| #' Makes a single completion request to the GPT-3 API |
| #' |
| #' @description |
| #' `gpt3_single_completion()` sends a single [completion request](https://beta.openai.com/docs/api-reference/completions) to the Open AI GPT-3 API. |
| #' @details For a general guide on the completion requests, see [https://beta.openai.com/docs/guides/completion](https://beta.openai.com/docs/guides/completion). This function provides you with an R wrapper to send requests with the full range of request parameters as detailed on [https://beta.openai.com/docs/api-reference/completions](https://beta.openai.com/docs/api-reference/completions) and reproduced below. |
| #' |
| #' For the `best_of` parameter: When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n. Note that this is handled by the wrapper automatically if(best_of <= n){ best_of = n}. |
| #' |
| #' Parameters not included/supported: |
| #' - `logit_bias`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias](https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias) |
| #' - `echo`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo](https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo) |
| #' - `stream`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream](https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream) |
| #' |
| #' @param prompt_input character that contains the prompt to the GPT-3 request |
| #' @param model a character vector that indicates the [model](https://beta.openai.com/docs/models/gpt-3) to use; one of "text-davinci-002" (default), "text-curie-001", "text-babbage-001" or "text-ada-001" |
| #' @param output_type character determining the output provided: "complete" (default), "text" or "meta" |
| #' @param suffix character (default: NULL) (from the official API documentation: _The suffix that comes after a completion of inserted text_) |
| #' @param max_tokens numeric (default: 100) indicating the maximum number of tokens that the completion request should return (from the official API documentation: _The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096)_) |
| #' @param temperature numeric (default: 0.9) specifying the sampling strategy of the possible completions (from the official API documentation: _What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both._) |
| #' @param top_p numeric (default: 1) specifying sampling strategy as an alternative to the temperature sampling (from the official API documentation: _An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both._) |
| #' @param n numeric (default: 1) specifying the number of completions per request (from the official API documentation: _How many completions to generate for each prompt. **Note: Because this parameter generates many completions, it can quickly consume your token quota.** Use carefully and ensure that you have reasonable settings for max_tokens and stop._) |
| #' @param logprobs numeric (default: NULL) (from the official API documentation: _Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response. The maximum value for logprobs is 5. If you need more than this, please contact support@openai.com and describe your use case._) |
| #' @param stop character or character vector (default: NULL) that specifies after which character value when the completion should end (from the official API documentation: _Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence._) |
| #' @param presence_penalty numeric (default: 0) between -2.00 and +2.00 to determine the penalisation of repetitiveness if a token already exists (from the official API documentation: _Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics._). See also: [https://beta.openai.com/docs/api-reference/parameter-details](https://beta.openai.com/docs/api-reference/parameter-details) |
| #' @param frequency_penalty numeric (default: 0) between -2.00 and +2.00 to determine the penalisation of repetitiveness based on the frequency of a token in the text already (from the official API documentation: _Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim._). See also: [https://beta.openai.com/docs/api-reference/parameter-details](https://beta.openai.com/docs/api-reference/parameter-details) |
| #' @param best_of numeric (default: 1) that determines the space of possibilities from which to select the completion with the highest probability (from the official API documentation: _Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token)_). See details. |
