Package 'gpttools' reference manual

Title:	Extensions and Tools for gptstudio
Description:	gpttools is an R package that provides extensions to gptstudio to provide devtools-like functionality using the latest natural language processing (NLP) models. It is designed to make package development easier by providing a range of tools and functions that can be used to improve the quality of your package's documentation, testing, and maybe even functionality.
Authors:	James Wade [aut, cre]
Maintainer:	James Wade <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.8.9016
Built:	2024-11-19 04:26:59 UTC
Source:	https://github.com/JamesHWade/gpttools

Add Roxygen documentation to a function

Description

This function uses the OpenAI API to generate a roxygen skeleton for the current selection in RStudio. The roxygen skeleton is then inserted into the document using prompt: "insert roxygen skeleton to document this function"

Usage

add_roxygen_addin()
add_roxygen_addin()

Value

NULL (nothing is returned; the generated roxygen skeleton is inserted into the document).

Addin to scrape installed packages

Description

Invokes RStudio addin functionality to scrape select installed packages and create indices for use in the "Chat with Retrieval" application.

Usage

addin_run_scrape_pkgs()
addin_run_scrape_pkgs()

Value

No return value, called for side effects only.

Note

This addin requires RStudio to be available and will stop with an error message if RStudio API is not accessible.

Examples


# This function is typically run within RStudio as an Addin.
# It would not be called directly in the code.
addin_scrape_pkgs()

# This function is typically run within RStudio as an Addin.
# It would not be called directly in the code.
addin_scrape_pkgs()

Run Package Selector App

Description

Run the package selector shiny app

Usage

addin_run_select_pkgs()
addin_run_select_pkgs()

Value

This function has no return value.

Chat for gpttools

Description

This function provides a high-level interface for communicating with various services and models supported by gpttools. It orchestrates the creation, configuration, and execution of a request based on user inputs and options set for gpttools The function supports a range of tasks from text generation to code synthesis and can be customized according to skill level and coding style preferences.

Usage

chat(
  prompt,
  service = getOption("gpttools.service"),
  history = list(list(role = "system", content = "You are an R chat assistant")),
  stream = getOption("gpttools.stream", TRUE),
  model = getOption("gpttools.model"),
  skill = getOption("gpttools.skill", NULL),
  style = getOption("gpttools.code_style", "no preference"),
  task = NULL,
  custom_prompt = NULL,
  process_response = FALSE,
  where = "console",
  ...
)
chat(
  prompt,
  service = getOption("gpttools.service"),
  history = list(list(role = "system", content = "You are an R chat assistant")),
  stream = getOption("gpttools.stream", TRUE),
  model = getOption("gpttools.model"),
  skill = getOption("gpttools.skill", NULL),
  style = getOption("gpttools.code_style", "no preference"),
  task = NULL,
  custom_prompt = NULL,
  process_response = FALSE,
  where = "console",
  ...
)

Arguments

`prompt`	A string containing the initial prompt or question to be sent to the model. This is a required parameter.
`service`	The AI service to be used for the request. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.service")`. If the option is not set, make sure to provide this parameter to avoid errors.
`history`	An optional parameter that can be used to include previous interactions or context for the current session. Defaults to a system message indicating "You are an R chat assistant".
`stream`	A logical value indicating whether the interaction should be treated as a stream for continuous interactions. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.stream")`.
`model`	The specific model to use for the request. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.model")`.
`skill`	A character string indicating the skill or capability level of the user. This parameter allows for customizing the behavior of the model to the user. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.skill")`.
`style`	The coding style preferred by the user for code generation tasks. This parameter is particularly useful when the task involves generating code snippets or scripts. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.code_style")`.
`task`	The specific type of task to be performed, ranging from text generation to code synthesis, depending on the capabilities of the model. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.task")`.
`custom_prompt`	An optional parameter that provides a way to extend or customize the initial prompt with additional instructions or context.
`process_response`	A logical indicating whether to process the model's response. If `TRUE`, the response will be passed to `gptstudio_response_process()` for further processing. Defaults to `FALSE`. Refer to `gptstudio_response_process()` for more details.,
`where`	A character string indicating the location or environment where the chat is taking place. Options are `⁠c("console", "source", and "shiny")⁠`. The default is `""`, which means the chat is taking place in the R console.
`...`	Reserved for future use.

