libraries (python and node.js endpoints), go to https://beta.openai.com/docs/libraries/python-bindings

Getting started

You can follow these steps to get started with making requests and retrieving embeddings from the Open AI GPT-3 language model.

If you already have an Open AI API key, you can skip step 1.

1. Obtain your API key

Go to https://openai.com/api/, register for a free account and obtain your API key located at https://beta.openai.com/account/api-keys.

Note that Open AI may rotate your key from time to time (but you should receive an email on your registered account if they do so).

2. Set up the access_key.txt file

Your access workflow for this package retrieves your API key from a local file. That file is easiest called access_key.txt (but any other file name would do). Important is that you use a .txt file with just the API key in there (i.e. no string quotation marks).

The path to that file (e.g. /Users/me/directory1/access_key.txt) is needed for the gpt3_authenticate() function (see below).

When using a version control workflow, make sure access_key.txt is in the .gitignore file (i.e., so your access code is not visible on a GitHub repo). If you use the package without any version control, you do not need to set a .gitignore file.

3. Install the rgpt3 package

The easiest way to use the package (before its CRAN release) is:

devtools::install_github("ben-aaron188/rgpt3")
library(rgpt3)

4. Run the test workflow

Once the package is installed, you will typically run this work flow:

Authentication:

Get the path to your access key file and run the authentication with: gpt3_authenticate("PATHTO/access_key.txt")

Make the test request:

You can run the test function below, which sends a simple request (here: the instruction to "Write a story about R Studio:") to the API and returns the output in the format used in this package (i.e., list[[1]] --> prompt and output, list[[2]] = meta information).

gpt3_test_completion()

Core functions

rgpt3 currently is structured into the following functions:

• Making requests (i.e. prompting the model)
  • single requests: gpt3_single_completion()
  • make multiple prompt-based requests from a source data.frame or data.table: gpt3_completions()
• Obtain embeddings
  • obtain embeddings for a single text input: gpt3_single_embedding
  • obtain embeddings for multiple texts from a source data.frame or data.table: gpt3_embeddings()

The basic principle is that you can (and should) best use the more extensible gpt3_completions() and gpt3_embeddings() functions as these allow you to make use of R's vectorisation. These do work even if you have only one prompt or text as input (see below). The difference between the extensible functions and their "single" counterparts is the input format.

This R package gives you full control over the parameters that the API contains. You can find these in detail in the package documentation and help files (e.g., ?gpt3_completions) on the Open AI website for completion requests and embeddings.

Note: this package enables you to use the core functionality of GPT-3 (making completion requests) and provides a function to obtain embeddings. There are additional functionalities in the core API such as fine-tuning models (i.e., providing labelled data to update/retrain the existing model) and asking GPT-3 to make edits to text input. These are not part of this package since the focus of making GPT-3 accessible from R is on the completion requests.

Examples

The examples below illustrate all functions of the package.

Note that due to the sampling temperature parameter of the requests - unless set to 0.0 - the results may vary (as the model is not deterministic then).

Making requests

The basic form of the GPT-3 API connector is via requests. These requests can be of various kinds including questions ("What is the meaning of life?"), text summarisation tasks, text generation tasks and many more. A whole list of examples is on the Open AI examples page.

Think of requests as instructions you give the to model. You may also hear the instructions being referred to as prompts and the task that GPT-3 then fulfills as completions (have a look at the API guide on prompt design).

Example 1: making a single completion request

This request "tells" GPT-3 to write a cynical text about human nature (five times) with a sampling temperature of 0.9, a maximium length of 100 tokens.

example_1 = gpt3_single_completion(prompt_input = 'Write a cynical text about human nature:'
                    , temperature = 0.9
                    , max_tokens = 100
                    , n = 5)

The returned list contains the actual instruction + output in example_1[[1]] and meta information about your request in example_1[[2]].

Example 2: multiple prompts

We can extend the example and make multiple requests by using a data.frame / data.table as input for the gpt3_completions() function:

my_prompts = data.frame('prompts' = 
                          c('Complete this sentence: universities are'
                            , 'Write a poem about music:'
                            , 'Which colour is better and why? Red or blue?')
                        ,'prompt_id' = c(LETTERS[1:3]))

example_2 = gpt3_completions(prompt_var = my_prompts$prompts
              , id_var = my_prompts$prompt_id
              , param_model = 'text-curie-001'
              , param_max_tokens = 100
              , param_n = 5
              , param_temperature = 0.4)

Note that this completion request produced 5 (param_n = 5) completions for each of the three prompts, so a total of 15 completions.

Embeddings

Here we load the data that comes with the package:

data("travel_blog_data")

The dataset is now available in your environment as travel_blog_data. The column (travel_blog_data$gpt3) contains ten texts written by GPT-3 (instructed to: "Write a travel blog about a dog's journey through the UK:").

Example 1: get embeddings for a single text

We can ask the model to retrieve embeddings for text similarity for a single text as follows:

# we just take the first text here as a single text example
my_text = travel_blog_data$gpt3[1]
my_embeddings = gpt3_single_embedding(input = my_text)
length(my_embeddings)
# 1024 (since the default model uses the 1024-dimensional Ada embeddings model)

Example 2: get embeddings from text data in a data.frame / data.table

Now we can do the same example with the full travel_blog_data dataset:

multiple_embeddings = gpt3_embeddings(input_var = travel_blog_data$gpt3
                                      , id_var = travel_blog_data$n)
dim(multiple_embeddings)

# [1]    10 1025 # because we have ten rows of 1025 columns each (by default 1024 embeddings elements and 1 id variable)

Cautionary note

Read this: using GPT-3 is not free, so any interaction with the GPT-3 model(s) is counted towards your token quota. You can find details about Open AI's pricing model at https://openai.com/api/pricing/.

You receive a reasonable credit for free and do not need to provide any payment information for the first interactions with the model(s). Once your token quota nears its end, Open AI will let you know. Your usage is traceable in your Open AI account dashboard.

Support and contributions

If you have questions or problems, please raise an issue on GitHub.

You are free to make contributions to the package via pull requests. If you do so, you agree that your contributions will be licensed under the GNU General Public License v3.0.

Changelog/updates

• [update] 29 Nov 2022: the just released davinci-003 model for text completions is now the default model for the text completion functions.

Citation

@software{Kleinberg_rgpt3_Making_requests_2022,
    author = {Kleinberg, Bennett},
    doi = {10.5281/zenodo.7327667},
    month = {11},
    title = {{rgpt3: Making requests from R to the GPT-3 API}},
    url = {https://github.com/ben-aaron188/rgpt3},
    version = {0.3},
    year = {2022}
}

See also "Cite this repository" on the right-hand side.