Package 'tidyllm' reference manual

Title:	Tidy Integration of Large Language Models
Description:	A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'Openai', 'Groq','Mistral' and local models via 'Ollama' into R workflows. The package supports text and media-based interactions, interactive message history, batch request APIs, and a tidy, pipeline-oriented interface for streamlined integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://groq.com>, <https://mistral.ai/> and <https://ollama.com>.
Authors:	Eduard Brüll [aut, cre]
Maintainer:	Eduard Brüll <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.0
Built:	2024-11-08 10:31:20 UTC
Source:	CRAN

Send LLM Messages to an OpenAI Chat Completions endpoint on Azure

Description

This function sends a message history to the Azure OpenAI Chat Completions API and returns the assistant's reply. This function is work in progress and not fully tested

Usage

azure_openai(
  .llm,
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .deployment = "gpt-4o-mini",
  .api_version = "2024-08-01-preview",
  .max_completion_tokens = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = NULL,
  .top_p = NULL,
  .timeout = 60,
  .verbose = FALSE,
  .json = FALSE,
  .json_schema = NULL,
  .dry_run = FALSE,
  .max_tries = 3
)
azure_openai(
  .llm,
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .deployment = "gpt-4o-mini",
  .api_version = "2024-08-01-preview",
  .max_completion_tokens = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = NULL,
  .top_p = NULL,
  .timeout = 60,
  .verbose = FALSE,
  .json = FALSE,
  .json_schema = NULL,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

`.llm`	An `LLMMessage` object containing the conversation history.
`.endpoint_url`	Base URL for the API (default: Sys.getenv("AZURE_ENDPOINT_URL")).
`.deployment`	The identifier of the model that is deployed (default: "gpt-4o-mini").
`.api_version`	Which version of the API is deployed (default: "2024-08-01-preview")
`.max_completion_tokens`	An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
`.frequency_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far.
`.logit_bias`	A named list modifying the likelihood of specified tokens appearing in the completion.
`.logprobs`	Whether to return log probabilities of the output tokens (default: FALSE).
`.top_logprobs`	An integer between 0 and 20 specifying the number of most likely tokens to return at each token position.
`.presence_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.
`.seed`	If specified, the system will make a best effort to sample deterministically.
`.stop`	Up to 4 sequences where the API will stop generating further tokens.
`.stream`	If set to TRUE, the answer will be streamed to console as it comes (default: FALSE).
`.temperature`	What sampling temperature to use, between 0 and 2. Higher values make the output more random.
`.top_p`	An alternative to sampling with temperature, called nucleus sampling.
`.timeout`	Request timeout in seconds (default: 60).
`.verbose`	Should additional information be shown after the API call (default: FALSE).
`.json`	Should output be in JSON mode (default: FALSE).
`.json_schema`	A JSON schema object as R list to enforce the output structure (If defined has precedence over JSON mode).
`.dry_run`	If TRUE, perform a dry run and return the request object (default: FALSE).
`.max_tries`	Maximum retries to perform request

Value

A new LLMMessage object containing the original messages plus the assistant's response.

Examples

## Not run: 
# Basic usage
msg <- llm_message("What is R programming?")
result <- azure_openai(msg)

# With custom parameters
result2 <- azure_openai(msg, 
                 .deployment = "gpt-4o-mini",
                 .temperature = 0.7, 
                 .max_tokens = 1000)

## End(Not run)

## Not run: 
# Basic usage
msg <- llm_message("What is R programming?")
result <- azure_openai(msg)

# With custom parameters
result2 <- azure_openai(msg, 
                 .deployment = "gpt-4o-mini",
                 .temperature = 0.7, 
                 .max_tokens = 1000)

## End(Not run)

ChatGPT Wrapper (Deprecated)

Description

Provides a wrapper for the openai() function to facilitate migration from the deprecated chatgpt() function. This ensures backward compatibility while allowing users to transition to the updated features.

