# Wrap a prompt with functions for modification and handling the LLM response This function takes a single string or a tidyprompt object and adds a new prompt wrap to it. A prompt wrap is a set of functions that modify the prompt text, extract a value from the LLM response, and validate the extracted value. The functions are used to ensure that the prompt and LLM response are in the correct format and meet the specified criteria; they may also be used to provide the LLM with feedback or additional information, like the result of a tool call or some evaluated code. Advanced prompt wraps may also include functions that directly handle the response from a LLM API or configure API parameters. ```r prompt_wrap( prompt, modify_fn = NULL, extraction_fn = NULL, validation_fn = NULL, handler_fn = NULL, parameter_fn = NULL, type = c("unspecified", "mode", "tool", "break", "check"), name = NULL ) ``` ## Arguments - `prompt`: A string or a tidyprompt object - `modify_fn`: A function that takes the previous prompt text (as first argument) and returns the new prompt text - `extraction_fn`: A function that takes the LLM response (as first argument) and attempts to extract a value from it. Upon succesful extraction, the function should return the extracted value. If the extraction fails, the function should return a `llm_feedback()` message to initiate a retry. A `llm_break()` can be returned to break the extraction and validation loop, ending `send_prompt()` - `validation_fn`: A function that takes the (extracted) LLM response (as first argument) and attempts to validate it. Upon succesful validation, the function should return TRUE. If the validation fails, the function should return a `llm_feedback()` message to initiate a retry. A `llm_break()` can be returned to break the extraction and validation loop, ending `send_prompt()` - `handler_fn`: A function that takes a 'completion' object (a result of a request to a LLM, as returned by `$complete_chat()` of a llm_provider object) as first argument and the llm_provider object as second argument. The function should return a (modified or identical) completion object. This can be used for advanced side effects, like logging, or native tool calling, or keeping track of token usage. See llm_provider for more information; handler_fn is attached to the llm_provider object that is being used. When using an `llm_provider_ellmer()`, the up-to-date ellmer_chat is synced onto the provider before handlers run. This allows handlers to access, for instance, the current cost of the conversation, and, for instance, to stop the conversation if a certain budget is exceeded. For example usage, see source code of `answer_using_tools()` - `parameter_fn`: A function that takes the llm_provider object which is being used with `send_prompt()` and returns a named list of parameters to be set in the llm_provider object via its `$set_parameters()` method. This can be used to configure specific parameters of the llm_provider object when evaluating the prompt. For example, `answer_as_json()` may set different parameters for different APIs related to JSON output. This function is typically only used with advanced prompt wraps that require specific settings in the llm_provider object - `type`: The type of prompt wrap. Must be one of: * "unspecified": The default type, typically used for prompt wraps which request a specific format of the LLM response, like `answer_as_integer()` * "mode": For prompt wraps that change how the LLM should answer the prompt, like `answer_by_chain_of_thought()` or `answer_by_react()` * "tool": For prompt wraps that enable the LLM to use tools, like `answer_using_tools()` or `answer_using_r()` when 'output_as_tool' = TRUE * "break": For prompt wraps that may break the extraction and validation loop, like `quit_if()`. These are applied before type "unspecified" as they may instruct the LLM to not answer the prompt in the manner specified by those prompt wraps * "check": For prompt wraps that apply a last check to the final answer, after all other prompt wraps have been evaluated. These prompt wraps may only contain a validation function, and are applied after all other prompt wraps have been evaluated. These prompt wraps are even applied after an earlier prompt wrap has broken the extraction and validation loop with `llm_break()` Types are used to determine the order in which prompt wraps are applied. When constructing the prompt text, prompt wraps are applied to the base prompt in the following order: 'check', 'unspecified', 'break', 'mode', 'tool'. When evaluating the LLM response and applying extraction and validation functions, prompt wraps are applied in the reverse order: 'tool', 'mode', 'break', 