Value

Depending on the task and processing, the function returns the response from the model, which could be text, code, or any other structured output defined by the task and model capabilities. The precise format and content of the output depend on the specified options and the capabilities of the selected model.

Examples

## Not run: 
# Basic usage with a text prompt:
result <- chat("What is the weather like today?")

# Advanced usage with custom settings, assuming appropriate global options are set:
result <- chat(
  prompt = "Write a simple function in R",
  skill = "advanced",
  style = "tidyverse",
  task = "coding"
)

# Usage with explicit service and model specification:
result <- chat(
  prompt = "Explain the concept of tidy data in R",
  service = "openai",
  model = "gpt-4-turbo-preview",
  skill = "intermediate",
  task = "general"
)

## End(Not run)

## Not run: 
# Basic usage with a text prompt:
result <- chat("What is the weather like today?")

# Advanced usage with custom settings, assuming appropriate global options are set:
result <- chat(
  prompt = "Write a simple function in R",
  skill = "advanced",
  style = "tidyverse",
  task = "coding"
)

# Usage with explicit service and model specification:
result <- chat(
  prompt = "Explain the concept of tidy data in R",
  service = "openai",
  model = "gpt-4-turbo-preview",
  skill = "intermediate",
  task = "general"
)

## End(Not run)

chat_with_context

Description

This function allows you to chat with a chatbot that answers questions based on the provided context and chat history. It uses GPT-4 architecture to generate responses.

Usage

chat_with_context(
  query,
  service = "openai",
  model = "gpt-4-turbo-preview",
  index = NULL,
  add_context = TRUE,
  chat_history = NULL,
  history_name = "chat_history",
  session_history = NULL,
  add_history = TRUE,
  task = "Context Only",
  k_context = 4,
  k_history = 4,
  save_history = TRUE,
  overwrite = FALSE,
  local = FALSE,
  embedding_model = NULL,
  stream = FALSE,
  rv = NULL
)
chat_with_context(
  query,
  service = "openai",
  model = "gpt-4-turbo-preview",
  index = NULL,
  add_context = TRUE,
  chat_history = NULL,
  history_name = "chat_history",
  session_history = NULL,
  add_history = TRUE,
  task = "Context Only",
  k_context = 4,
  k_history = 4,
  save_history = TRUE,
  overwrite = FALSE,
  local = FALSE,
  embedding_model = NULL,
  stream = FALSE,
  rv = NULL
)

Arguments

`query`	The input query to be processed.
`service`	Name of the AI service to use, defaults to openai.
`model`	Name of the openai model to use, defaults to gpt-3.5-turbo
`index`	Index to look for context.
`add_context`	Whether to add context to the query. Options are `"always"`, `"sometimes"`, and `"never"`. The default is `"sometimes"`.
`chat_history`	Chat history dataframe for reference.
`history_name`	Name of the file where chat history is stored.
`session_history`	Session history data for reference.
`add_history`	Whether to add chat history to the query or not. Default is TRUE.
`task`	Task type, either "Context Only" or "Permissive Chat". Default is "Context Only".
`k_context`	Number of top context matches to consider. Default is 4.
`k_history`	Number of top chat history matches to consider. Default is 4.
`save_history`	Whether to save the chat history or not. Default is TRUE.
`overwrite`	Whether to overwrite the history file or not. Default is FALSE.
`local`	Whether to use the local model or not. Default is FALSE.
`embedding_model`	A model object to use for embedding. Only needed if local is TRUE. Default is NULL.
`stream`	Whether to stream the response or not. Default is FALSE.
`rv`	A reactive value to store the response. Default is NULL.

Value

A list containing the prompt, context, and answer.

Examples


rlang::is_interactive()
query <- "What is the capital of France?"
result <- chat_with_context(query = query, context = context)

rlang::is_interactive()
query <- "What is the capital of France?"
result <- chat_with_context(query = query, context = context)

Run Chat with Retrieval

Description

Run the Chat with Retrieval shiny app

Usage

chat_with_retrieval()
chat_with_retrieval()

Value

This function has no return value.

