Package: tidyprompt 0.4.0.9000

Luka Koning

tidyprompt: Prompt Large Language Models and Enhance Their Functionality

Easily construct prompts and associated logic for interacting with large language models (LLMs). 'tidyprompt' introduces the concept of prompt wraps, which are building blocks that you can use to quickly turn a simple prompt into a complex one. Prompt wraps do not just modify the prompt text, but also add extraction and validation functions that will be applied to the response of the LLM. This ensures that the user gets the desired output. 'tidyprompt' can add various features to prompts and their evaluation by LLMs, such as structured output, automatic feedback, retries, reasoning modes, autonomous R function calling, and R code generation and evaluation. It is designed to be compatible with any LLM provider that offers chat completion.

Authors:Luka Koning [aut, cre, cph], Tjark Van de Merwe [aut, cph], Kennispunt Twente [fnd]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidyprompt/json (API)

# Install 'tidyprompt' in R:
install.packages('tidyprompt', repos = c('https://kennispunttwente.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kennispunttwente/tidyprompt/issues

Pkgdown/docs site:https://kennispunttwente.github.io

On CRAN:

Conda:

llm

7.17 score 31 stars 1 packages 20 scripts 511 downloads 55 exports 25 dependencies

Last updated from:9513451f93. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK189
source / vignettesOK216
linux-release-x86_64OK149
macos-release-arm64OK183
macos-oldrel-arm64OK172
windows-develOK131
windows-releaseOK130
windows-oldrelOK131
wasm-releaseOK115

Exports:add_imageadd_msg_to_chat_historyadd_textanswer_as_booleananswer_as_categoryanswer_as_dataframeanswer_as_integeranswer_as_jsonanswer_as_key_valueanswer_as_listanswer_as_multi_categoryanswer_as_named_listanswer_as_numericanswer_as_regex_matchanswer_as_textanswer_by_chain_of_thoughtanswer_by_reactanswer_using_ranswer_using_sqlanswer_using_toolschat_historyconstruct_prompt_textdf_to_stringextract_from_return_listget_chat_historyget_prompt_wrapsis_tidypromptllm_breakllm_break_softllm_feedbackllm_provider_ellmerllm_provider_google_geminillm_provider_groqllm_provider_mistralllm_provider_ollamallm_provider_openaillm_provider_openrouterllm_provider_xaillm_provider-classllm_verifypersistent_chat-classprompt_wrapprovider_prompt_wrapquit_ifr_json_schema_to_examplesend_promptset_chat_historyset_system_promptskim_with_labels_and_levelstidyprompttidyprompt-classtools_add_docstools_get_docsuser_verifyvector_list_to_string

Dependencies:askpassclicurldplyrgenericsgluehttr2jsonlitelifecyclemagrittropensslpillarpkgconfigR6rappdirsrlangS7stringistringrsystibbletidyselectutf8vctrswithr

Getting started
Setup an LLM provider | Basic prompting | Prompt wraps | answer_as: Retrieving output in a specific format | JSON output | answer_by: Adding a reasoning mode to the LLM | answer_using: Have the LLM work with tools and code | Tools (function-calling) | Code generation and evaluation | Creating custom prompt wraps | Provider-level prompt wraps
Rendered fromgetting_started.Rmdusingknitr::rmarkdown

Last update: 2026-04-16
Started: 2024-11-22

Streaming LLM responses to Shiny apps
Example
Rendered fromstreaming_shiny_ipc.Rmdusingknitr::rmarkdown

Last update: 2025-11-30
Started: 2025-11-14

Creating prompt wraps
Breaking out of the evaluation loop | Extraction versus validation functions | Prompt wrap types and order of application | Configuring a LLM provider with a prompt wrap | Configuring a prompt wrap based on the LLM provider or HTTP responses | Configuring a prompt wrap based on other prompt wraps | Handler functions
Rendered fromcreating_prompt_wraps.Rmdusingknitr::rmarkdown

Last update: 2025-02-06
Started: 2024-12-07

Sentiment analysis in R with a LLM and 'tidyprompt'
Rendered fromsentiment_analysis.Rmdusingknitr::rmarkdown

Last update: 2024-11-30
Started: 2024-11-30

Readme and manuals

Help Manual

Help pageTopics
Add an image to a tidyprompt (multimodal)add_image
Add a message to a chat historyadd_msg_to_chat_history
Add text to a tidypromptadd_text
Make LLM answer as a boolean (TRUE or FALSE)answer_as_boolean
Make LLM answer as a categoryanswer_as_category
Make LLM answer as a data frame via structured outputanswer_as_dataframe
Make LLM answer as an integer (between min and max)answer_as_integer
Make LLM answer as JSON (with optional schema; structured output)answer_as_json
Make LLM answer as a list of key-value pairsanswer_as_key_value
Make LLM answer as a list of itemsanswer_as_list
Build prompt for categorizing a text into multiple categoriesanswer_as_multi_category
Make LLM answer as a named listanswer_as_named_list
Make LLM answer as a number (between min and max)answer_as_numeric
Make LLM answer match a specific regexanswer_as_regex_match
Make LLM answer as a constrained text responseanswer_as_text
Set chain of thought mode for a promptanswer_by_chain_of_thought
Set ReAct mode for a promptanswer_by_react
Enable LLM to draft and execute R codeanswer_using_r
Enable LLM to draft and execute SQL queries on a databaseanswer_using_sql
Enable LLM to call R functions (and/or MCP server tools)answer_using_tools
Create or validate 'chat_history' objectchat_history
Construct prompt text from a tidyprompt objectconstruct_prompt_text
Convert a dataframe to a string representationdf_to_string
Function to extract a specific element from a listextract_from_return_list
Get the chat history of a tidyprompt objectget_chat_history
Get prompt wraps from a tidyprompt objectget_prompt_wraps
Check if object is a tidyprompt objectis_tidyprompt
Create an 'llm_break' objectllm_break
Create an 'llm_break_soft' objectllm_break_soft
Create an 'llm_feedback' objectllm_feedback
Create a new LLM provider from an 'ellmer::chat()' objectllm_provider_ellmer
Create a new Google Gemini LLM providerllm_provider_google_gemini
Create a new Groq LLM providerllm_provider_groq
Create a new Mistral LLM providerllm_provider_mistral
Create a new Ollama LLM providerllm_provider_ollama
Create a new OpenAI LLM providerllm_provider_openai
Create a new OpenRouter LLM providerllm_provider_openrouter
Create a new XAI (Grok) LLM providerllm_provider_xai
LlmProvider R6 Classllm_provider-class
Have LLM check the result of a prompt (LLM-in-the-loop)llm_verify
PersistentChat R6 classpersistent_chat-class
Wrap a prompt with functions for modification and handling the LLM responseprompt_wrap
Create a provider-level prompt wrapprovider_prompt_wrap
Make evaluation of a prompt stop if LLM gives a specific responsequit_if
Generate an example object from a JSON schemar_json_schema_to_example
Send a prompt to a LLM providersend_prompt
Set the chat history of a tidyprompt objectset_chat_history
Set system prompt of a tidyprompt objectset_system_prompt
Skim a dataframe and include labels and levelsskim_with_labels_and_levels
Create a tidyprompt objecttidyprompt
Tidyprompt R6 Classtidyprompt-class
Add tidyprompt function documentation to a functiontools_add_docs
Extract documentation from a functiontools_get_docs
Have user check the result of a prompt (human-in-the-loop)user_verify
Convert a named or unnamed list/vector to a string representationvector_list_to_string