Package: tidyprompt 0.0.1
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:
tidyprompt_0.0.1.tar.gz
tidyprompt_0.0.1.zip(r-4.5)tidyprompt_0.0.1.zip(r-4.4)tidyprompt_0.0.1.zip(r-4.3)
tidyprompt_0.0.1.tgz(r-4.4-any)tidyprompt_0.0.1.tgz(r-4.3-any)
tidyprompt_0.0.1.tar.gz(r-4.5-noble)tidyprompt_0.0.1.tar.gz(r-4.4-noble)
tidyprompt_0.0.1.tgz(r-4.4-emscripten)tidyprompt_0.0.1.tgz(r-4.3-emscripten)
tidyprompt.pdf |tidyprompt.html✨
tidyprompt/json (API)
NEWS
# Install 'tidyprompt' in R: |
install.packages('tidyprompt', repos = c('https://tjarkvandemerwe.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tjarkvandemerwe/tidyprompt/issues
Pkgdown site:https://tjarkvandemerwe.github.io
Last updated 2 days agofrom:a377826736. Checks:7 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Jan 09 2025 |
R-4.5-win | OK | Jan 09 2025 |
R-4.5-linux | OK | Jan 09 2025 |
R-4.4-win | OK | Jan 09 2025 |
R-4.4-mac | OK | Jan 09 2025 |
R-4.3-win | OK | Jan 09 2025 |
R-4.3-mac | OK | Jan 09 2025 |
Exports:add_msg_to_chat_historyadd_textanswer_as_booleananswer_as_integeranswer_as_jsonanswer_as_key_valueanswer_as_listanswer_as_named_listanswer_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_feedbackllm_provider_google_geminillm_provider_groqllm_provider_mistralllm_provider_ollamallm_provider_openaillm_provider_openrouterllm_provider_xaillm_provider-classllm_verifypersistent_chat-classprompt_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:askpassclicurldplyrfansigenericsgluehttr2jsonlitelifecyclemagrittropensslpillarpkgconfigR6rappdirsrlangstringistringrsystibbletidyselectutf8vctrswithr
Creating prompt wraps
Rendered fromcreating_prompt_wraps.Rmd
usingknitr::rmarkdown
on Jan 09 2025.Last update: 2024-12-07
Started: 2024-12-07
Getting started
Rendered fromgetting_started.Rmd
usingknitr::rmarkdown
on Jan 09 2025.Last update: 2024-12-21
Started: 2024-11-22
Sentiment analysis in R with a LLM and 'tidyprompt'
Rendered fromsentiment_analysis.Rmd
usingknitr::rmarkdown
on Jan 09 2025.Last update: 2024-11-30
Started: 2024-11-30