Examples

# Call the function as an RStudio addin
## Not run: 
chat_with_retrieval()

## End(Not run)
# Call the function as an RStudio addin
## Not run: 
chat_with_retrieval()

## End(Not run)

Collect Dataframes

Description

Collect all the dataframes in the global environment.

Usage

collect_dataframes()
collect_dataframes()

Value

A character vector of dataframe names.

Copilot-style Code Suggestions

Description

Copilot-style Code Suggestions

Usage

copilot_addin()
copilot_addin()

Scrape and process all hyperlinks within a given URL

Description

This function scrapes all hyperlinks within a given URL and processes the data into a tibble format. It saves the resulting tibble into a parquet file.

Usage

crawl(
  url,
  index_create = TRUE,
  aggressive = FALSE,
  overwrite = FALSE,
  update = FALSE,
  pkg_version = "not a package",
  pkg_name = NULL,
  service = "openai"
)
crawl(
  url,
  index_create = TRUE,
  aggressive = FALSE,
  overwrite = FALSE,
  update = FALSE,
  pkg_version = "not a package",
  pkg_name = NULL,
  service = "openai"
)

Arguments

`url`	A character string with the URL to be scraped.
`index_create`	A logical value indicating whether to create an index. Default is TRUE.
`aggressive`	A logical value indicating whether to use aggressive link crawling. Default is FALSE.
`overwrite`	A logical value indicating whether to overwrite scraped pages and index if they already exist. Default is FALSE.
`update`	A logical value indicating whether to update scraped pages, defaults to FALSE.
`pkg_version`	Package version number
`pkg_name`	Package name
`service`	The service to use for scraping. Default is "openai". Options are "openai" and "local".

Value

NULL. The resulting tibble is saved into a parquet file.

Create index from audio

Description

This function creates an index by first transcribing the audio file and then creating the index.

Usage

create_index_from_audio(file_path, source, link = NULL, overwrite = FALSE)
create_index_from_audio(file_path, source, link = NULL, overwrite = FALSE)

Arguments

`file_path`	Character string specifying the path to the audio file.
`source`	Character string specifying the source of the audio.
`link`	Character string specifying the link to the audio file (optional).
`overwrite`	Logical, whether to overwrite the existing index (default: FALSE).

Value

The function writes an index in Parquet format to disk.

Create index from PDF

Description

This function creates an index by first ingesting the PDF file and then creating the index.

Usage

create_index_from_pdf(file_path, source, link = NULL, overwrite = FALSE)
create_index_from_pdf(file_path, source, link = NULL, overwrite = FALSE)

Arguments

`file_path`	Character string specifying the path to the PDF file.
`source`	Character string specifying the source of the PDF.
`link`	Character string specifying the link to the PDF file (optional).
`overwrite`	Logical, whether to overwrite the existing index (default: FALSE).

Value

The function writes an index in Parquet format to disk.

Get Transcription from Audio

Description

This function transcribes audio files and returns the transcription text.

Usage

create_transcript(file_path, prompt = NULL, chunk_size = 120)
create_transcript(file_path, prompt = NULL, chunk_size = 120)

Arguments

`file_path`	Character string specifying the path to the audio file.
`prompt`	Character string specifying the prompt for transcription (optional).
`chunk_size`	Audio size in seconds.

Value

A character string containing the transcription text.

Delete chat history files

Description

This function interactively deletes chat history files stored in the user's directory. It lists all the .parquet files in the gpttools data directory and prompts the user for confirmation before deleting each file.

Usage

delete_history(local = FALSE)
delete_history(local = FALSE)

Arguments

local

Whether to delete history made with local or OpenAI embeddings.

Examples

## Not run: 
# Interactively delete chat history files
delete_history()

## End(Not run)
## Not run: 
# Interactively delete chat history files
delete_history()

## End(Not run)

Delete an Index File

Description

Interactively deletes a specified index file from a user-defined directory. Presents the user with a list of available index files and prompts for confirmation before deletion.

Usage

delete_index()
delete_index()

Document Data

Description

This function runs a Shiny application to view and document data.

Usage

document_data()
document_data()

Extract Code Chunks from Text

Description

This function extracts code chunks that are enclosed in triple backticks from a given text input. It supports identifying code blocks that are indicated by triple backticks followed by an optional language identifier. The function returns the extracted code chunks.