Usage

chatgpt(
  .llm,
  .model = "gpt-4o",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .top_k = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .api_url = "https://api.openai.com/",
  .timeout = 60,
  .verbose = FALSE,
  .json = FALSE,
  .stream = FALSE,
  .dry_run = FALSE
)
chatgpt(
  .llm,
  .model = "gpt-4o",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .top_k = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .api_url = "https://api.openai.com/",
  .timeout = 60,
  .verbose = FALSE,
  .json = FALSE,
  .stream = FALSE,
  .dry_run = FALSE
)

Arguments

`.llm`	An `LLMMessage` (passed directly to the `openai()` function)
`.model`	A character string specifying the model to use.
`.max_tokens`	An integer specifying the maximum number of tokens (mapped to `.max_completion_tokens` in `openai()`)
`.temperature`	A numeric value for controlling randomness. This is
`.top_p`	A numeric value for nucleus sampling, indicating the top
`.top_k`	Currently unused, as it is not supported by `openai()`.
`.frequency_penalty`	A numeric value that penalizes new tokens based on their frequency so far.
`.presence_penalty`	A numeric value that penalizes new tokens based on whether they appear in the text so far.
`.api_url`	Character string specifying the API URL. Defaults to the OpenAI API endpoint.
`.timeout`	An integer specifying the request timeout in seconds. This is
`.verbose`	Will print additional information about the request (default: false)
`.json`	Should json-mode be used? (detault: false)
`.stream`	Should the response be processed as a stream (default: false)
`.dry_run`	Should the request is constructed but not actually sent. Useful for debugging and testing. (default: false)

Details

This function is deprecated and is now a wrapper around openai(). It is recommended to switch to using openai() directly in future code. The chatgpt() function remains available to ensure backward compatibility for existing projects.

Value

An LLMMessage object with the assistant's reply.

Examples

## Not run: 
# Using the deprecated chatgpt() function
result <- chatgpt(.llm = llm_message(), .prompt = "Hello, how are you?")

## End(Not run)

## Not run: 
# Using the deprecated chatgpt() function
result <- chatgpt(.llm = llm_message(), .prompt = "Hello, how are you?")

## End(Not run)

Check Batch Processing Status for Claude API

Description

This function retrieves the processing status and other details of a specified Claude batch ID from the Claude API.

Usage

check_claude_batch(
  .llms = NULL,
  .batch_id = NULL,
  .api_url = "https://api.anthropic.com/",
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)
check_claude_batch(
  .llms = NULL,
  .batch_id = NULL,
  .api_url = "https://api.anthropic.com/",
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)

Arguments

`.llms`	A list of LLMMessage objects
`.batch_id`	A manually set batchid
`.api_url`	Character; base URL of the Claude API (default: "https://api.anthropic.com/").
`.dry_run`	Logical; if TRUE, returns the prepared request object without executing it (default: FALSE).
`.max_tries`	Maximum retries to peform request
`.timeout`	Integer specifying the request timeout in seconds (default: 60).

Value

A tibble with information about the status of batch processing

Check Batch Processing Status for OpenAI Batch API

Description

This function retrieves the processing status and other details of a specified OpenAI batch ID from the OpenAI Batch API.

Usage

check_openai_batch(
  .llms = NULL,
  .batch_id = NULL,
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)
check_openai_batch(
  .llms = NULL,
  .batch_id = NULL,
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)

Arguments

`.llms`	A list of LLMMessage objects.
`.batch_id`	A manually set batch ID.
`.dry_run`	Logical; if TRUE, returns the prepared request object without executing it (default: FALSE).
`.max_tries`	Maximum retries to perform the request (default: 3).
`.timeout`	Integer specifying the request timeout in seconds (default: 60).

Value

A tibble with information about the status of batch processing.

