Package 'chattr'

Submits prompt to LLM

Description

Submits prompt to LLM

Usage

chattr(prompt = NULL, preview = FALSE, prompt_build = TRUE, stream = NULL)

Arguments

prompt	Request to send to LLM. Defaults to NULL
preview	Primarily used for debugging. It indicates if it should send the prompt to the LLM (FALSE), or if it should print out the resulting prompt (TRUE)
prompt_build	Include the context and additional prompt as part of the request
stream	To output the response from the LLM as it happens, or wait until the response is complete. Defaults to TRUE.

Value

The output of the LLM to the console, document or script.

Examples

library(chattr)
chattr_use("test")
chattr("hello")
chattr("hello", preview = TRUE)

---

Starts a Shiny app interface to the LLM

Description

Starts a Shiny app interface to the LLM

Usage

chattr_app(
  viewer = c("viewer", "dialog"),
  as_job = getOption("chattr.as_job", FALSE),
  as_job_port = getOption("shiny.port", 7788),
  as_job_host = getOption("shiny.host", "127.0.0.1")
)

Arguments

viewer	Specifies where the Shiny app is going to display
as_job	App runs as an RStudio IDE Job. Defaults to FALSE. If set to TRUE, the Shiny app will not be able to transfer the code blocks directly to the document, or console, in the IDE.
as_job_port	Port to use for the Shiny app. Applicable only if as_job is set to TRUE.
as_job_host	Host IP to use for the Shiny app. Applicable only if as_job is set to TRUE.

Value

A chat interface inside the 'RStudio' IDE

---

Default arguments to use when making requests to the LLM

Description

Default arguments to use when making requests to the LLM

Usage

chattr_defaults(
  type = "default",
  prompt = NULL,
  max_data_files = NULL,
  max_data_frames = NULL,
  include_doc_contents = NULL,
  include_history = NULL,
  provider = NULL,
  path = NULL,
  model = NULL,
  model_arguments = NULL,
  system_msg = NULL,
  yaml_file = "chattr.yml",
  force = FALSE,
  label = NULL,
  ...
)

Arguments

type	Entry point to interact with the model. Accepted values: 'notebook', chat'
prompt	Request to send to LLM. Defaults to NULL
max_data_files	Sets the maximum number of data files to send to the model. It defaults to 20. To send all, set to NULL
max_data_frames	Sets the maximum number of data frames loaded in the current R session to send to the model. It defaults to 20. To send all, set to NULL
include_doc_contents	Send the current code in the document
include_history	Indicates whether to include the chat history when every time a new prompt is submitted
provider	The name of the provider of the LLM. Today, only "openai" is is available
path	The location of the model. It could be an URL or a file path.
model	The name or path to the model to use.
model_arguments	Additional arguments to pass to the model as part of the request, it requires a list. Examples of arguments: temperature, top_p, max_tokens
system_msg	For OpenAI GPT 3.5 or above, the system message to send as part of the request
yaml_file	The path to a valid config YAML file that contains the defaults to use in a session
force	Re-process the base and any work space level file defaults
label	Label to display in the Shiny app, and other locations
...	Additional model arguments that are not standard for all models/backends

Details

The idea is that because we will use addin shortcut to execute the request, all of the other arguments can be controlled via this function. By default, it will try to load defaults from a config YAML file, if none are found, then the defaults for GPT 3.5 will be used. The defaults can be modified by calling this function, even after the interactive session has started.

Value

An 'ch_model' object that contains the current defaults that will be used to communicate with the LLM.

---

Saves the current defaults in a yaml file that is compatible with the config package

Description

Saves the current defaults in a yaml file that is compatible with the config package

Usage

chattr_defaults_save(path = "chattr.yml", overwrite = FALSE, type = NULL)

Arguments

path	Path to the file to save the configuration to
overwrite	Indicates to replace the file if it exists
type	The type of UI to save the defaults for. It defaults to NULL which will save whatever types had been used during the current R session

Value

It creates a YAML file with the defaults set in the current R session.

---

Confirms connectivity to LLM interface

Description

Confirms connectivity to LLM interface

Usage

chattr_test(defaults = NULL)

ch_test(defaults = NULL)

Arguments

defaults

Defaults object, generally puled from chattr_defaults()

Value

It returns console massages with the status of the test.

---

Sets the LLM model to use in your session

Description

Sets the LLM model to use in your session

Usage

chattr_use(x = NULL, ...)

Arguments

x	The label of the LLM model to use, or the path of a valid YAML default file . Valid values are 'copilot', 'gpt4', 'gpt35', 'llamagpt', 'databricks-dbrx', 'databricks-meta-llama3-70b', and 'databricks-mixtral8x7b'. The value 'test' is also acceptable, but it is meant for package examples, and internal testing.
...	Default values to modify.

Details

If the error "No model setup found" was returned, that is because none of the expected setup for Copilot, OpenAI or LLama was automatically detected. Here is how to setup a model:

• OpenAI - The main thing chattr checks is the presence of the R user's OpenAI PAT (Personal Access Token). It looks for it in the 'OPENAI_API_KEY' environment variable. Get a PAT from the OpenAI website, and save it to that environment variable. Then restart R, and try again.

• GitHub Copilot - Setup GitHub Copilot in your RStudio IDE, and restart R. chattr will look for the default location where RStudio saves the Copilot authentication information.

• Databricks - chattr checks for presence of R user's Databricks host and token ('DATABRICKS_HOST' and 'DATABRICKS TOKEN' environment variables).

Use the 'CHATTR_MODEL' environment variable to set it for the R session, or create a YAML file named 'chattr.yml' in your working directory to control the model, and the defaults it will use to communicate with such model.

Value

It returns console messages to allow the user select the model to use.

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