Usage

extract_code_chunks(text)
extract_code_chunks(text)

Arguments

text

A character vector containing the text from which code chunks need to be extracted.

Value

A list containing the extracted code chunks.

Examples

sample_text <- "Here is some text.
```
This is a code chunk.
```"
extract_code_chunks(sample_text)

sample_text <- "Here is some text.
```
This is a code chunk.
```"
extract_code_chunks(sample_text)

Wrapper around selectionGet to help with testthat

Description

Wrapper around selectionGet to help with testthat

Usage

get_selection()
get_selection()

Value

Text selection via rstudioapi::selectionGet

Get Transformer Model

Description

This function is designed to download and load a pre-trained transformer model using the transformers Python library via the reticulate package. It checks for the availability of the required Python package and then downloads the specified transformer model.

Usage

get_transformer_model(
  model_name = getOption("gpttools.local_embed_model", "BAAI/bge-small-en-v1.5")
)
get_transformer_model(
  model_name = getOption("gpttools.local_embed_model", "BAAI/bge-small-en-v1.5")
)

Arguments

model_name

The name of the transformer model to download. This should be in the format "username/modelname" as recognized by the transformers library. Default is "BAAI/bge-small-en-v1.5".

Value

An object of the downloaded transformer model.

Note

Users of this function need to ensure that the Python environment is set up with the 'transformers' package installed. The function uses the 'reticulate' R package to interface with Python and the user may need to configure it accordingly.

Examples

## Not run: 
# To get the default transformer model:
get_transformer_model()

# To get a custom transformer model by specifying the model name:
get_transformer_model("bert-base-uncased")

## End(Not run)

## Not run: 
# To get the default transformer model:
get_transformer_model()

# To get a custom transformer model by specifying the model name:
get_transformer_model("bert-base-uncased")

## End(Not run)

Ghost Chat

Description

Ghost Chat

Usage

ghost_chat(
  service = getOption("gpttools.service", "openai"),
  stream = TRUE,
  where = "source"
)
ghost_chat(
  service = getOption("gpttools.service", "openai"),
  stream = TRUE,
  where = "source"
)

Arguments

`service`	The AI service to be used for the request. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.service")`. If the option is not set, make sure to provide this parameter to avoid errors.
`stream`	A logical value indicating whether the interaction should be treated as a stream for continuous interactions. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.stream")`.
`where`	A character string indicating the location or environment where the chat is taking place. Options are `⁠c("console", "source", and "shiny")⁠`. The default is `""`, which means the chat is taking place in the R console.

Writing Assistant

Description

Writing Assistant

Usage

ghost_writer(
  service = getOption("gpttools.service", "openai"),
  stream = TRUE,
  where = "source"
)
ghost_writer(
  service = getOption("gpttools.service", "openai"),
  stream = TRUE,
  where = "source"
)

Arguments

`service`	The AI service to be used for the request. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.service")`. If the option is not set, make sure to provide this parameter to avoid errors.
`stream`	A logical value indicating whether the interaction should be treated as a stream for continuous interactions. If not explicitly provided, this defaults to the value set in `getOption("gptstudio.stream")`.
`where`	A character string indicating the location or environment where the chat is taking place. Options are `⁠c("console", "source", and "shiny")⁠`. The default is `""`, which means the chat is taking place in the R console.

GPT Sitrep

Description

Check the status of the OpenAI API and RStudio API, and inform the user of the settings for gpttools.

Usage

gpt_sitrep()
gpt_sitrep()

Value

A message is returned to the user informing them of the status of the OpenAI API, RStudio API, and gpttools settings.

Examples

## Not run: 
gpt_sitrep()

## End(Not run)
## Not run: 
gpt_sitrep()

## End(Not run)

Index All Scraped Data

Description

This function iterates through all the text files in a specified directory, updating or creating indexes for each domain contained in the file names. Allows customization of the indexing process through various parameters.

Usage

gpttools_index_all_scraped_data(
  overwrite = FALSE,
  local_embeddings = TRUE,
  dont_ask = TRUE
)
gpttools_index_all_scraped_data(
  overwrite = FALSE,
  local_embeddings = TRUE,
  dont_ask = TRUE
)

Arguments

`overwrite`	A logical value determining whether to overwrite existing indexes.
`local_embeddings`	A logical indicating whether to use local embeddings for indexing.
`dont_ask`	A logical value that, if TRUE, disables interactive confirmation prompts during the indexing process.