Interact with Claude AI models via the Anthropic API

Description

Interact with Claude AI models via the Anthropic API

Usage

claude(
  .llm,
  .model = "claude-3-5-sonnet-20241022",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .metadata = NULL,
  .stop_sequences = NULL,
  .tools = NULL,
  .api_url = "https://api.anthropic.com/",
  .verbose = FALSE,
  .max_tries = 3,
  .timeout = 60,
  .stream = FALSE,
  .dry_run = FALSE
)
claude(
  .llm,
  .model = "claude-3-5-sonnet-20241022",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .metadata = NULL,
  .stop_sequences = NULL,
  .tools = NULL,
  .api_url = "https://api.anthropic.com/",
  .verbose = FALSE,
  .max_tries = 3,
  .timeout = 60,
  .stream = FALSE,
  .dry_run = FALSE
)

Arguments

`.llm`	An LLMMessage object containing the conversation history and system prompt.
`.model`	Character string specifying the Claude model version (default: "claude-3-5-sonnet-20241022").
`.max_tokens`	Integer specifying the maximum number of tokens in the response (default: 1024).
`.temperature`	Numeric between 0 and 1 controlling response randomness.
`.top_k`	Integer controlling diversity by limiting the top K tokens.
`.top_p`	Numeric between 0 and 1 for nucleus sampling.
`.metadata`	List of additional metadata to include with the request.
`.stop_sequences`	Character vector of sequences that will halt response generation.
`.tools`	List of additional tools or functions the model can use.
`.api_url`	Base URL for the Anthropic API (default: "https://api.anthropic.com/").
`.verbose`	Logical; if TRUE, displays additional information about the API call (default: FALSE).
`.max_tries`	Maximum retries to peform request
`.timeout`	Integer specifying the request timeout in seconds (default: 60).
`.stream`	Logical; if TRUE, streams the response piece by piece (default: FALSE).
`.dry_run`	Logical; if TRUE, returns the prepared request object without executing it (default: FALSE).

Value

A new LLMMessage object containing the original messages plus Claude's response.

Examples

## Not run: 
# Basic usage
msg <- llm_message("What is R programming?")
result <- claude(msg)

# With custom parameters
result2 <- claude(msg, 
                 .temperature = 0.7, 
                 .max_tokens = 1000)

## End(Not run)

## Not run: 
# Basic usage
msg <- llm_message("What is R programming?")
result <- claude(msg)

# With custom parameters
result2 <- claude(msg, 
                 .temperature = 0.7, 
                 .max_tokens = 1000)

## End(Not run)

Convert a Data Frame to an LLMMessage Object

Description

This function takes a data frame and converts it into an LLMMessage object representing a conversation history. The data frame should contain specific columns (role and content) with each row representing a message in the conversation.

Usage

df_llm_message(.df)
df_llm_message(.df)

Arguments

.df

A data frame with at least two rows and columns role and content. The column role should contain values from "user", "assistant", or "system", and content should be of type character.

Value

An LLMMessage object containing the structured messages as per the input data frame.

Fetch Results for a Claude Batch

Description

This function retrieves the results of a completed Claude batch and updates the provided list of LLMMessage objects with the responses. It aligns each response with the original request using the custom_ids generated in send_claude_batch().

Usage

fetch_claude_batch(
  .llms,
  .batch_id = NULL,
  .api_url = "https://api.anthropic.com/",
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)
fetch_claude_batch(
  .llms,
  .batch_id = NULL,
  .api_url = "https://api.anthropic.com/",
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)

Arguments

`.llms`	A list of `LLMMessage` objects that were part of the batch. The list should have names (custom IDs) set by `send_claude_batch()` to ensure correct alignment.
`.batch_id`	Character; the unique identifier for the batch. By default this is NULL and the function will attempt to use the `batch_id` attribute from `.llms`.
`.api_url`	Character; the base URL for the Claude API (default: "https://api.anthropic.com/").
`.dry_run`	Logical; if `TRUE`, returns the constructed request without executing it (default: `FALSE`).
`.max_tries`	Integer; maximum number of retries if the request fails (default: `3`).
`.timeout`	Integer; request timeout in seconds (default: `60`).