'unspecified', 'check'. Order among the same type is preserved in the order they were added to the prompt. - `name`: An optional name for the prompt wrap. This can be used to identify the prompt wrap in the tidyprompt object ## Returns A tidyprompt object with the `prompt_wrap()` appended to it ## Details For advanced use, modify_fn, extraction_fn, and validation_fn may take the llm_provider object (as used with `send_prompt()`) as second argument, and the 'http_list' (a list of all HTTP requests and responses made during `send_prompt()`) as third argument. Use of these arguments is not required, but can be useful for more complex prompt wraps which require additional information about the LLM provider or requests made so far. The functions (including parameter_fn) also have access to the object `self` (not a function argument; it is attached to the environment of the function) which contains the tidyprompt object that the prompt wrap is a part of. This can be used to access other prompt wraps, or to access the prompt text or other information about the prompt. For instance, other prompt wraps can be accessed through `self$get_prompt_wraps()`. ## Examples ```r # A custom prompt_wrap may be created during piping prompt <- "Hi there!" |> prompt_wrap( modify_fn = function(base_prompt) { paste(base_prompt, "How are you?", sep = "\n\n") } ) prompt # (Shorter notation of the above:) prompt <- "Hi there!" |> prompt_wrap(\(x) paste(x, "How are you?", sep = "\n\n")) # It may often be preferred to make a function which takes a prompt and # returns a wrapped prompt: my_prompt_wrap <- function(prompt) { modify_fn <- function(base_prompt) { paste(base_prompt, "How are you?", sep = "\n\n") } prompt_wrap(prompt, modify_fn) } prompt <- "Hi there!" |> my_prompt_wrap() # For more advanced examples, take a look at the source code of the # pre-built prompt wraps in the tidyprompt package, like # answer_as_boolean, answer_as_integer, add_tools, answer_as_code, etc. # Below is the source code for the 'answer_as_integer' prompt wrap function: #' Make LLM answer as an integer (between min and max) #' #' @param prompt A single string or a [tidyprompt()] object #' @param min (optional) Minimum value for the integer #' @param max (optional) Maximum value for the integer #' @param add_instruction_to_prompt (optional) Add instruction for replying #' as an integer to the prompt text. Set to FALSE for debugging if extractions/validations #' are working as expected (without instruction the answer should fail the #' validation function, initiating a retry) #' #' @return A [tidyprompt()] with an added [prompt_wrap()] which #' will ensure that the LLM response is an integer. #' #' @export #' #' @example inst/examples/answer_as_integer.R #' #' @family pre_built_prompt_wraps #' @family answer_as_prompt_wraps answer_as_integer <- function( prompt, min = NULL, max = NULL, add_instruction_to_prompt = TRUE ) { instruction <- "You must answer with only an integer (use no other characters)." if (!is.null(min) && !is.null(max)) { instruction <- paste( instruction, glue::glue( "Enter an integer between {min} and {max}." ) ) } else if (!is.null(min)) { instruction <- paste( instruction, glue::glue( "Enter an integer greater than or equal to {min}." ) ) } else if (!is.null(max)) { instruction <- paste( instruction, glue::glue( "Enter an integer less than or equal to {max}." ) ) } modify_fn <- function(original_prompt_text) { if (!add_instruction_to_prompt) { return(original_prompt_text) } glue::glue("{original_prompt_text}\n\n{instruction}") } extraction_fn <- function(x) { extracted <- suppressWarnings(as.numeric(x)) if (is.na(extracted)) { return(llm_feedback(instruction)) } return(extracted) } validation_fn <- function(x) { if (x != floor(x)) { # Not a whole number return(llm_feedback(instruction)) } if (!is.null(min) && x < min) { return( llm_feedback( glue::glue( "The number should be greater than or equal to {min}." ) ) ) } if (!is.null(max) && x > max) { return( llm_feedback( glue::glue( "The number should be less than or equal to {max}." ) ) ) } return(TRUE) } prompt_wrap( prompt, modify_fn, extraction_fn, validation_fn, name = "answer_as_integer" ) } ``` ## See Also tidyprompt `send_prompt()` Other prompt_wrap: `llm_break()`, `llm_feedback()` Other pre_built_prompt_wraps: `add_image()`, `add_text()`, `answer_as_boolean()`, `answer_as_category()`, `answer_as_integer()`, `answer_as_json()`, `answer_as_list()`, `answer_as_multi_category()`, `answer_as_named_list()`, `answer_as_regex_match()`, `answer_as_text()`, `answer_by_chain_of_thought()`, `answer_by_react()`, `answer_using_r()`, `answer_using_sql()`, `answer_using_tools()`, `quit_if()`, `set_system_prompt()`

prompt_wrap function