Details

The function first retrieves a list of all text files in the targeted directory. For each file, it extracts the domain name from the filename, prints an informative message about the indexing process for that domain, and then proceeds to create or update the index for the domain based on the function arguments.

Value

Invisible NULL. The function is called for its side effects.

Examples

# Index all scraped data without overwriting existing indexes, using local
# embeddings, and without interactive prompts.

## Not run: 
gpttools_index_all_scraped_data(
  overwrite = FALSE,
  local_embeddings = TRUE,
  dont_ask = TRUE
)

## End(Not run)

# Index all scraped data without overwriting existing indexes, using local
# embeddings, and without interactive prompts.

## Not run: 
gpttools_index_all_scraped_data(
  overwrite = FALSE,
  local_embeddings = TRUE,
  dont_ask = TRUE
)

## End(Not run)

Ingest PDF and write to index

Description

This function reads the text from a PDF file, processes it, and writes the result to an index.

Usage

ingest_pdf(file_path, source, link = NULL)
ingest_pdf(file_path, source, link = NULL)

Arguments

`file_path`	Character string specifying the path to the PDF file.
`source`	Character string specifying the source of the PDF.
`link`	Character string specifying the link to the PDF file (optional).

Value

The function writes an index in Parquet format to disk.

Wrapper around selectionGet to help with testthat

Description

Wrapper around selectionGet to help with testthat

Usage

insert_text(improved_text)
insert_text(improved_text)

Arguments

improved_text

Text from model queries to inert into script or document

Install sentence-transformers and its dependencies

Description

install_sentence_transformers() installs the sentence-transformers python package and its direct dependencies. This code is taken and only slightly modified from the tensorflow and reticulate packages. See the original code from tensorflow::install_tensorflow().

Usage

install_sentence_transformers(
  method = c("auto", "virtualenv", "conda"),
  conda = "auto",
  version = NULL,
  envname = NULL,
  extra_packages = NULL,
  restart_session = TRUE,
  conda_python_version = NULL,
  ...,
  pip_ignore_installed = TRUE,
  python_version = conda_python_version
)
install_sentence_transformers(
  method = c("auto", "virtualenv", "conda"),
  conda = "auto",
  version = NULL,
  envname = NULL,
  extra_packages = NULL,
  restart_session = TRUE,
  conda_python_version = NULL,
  ...,
  pip_ignore_installed = TRUE,
  python_version = conda_python_version
)

Arguments

`method`	Installation method. By default, "auto" automatically finds a method that will work in the local environment. Change the default to force a specific installation method. Note that the "virtualenv" method is not available on Windows.
`conda`	The path to a `conda` executable. Use `"auto"` to allow `reticulate` to automatically find an appropriate `conda` binary. See Finding Conda and `conda_binary()` for more details.
`version`	version to install. Valid values include: `"release"` installs the latest release version of sentence-transformers (which may be incompatible with the current version of the R package) A version specification like `"2.4"` or `"2.4.0"`. Note that if the patch version is not supplied, the latest patch release is installed (e.g., `"2.4"` today installs version "2.4.2") `nightly` for the latest available nightly build. The full URL or path to a installer binary or python *.whl file.
`envname`	The name, or full path, of the environment in which Python packages are to be installed. When `NULL` (the default), the active environment as set by the `RETICULATE_PYTHON_ENV` variable will be used; if that is unset, then the `r-reticulate` environment will be used.
`extra_packages`	Additional Python packages to install along with TensorFlow.
`restart_session`	Restart R session after installing (note this will only occur within RStudio).
`...`	other arguments passed to `reticulate::conda_install()` or `reticulate::virtualenv_install()`, depending on the `method` used.
`pip_ignore_installed`	Whether pip should ignore installed python packages and reinstall all already installed python packages. This defaults to `TRUE`, to ensure that TensorFlow dependencies like NumPy are compatible with the prebuilt TensorFlow binaries.
`python_version`, `conda_python_version`	Pass a string like "3.8" to request that conda install a specific Python version. This is ignored when attempting to install in a Python virtual environment.