Value

A list of updated LLMMessage objects, each with the assistant's response added if successful.

Fetch Results for an OpenAI Batch

Description

This function retrieves the results of a completed OpenAI batch and updates the provided list of LLMMessage objects with the responses. It aligns each response with the original request using the custom_ids generated in send_openai_batch().

Usage

fetch_openai_batch(
  .llms,
  .batch_id = NULL,
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)
fetch_openai_batch(
  .llms,
  .batch_id = NULL,
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)

Arguments

`.llms`	A list of `LLMMessage` objects that were part of the batch.
`.batch_id`	Character; the unique identifier for the batch. By default this is NULL and the function will attempt to use the `batch_id` attribute from `.llms`.
`.dry_run`	Logical; if `TRUE`, returns the constructed request without executing it (default: `FALSE`).
`.max_tries`	Integer; maximum number of retries if the request fails (default: `3`).
`.timeout`	Integer; request timeout in seconds (default: `60`).

Value

A list of updated LLMMessage objects, each with the assistant's response added if successful.

Generate API-Specific Callback Function for Streaming Responses

Description

This function generates a callback function that processes streaming responses from different language model APIs. The callback function is specific to the API provided (claude, ollama, "mistral", or openai) and processes incoming data streams, printing the content to the console and updating a global environment for further use.

Usage

generate_callback_function(.api)
generate_callback_function(.api)

Arguments

.api

A character string indicating the API type. Supported values are "claude", "ollama", "mistral", "groq" and "openai".

Details

For Claude API: The function processes event and data lines, and handles the message_start and message_stop events to control streaming flow.
For Ollama API: The function directly parses the stream content as JSON and extracts the message$content field.
For OpenAI, Mistral and Groq: The function handles JSON data streams and processes content deltas. It stops processing when the ⁠[DONE]⁠ message is encountered.

Value

A function that serves as a callback to handle streaming responses from the specified API. The callback function processes the raw data, updates the .tidyllm_stream_env$stream object, and prints the streamed content to the console. The function returns TRUE if streaming should continue, and FALSE when streaming is finished.

Get Assistant Reply as Text

Description

Retrieves the assistant's reply as plain text from an LLMMessage object at a specified index.

Usage

get_reply(.llm, .index = NULL)
get_reply(.llm, .index = NULL)

Arguments

`.llm`	An `LLMMessage` object containing the message history.
`.index`	A positive integer for the assistant reply index to retrieve, defaulting to the last reply.

Value

Plain text content of the assistant's reply, or NA_character_ if no reply is available.

Get Data from an Assistant Reply by parsing structured JSON responses

Description

Retrieves and parses the assistant's reply as JSON from an LLMMessage object at a specified index. If the reply is not marked as JSON, attempts to extract JSON content from text.

Usage

get_reply_data(.llm, .index = NULL)
get_reply_data(.llm, .index = NULL)

Arguments

`.llm`	An `LLMMessage` object containing the message history.
`.index`	A positive integer for the assistant reply index to retrieve, defaulting to the last reply.

Value

Parsed data content of the assistant's reply, or NULL if parsing fails.

Retrieve a User Message by Index

Description

Extracts the content of a user's message from an LLMMessage object at a specific index.

Usage

get_user_message(.llm, .index = NULL)
get_user_message(.llm, .index = NULL)

Arguments

`.llm`	A `LLMMessage` object.
`.index`	A positive integer indicating which user message to retrieve. Defaults to `NULL`, which retrieves the last message.

Value

Returns the content of the user's message at the specified index. If no messages are found, returns NULL.

Send LLM Messages to the Groq Chat API

Description

This function sends a message history to the Groq Chat API and returns the assistant's reply.