| #' |
| #' @return A list with two data tables (if `output_type` is the default "complete"): [[1]] contains the data table with the columns `n` (= the mo. of `n` responses requested), `prompt` (= the prompt that was sent), and `gpt3` (= the completion as returned from the GPT-3 model). [[2]] contains the meta information of the request, including the request id, the parameters of the request and the token usage of the prompt (`tok_usage_prompt`), the completion (`tok_usage_completion`) and the total usage (`tok_usage_total`). |
| #' |
| #' If `output_type` is "text", only the data table in slot [[1]] is returned. |
| #' |
| #' If `output_type` is "meta", only the data table in slot [[2]] is returned. |
| #' @examples |
| #' # First authenticate with your API key via `gpt3_authenticate('pathtokey')` |
| #' |
| #' # Once authenticated: |
| #' |
| #' ## Simple request with defaults: |
| #' gpt3_single_completion(prompt_input = 'How old are you?') |
| #' |
| #' ## Instruct GPT-3 to write ten research ideas of max. 150 tokens with some controls: |
| #'gpt3_single_completion(prompt_input = 'Write a research idea about using text data to understand human behaviour:' |
| #' , temperature = 0.8 |
| #' , n = 10 |
| #' , max_tokens = 150) |
| #' |
| #' ## For fully reproducible results, we need `temperature = 0`, e.g.: |
| #' gpt3_single_completion(prompt_input = 'Finish this sentence:/n There is no easier way to learn R than' |
| #' , temperature = 0.0 |
| #' , max_tokens = 50) |
| #' |
| #' ## The same example with a different GPT-3 model: |
| #' gpt3_single_completion(prompt_input = 'Finish this sentence:/n There is no easier way to learn R than' |
| #' , model = 'text-babbage-001' |
| #' , temperature = 0.0 |
| #' , max_tokens = 50) |
| #' @export |
| gpt3_single_completion = function(prompt_input |
| , model = 'text-davinci-002' |
| , output_type = 'complete' |
| , suffix = NULL |
| , max_tokens = 100 |
| , temperature = 0.9 |
| , top_p = 1 |
| , n = 1 |
| , logprobs = NULL |
| , stop = NULL |
| , presence_penalty = 0 |
| , frequency_penalty = 0 |
| , best_of = 1){ |
| |
| #check for request issues with `n` and `best_of` |
| if(best_of < n){ |
| best_of = n |
| message('To avoid an `invalid_request_error`, `best_of` was set to equal `n`') |
| } |
| |
| if(temperature == 0 & n > 1){ |
| n = 1 |
| message('You are running the deterministic model, so `n` was set to 1 to avoid unnecessary token quota usage.') |
| } |
| |
| parameter_list = list(prompt = prompt_input |
| , model = model |
| , suffix = suffix |
| , max_tokens = max_tokens |
| , temperature = temperature |
| , top_p = top_p |
| , n = n |
| , logprobs = logprobs |
| , stop = stop |
| , presence_penalty = presence_penalty |
| , frequency_penalty = frequency_penalty |
| , best_of = best_of) |
| |
| request_base = httr::POST(url = url.completions |
| , body = parameter_list |
| , httr::add_headers(Authorization = paste("Bearer", api_key)) |
| , encode = "json") |
| |
| request_content = httr::content(request_base) |
| |
| if(n == 1){ |
| core_output = data.table::data.table('n' = 1 |
| , 'prompt' = prompt_input |
| , 'gpt3' = request_content$choices[[1]]$text) |
| } else if(n > 1){ |
| |
| core_output = data.table::data.table('n' = 1:n |
| , 'prompt' = rep(prompt_input, n) |
| , 'gpt3' = rep("", n)) |
| |
| for(i in 1:n){ |
| core_output$gpt3[i] = request_content$choices[[i]]$text |
| } |
| |
| } |
| |
| |
| meta_output = data.table::data.table('request_id' = request_content$id |
| , 'object' = request_content$object |
| , 'model' = request_content$model |
| , 'param_prompt' = prompt_input |
| , 'param_model' = model |
| , 'param_suffix' = suffix |
| , 'param_max_tokens' = max_tokens |
| , 'param_temperature' = temperature |
| , 'param_top_p' = top_p |
| , 'param_n' = n |
| , 'param_logprobs' = logprobs |
| , 'param_stop' = stop |
| , 'param_presence_penalty' = presence_penalty |
| , 'param_frequency_penalty' = frequency_penalty |
| , 'param_best_of' = best_of |
| , 'tok_usage_prompt' = request_content$usage$prompt_tokens |
| , 'tok_usage_completion' = request_content$usage$completion_tokens |
| , 'tok_usage_total' = request_content$usage$total_tokens) |
| |
| if(output_type == 'complete'){ |
| output = list(core_output |
| , meta_output) |
| } else if(output_type == 'meta'){ |
| output = meta_output |
| } else if(output_type == 'text'){ |
| output = core_output |
| } |
| |
| return(output) |
| |
| } |