Details

You may be prompted to download and install miniconda if reticulate did not find a non-system installation of python. Miniconda is the recommended installation method for most users, as it ensures that the R python installation is isolated from other python installations. All python packages will by default be installed into a self-contained conda or venv environment named "r-reticulate". Note that "conda" is the only supported method on M1 Mac. If you initially declined the miniconda installation prompt, you can later manually install miniconda by running reticulate::install_miniconda().

Custom Installation

install_sentence_transformers() or isn't required to use tensorflow with the package. If you manually configure a python environment with the required dependencies, you can tell R to use it by pointing reticulate at it, commonly by setting an environment variable:

Sys.setenv("RETICULATE_PYTHON" = "~/path/to/python-env/bin/python")

Additional Packages

If you wish to add additional PyPI packages to your Keras / TensorFlow environment you can either specify the packages in the extra_packages argument of install_sentence_transformers(), or alternatively install them into an existing environment using the reticulate::py_install() function.

Settings App for gpttools

Description

Settings App for gpttools

Usage

launch_settings()
launch_settings()

Value

This function has no return value.

List Index Files

Description

This function lists the index files in the specified directory.

Usage

list_index(dir = "index", full_path = FALSE)
list_index(dir = "index", full_path = FALSE)

Arguments

`dir`	Name of the directory, defaults to "index"
`full_path`	If TRUE, returns the full path to the index files.

Value

A character vector containing the names of the index files found in the specified directory.

Examples

## Not run: 
list_index()

## End(Not run)
## Not run: 
list_index()

## End(Not run)

Load Index Data for a Domain

Description

This function loads the index data for a given domain from a parquet file.

Usage

load_index(domain, local_embeddings = FALSE)
load_index(domain, local_embeddings = FALSE)

Arguments

`domain`	A character string indicating the name of the domain.
`local_embeddings`	A logical indicating whether to load the local embeddings or the OpenAI embeddings. Defaults to FALSE.

Value

A data frame containing the index data for the specified domain.

Examples

## Not run: 
load_index("example_domain")

## End(Not run)
## Not run: 
load_index("example_domain")

## End(Not run)

Preps OpenAI model prompt for data documentation

Description

Prepares data prompt by summarizing data and printing it

Usage

prep_data_prompt(data, method, prompt)
prep_data_prompt(data, method, prompt)

Arguments

`data`	A data.frame
`method`	A summarization method, one of "skimr", "glimpse", or "summary"
`prompt`	A prompt string

Value

A string

Examples

prep_data_prompt(
  data = mtcars,
  method = "skimr",
  prompt = "This is a test prompt."
)
prep_data_prompt(
  data = mtcars,
  method = "skimr",
  prompt = "This is a test prompt."
)

Read chat history from a file

Description

Read chat history from a file

Usage

read_history(file_name = "chat_history", local)
read_history(file_name = "chat_history", local)

Arguments

`file_name`	Name of the chat history file to read from. Default is "chat_history".
`local`	Whether to read from history made with local or OpenAI embeddings.

Value

A dataframe containing the chat history or NULL if the file doesn't exist.

Examples

## Not run: 
# Read chat history from the default file
history <- read_history()

## End(Not run)
## Not run: 
# Read chat history from the default file
history <- read_history()

## End(Not run)

Remove new lines from a character vector.

Description

Removes all new line characters from a character vector.

Usage

remove_lines_and_spaces(serie)
remove_lines_and_spaces(serie)

Arguments

serie

A character vector.

Value

A character vector with all new line characters removed.

Repair Index Names

Description

This function scans index files in a specified directory, checks if they lack a "name" column, and attempts to repair them by adding a "name" column based on the file name or user input.

Usage

repair_index_names(local = TRUE)
repair_index_names(local = TRUE)

Arguments

local

logical; if TRUE, the function operates on the local index directory, otherwise it uses the main index directory.

Value

The function does not return a value, but it modifies the index files in place by either adding a "name" column based on automated generation or user input.