Usage

groq(
  .llm,
  .model = "llama-3.2-11b-vision-preview",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .stop = NULL,
  .seed = NULL,
  .api_url = "https://api.groq.com/",
  .json = FALSE,
  .timeout = 60,
  .verbose = FALSE,
  .stream = FALSE,
  .dry_run = FALSE,
  .max_tries = 3
)
groq(
  .llm,
  .model = "llama-3.2-11b-vision-preview",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .stop = NULL,
  .seed = NULL,
  .api_url = "https://api.groq.com/",
  .json = FALSE,
  .timeout = 60,
  .verbose = FALSE,
  .stream = FALSE,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

`.llm`	An `LLMMessage` object containing the conversation history.
`.model`	The identifier of the model to use (default: "llama-3.2-11b-vision-preview").
`.max_tokens`	The maximum number of tokens that can be generated in the response (default: 1024).
`.temperature`	Controls the randomness in the model's response. Values between 0 and 2 are allowed, where higher values increase randomness (optional).
`.top_p`	Nucleus sampling parameter that controls the proportion of probability mass considered. Values between 0 and 1 are allowed (optional).
`.frequency_penalty`	Number between -2.0 and 2.0. Positive values penalize repeated tokens, reducing likelihood of repetition (optional).
`.presence_penalty`	Number between -2.0 and 2.0. Positive values encourage new topics by penalizing tokens that have appeared so far (optional).
`.stop`	One or more sequences where the API will stop generating further tokens. Can be a string or a list of strings (optional).
`.seed`	An integer for deterministic sampling. If specified, attempts to return the same result for repeated requests with identical parameters (optional).
`.api_url`	Base URL for the Groq API (default: "https://api.groq.com/").
`.json`	Whether the response should be structured as JSON (default: FALSE).
`.timeout`	Request timeout in seconds (default: 60).
`.verbose`	If TRUE, displays additional information after the API call, including rate limit details (default: FALSE).
`.stream`	Logical; if TRUE, streams the response piece by piece (default: FALSE).
`.dry_run`	If TRUE, performs a dry run and returns the constructed request object without executing it (default: FALSE).
`.max_tries`	Maximum retries to peform request

Value

A new LLMMessage object containing the original messages plus the assistant's response.

Examples

## Not run: 
# Basic usage
msg <- llm_message("What is Groq?")
result <- groq(msg)

# With custom parameters
result2 <- groq(msg, 
               .model = "llama-3.2-vision",
               .temperature = 0.5, 
               .max_tokens = 512)

## End(Not run)

## Not run: 
# Basic usage
msg <- llm_message("What is Groq?")
result <- groq(msg)

# With custom parameters
result2 <- groq(msg, 
               .model = "llama-3.2-vision",
               .temperature = 0.5, 
               .max_tokens = 512)

## End(Not run)

Transcribe an Audio File Using Groq transcription API

Description

This function reads an audio file and sends it to the Groq transcription API for transcription.

Usage

groq_transcribe(
  .audio_file,
  .model = "whisper-large-v3",
  .language = NULL,
  .prompt = NULL,
  .temperature = 0,
  .api_url = "https://api.groq.com/openai/v1/audio/transcriptions",
  .dry_run = FALSE,
  .verbose = FALSE,
  .max_tries = 3
)
groq_transcribe(
  .audio_file,
  .model = "whisper-large-v3",
  .language = NULL,
  .prompt = NULL,
  .temperature = 0,
  .api_url = "https://api.groq.com/openai/v1/audio/transcriptions",
  .dry_run = FALSE,
  .verbose = FALSE,
  .max_tries = 3
)

Arguments

`.audio_file`	The path to the audio file (required). Supported formats include flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
`.model`	The model to use for transcription (default: "whisper-large-v3").
`.language`	The language of the input audio, in ISO-639-1 format (optional).
`.prompt`	A prompt to guide the transcription style. It should match the audio language (optional).
`.temperature`	Sampling temperature, between 0 and 1, with higher values producing more randomness (default: 0).
`.api_url`	Base URL for the API (default: "https://api.groq.com/openai/v1/audio/transcriptions").
`.dry_run`	Logical; if TRUE, performs a dry run and returns the request object without making the API call (default: FALSE).
`.verbose`	Logical; if TRUE, rate limiting info is displayed after the API request (default: FALSE).
`.max_tries`	Maximum retries to peform request

Value

A character vector containing the transcription.