Examples

## Not run: 
repair_index_names(TRUE)
repair_index_names(FALSE)

## End(Not run)

## Not run: 
repair_index_names(TRUE)
repair_index_names(FALSE)

## End(Not run)

Shiny app that supports Document Data addin

Description

Shiny app that supports Document Data addin

Usage

run_document_data()
run_document_data()

Value

Nothing is returned, a shiny app is run

Execute and Present Extracted Code Chunks

Description

This function takes extracted code chunks and utilizes the reprex packages to run them. It then formats the output into a collapsible HTML section for easy sharing and viewing. Images within the output are converted to clickable links to enhance accessibility and compactness of the presentation.

Usage

run_extracted_code(code)
run_extracted_code(code)

Arguments

code

A character vector of code to be executed and presented. The code is run with reprex::reprex(), capturing the outcome for presentation.

Value

An HTML-formatted string that includes the collapsible section for the code's output. This HTML string can be embedded in web pages, markdown documents, or viewed directly in browsers. The function is designed to work seamlessly in interactive environments, hence it might return the output invisibly.

Examples


code <- "plot(1:10)"
html_output <- run_extracted_code(code)
# The returned value is an HTML string with the plotted output presented within a
# collapsible section and any images replaced by links.

code <- "plot(1:10)"
html_output <- run_extracted_code(code)
# The returned value is an HTML string with the plotted output presented within a
# collapsible section and any images replaced by links.

Run a Shiny App to Select and Save Installed Packages

Description

This function launches a Shiny application that allows users to select from a list of installed packages and save their selections.

Usage

run_select_pkgs_app()
run_select_pkgs_app()

Details

The application provides a sidebar for package selection and an action button to save the selected packages. It displays the selected packages in a data table.

Value

None The function is used for its side effect of launching a Shiny app and doesn't return anything.

Examples


run_select_pkgs_app()

run_select_pkgs_app()

Save user configuration settings for gpttools

Description

This function saves the user's configuration settings for gpttools service to a YAML file.

Usage

save_user_config(
  service = "openai",
  model = "gpt-4-turbo-preview",
  task = "Permissive Chat",
  local_embed = FALSE,
  openai_embed_model = "text-embedding-3-small",
  local_embed_model = "BAAI/bge-small-en-v1.5",
  k_context = 4,
  k_history = 4,
  save_history = TRUE,
  add_context = "sometimes",
  sources = "All",
  run_code = FALSE,
  persist = TRUE
)
save_user_config(
  service = "openai",
  model = "gpt-4-turbo-preview",
  task = "Permissive Chat",
  local_embed = FALSE,
  openai_embed_model = "text-embedding-3-small",
  local_embed_model = "BAAI/bge-small-en-v1.5",
  k_context = 4,
  k_history = 4,
  save_history = TRUE,
  add_context = "sometimes",
  sources = "All",
  run_code = FALSE,
  persist = TRUE
)

Arguments

`service`	The name of the service to use, default is "openai".
`model`	The model to use, default is "gpt-4-1106-preview".
`task`	The task to perform, default is "Permissive Chat".
`local_embed`	Whether to use local embedding model. Default is FALSE.
`openai_embed_model`	The OpenAI embeddings model to use, default is "text-embedding-3-small".
`local_embed_model`	The local embeddings model to use, default is "BAAI/bge-small-en-v1.5".
`k_context`	The amount of context to keep, default is 4.
`k_history`	The amount of history to keep, default is 4.
`save_history`	Logical indicating whether history should be saved, default is TRUE.
`add_context`	Whether to add context to the query. Options are `"always"`, `"sometimes"`, and `"never"`. The default is `"sometimes"`.
`sources`	The sources to use, default is "All".
`run_code`	Whether to execute generated code with `reprex::reprex()`, default is FALSE.
`persist`	Logical indicating whether to persist the settings, default is TRUE.

Value

Invisible NULL.

Scrape packaging sites

Description

Scrape packaging sites

Usage

scrape_pkg_sites(
  sites = get_pkgs_to_scrape(local = TRUE),
  service = "local",
  index_create = TRUE,
  overwrite = TRUE,
  parallel = FALSE
)
scrape_pkg_sites(
  sites = get_pkgs_to_scrape(local = TRUE),
  service = "local",
  index_create = TRUE,
  overwrite = TRUE,
  parallel = FALSE
)

Arguments

`sites`	A data frame containing the package sites to be scraped. If not provided, it defaults to `get_pkgs_to_scrape(local = TRUE)`.
`service`	The service to be used for scraping, defaults to "local".
`index_create`	Logical indicating whether to create an index, defaults to `TRUE`.
`overwrite`	Logical indicating whether to overwrite existing content, defaults to `TRUE`.
`parallel`	Logical indicating whether to use parallel processing, defaults to `FALSE`.

Details

This function scrapes the websites for the packages specified in the sites dataframe. If sites is empty, it alerts the user with no packages to scrape and returns NULL invisibly. If the user confirms to proceed, it scrapes each package site using the supplied details.

Value

Invisible NULL. The function is called for its side effects.

Examples


scrape_pkg_sites()

scrape_pkg_sites()

Scrape text from a URL

Description

This function scrapes the text from a URL and returns a character vector.

Usage

scrape_url(url)
scrape_url(url)

Arguments

url

A character string containing a valid URL.

Value

A character vector containing the text from the URL.

Convert Script to Function Addin

Description

"convert this R code into an R function"

Usage

script_to_function_addin()
script_to_function_addin()

Set user configuration settings for gpttools from a file

Description

This function sets the user's configuration settings for gpttools by reading a YAML file. If no path is provided, it will look for the file in the default R user config directory.

Usage

set_user_config(path = NULL)
set_user_config(path = NULL)

Arguments

path

The path to the config YAML file. If NULL, the default path is used.

Value

Invisible NULL.

Suggest code improvements using a code copilot

Description

This function provides suggestions for improving the provided code using an expert code copilot. The output is formatted for inclusion in an R file.

Usage

suggest_code_improvements()
suggest_code_improvements()

Value

A string containing suggestions for code improvements, including descriptions of the changes and any necessary annotations as code comments. The output is formatted for inclusion in an R file.

Suggest a unit test for a function

Description

Prompt: "Suggest a unit test for this function using the testthat package"

Usage

suggest_unit_test_addin()
suggest_unit_test_addin()

Summarize data

Description

Summarize a data frame using one of three methods.

Usage

summarize_data(
  data,
  method = c("skimr", "skimr_lite", "column_types", "summary")
)
summarize_data(
  data,
  method = c("skimr", "skimr_lite", "column_types", "summary")
)

Arguments

`data`	A data frame
`method`	A character vector specifying the method to use for summarizing the data. Must be one of "skimr", "skimr_lite", "column_types", or "summary". Default is "skimr".

Value

Summarized data according to specified method

Transcribe audio and write to index

Description

This function transcribes audio files, processes the text, and writes the result to an index.

Usage

transcribe_audio(
  file_path,
  source = NA,
  link = NA,
  prompt = NA,
  chunk_size = 120
)
transcribe_audio(
  file_path,
  source = NA,
  link = NA,
  prompt = NA,
  chunk_size = 120
)

Arguments

`file_path`	Character string specifying the path to the audio file.
`source`	Character string specifying the source of the audio.
`link`	Character string specifying the HTML link to the audio file (optional).
`prompt`	Character string specifying the prompt for transcription (optional).
`chunk_size`	Audio size in seconds

Value

The function writes an index in Parquet format to disk.

Package 'gpttools'

Help Index

Add Roxygen documentation to a function

Description

Usage

Value

Addin to scrape installed packages

Description

Usage

Value

Note

Examples

Run Package Selector App

Description

Usage

Value

Chat for gpttools

Description

Usage

Arguments

Value

Examples

chat_with_context

Description

Usage

Arguments

Value

Examples

Run Chat with Retrieval

Description

Usage

Value

Examples

Collect Dataframes

Description

Usage

Value

Copilot-style Code Suggestions

Description

Usage

Scrape and process all hyperlinks within a given URL

Description

Usage

Arguments

Value

Create index from audio

Description

Usage

Arguments

Value

Create index from PDF

Description

Usage

Arguments

Value

Get Transcription from Audio

Description

Usage

Arguments

Value

Delete chat history files

Description

Usage

Arguments

Examples

Delete an Index File

Description

Usage

Document Data

Description

Usage

Extract Code Chunks from Text

Description

Usage

Arguments

Value

Examples

Wrapper around selectionGet to help with testthat

Description

Usage