Examples

## Not run: 
# Basic usage
groq_transcribe(.audio_file = "example.mp3")

## End(Not run)

## Not run: 
# Basic usage
groq_transcribe(.audio_file = "example.mp3")

## End(Not run)

Initialize or Retrieve API-specific Environment

Description

This function initializes a named environment for storing rate limit information specific to an API. It ensures that each API's rate limit data is stored separately.

Usage

initialize_api_env(.api_name)
initialize_api_env(.api_name)

Arguments

.api_name

The name of the API for which to initialize or retrieve the environment

Get the Last Assistant Reply as Text

Description

A wrapper around get_reply() to retrieve the most recent assistant text reply.

Usage

last_reply(.llm)
last_reply(.llm)

Arguments

.llm

A LLMMessage object.

Value

Returns the content of the assistant's reply at the specified index, based on the following conditions:

Get the Last Assistant Reply as Text

Description

A wrapper around get_reply_data() to retrieve structured data from the most recent assistant reply.

Usage

last_reply_data(.llm)
last_reply_data(.llm)

Arguments

.llm

A LLMMessage object.

Value

Returns the content of the assistant's reply at the specified index, based on the following conditions:

Retrieve the Last User Message

Description

A wrapper around get_user_message() to retrieve the most recent user message.

Usage

last_user_message(.llm)
last_user_message(.llm)

Arguments

.llm

A LLMMessage object.

Value

The content of the last user message.

List Claude Batch Requests

Description

Retrieves batch request details from the Claude API.

Usage

list_claude_batches(
  .api_url = "https://api.anthropic.com/",
  .limit = 20,
  .max_tries = 3,
  .timeout = 60
)
list_claude_batches(
  .api_url = "https://api.anthropic.com/",
  .limit = 20,
  .max_tries = 3,
  .timeout = 60
)

Arguments

`.api_url`	Base URL for the Claude API (default: "https://api.anthropic.com/").
`.limit`	Maximum number of batches to retrieve (default: 20).
`.max_tries`	Maximum retry attempts for requests (default: 3).
`.timeout`	Request timeout in seconds (default: 60).

Value

A tibble with batch details: batch ID, status, creation time, expiration time, and request counts (succeeded, errored, expired, canceled).

List OpenAI Batch Requests

Description

Retrieves batch request details from the OpenAI Batch API.

Usage

list_openai_batches(.limit = 20, .max_tries = 3, .timeout = 60)
list_openai_batches(.limit = 20, .max_tries = 3, .timeout = 60)

Arguments

`.limit`	Maximum number of batches to retrieve (default: 20).
`.max_tries`	Maximum retry attempts for requests (default: 3).
`.timeout`	Request timeout in seconds (default: 60).

Value

A tibble with batch details: batch ID, status, creation time, expiration time, and request counts (total, completed, failed).

Create or Update Large Language Model Message Object

Description

This function allows the creation of a new LLMMessage object or the updating of an existing one. It can handle the addition of text prompts and various media types such as images, PDFs, text files, or plots. The function includes input validation to ensure that all provided parameters are in the correct format.

Usage

llm_message(
  .llm = NULL,
  .prompt = NULL,
  .role = "user",
  .system_prompt = "You are a helpful assistant",
  .imagefile = NULL,
  .pdf = NULL,
  .textfile = NULL,
  .capture_plot = FALSE,
  .f = NULL
)
llm_message(
  .llm = NULL,
  .prompt = NULL,
  .role = "user",
  .system_prompt = "You are a helpful assistant",
  .imagefile = NULL,
  .pdf = NULL,
  .textfile = NULL,
  .capture_plot = FALSE,
  .f = NULL
)

Arguments

`.llm`	An existing LLMMessage object or an initial text prompt.
`.prompt`	Text prompt to add to the message history.
`.role`	The role of the message sender, typically "user" or "assistant".
`.system_prompt`	Default system prompt if a new LLMMessage needs to be created.
`.imagefile`	Path to an image file to be attached (optional).
`.pdf`	Path to a PDF file to be attached (optional). Can be a character vector of length one (file path), or a list with `filename`, `start_page`, and `end_page`.
`.textfile`	Path to a text file to be read and attached (optional).
`.capture_plot`	Boolean to indicate whether a plot should be captured and attached as an image (optional).
`.f`	An R function or an object coercible to a function via `rlang::as_function`, whose output should be captured and attached (optional).

Value

Returns an updated or new LLMMessage object.

Large Language Model Message Class

Description

Large Language Model Message Class

Details

This class manages a history of messages and media interactions intended for use with large language models. It allows for adding messages, converting messages for API usage, and printing the history in a structured format.

Public fields

message_history: List to store all message interactions.
system_prompt: The system prompt used for a conversation

Methods

Method `new()`

Initializes the LLMMessage object with an optional system prompt.

Usage

LLMMessage$new(system_prompt = "You are a helpful assistant")

Arguments

system_prompt: A string that sets the initial system prompt.

Returns

A new LLMMessage object. Deep Clone of LLMMessage Object

This method creates a deep copy of the LLMMessage object. It ensures that all internal states, including message histories and settings, are copied so that the original object remains unchanged when mutations are applied to the copy. This is particularly useful for maintaining immutability in a tidyverse-like functional programming context where functions should not have side effects on their inputs.

Method `clone_deep()`

Usage

LLMMessage$clone_deep()

Returns

A new LLMMessage object that is a deep copy of the original. Add a message

Adds a message to the history. Optionally includes media.

Method `add_message()`

Usage

LLMMessage$add_message(role, content, media = NULL, json = FALSE)

Arguments

role

The role of the message sender (e.g., "user", "assistant").

content

The textual content of the message.

media

Optional; media content to attach to the message.

json

Is the message a raw string that contains a json response? Convert to API format

Converts the message history to a format suitable for various API calls.

Method `to_api_format()`

Usage

LLMMessage$to_api_format(
  api_type,
  cgpt_image_detail = "auto",
  no_system = FALSE
)

Arguments

api_type: The type of API (e.g., "claude","groq","openai").
cgpt_image_detail: Specific option for ChatGPT API (imagedetail - set to auto)
no_system: Without system prompt (default: FALSE)

Returns

A message history in the target API format Simple helper function to determine whether the message history contains an image We check this function whenever we call models that do not support images so we can post a warning to the user that images were found but not sent to the model

Method `has_image()`

Usage

LLMMessage$has_image()

Returns

Returns TRUE if the message hisotry contains images Remove a Message by Index

Removes a message from the message history at the specified index.

Method `remove_message()`

Usage

LLMMessage$remove_message(index)

Arguments

index: A positive integer indicating the position of the message to remove.

Returns

The LLMMessage object, invisibly.

Method `print()`

Prints the current message history in a structured format.

Usage

LLMMessage$print()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LLMMessage$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Send LLMMessage to Mistral API

Description

Send LLMMessage to Mistral API

Usage

mistral(
  .llm,
  .model = "mistral-large-latest",
  .stream = FALSE,
  .seed = NULL,
  .json = FALSE,
  .temperature = 0.7,
  .top_p = 1,
  .stop = NULL,
  .safe_prompt = FALSE,
  .timeout = 120,
  .max_tries = 3,
  .max_tokens = 1024,
  .min_tokens = NULL,
  .dry_run = FALSE,
  .verbose = FALSE
)
mistral(
  .llm,
  .model = "mistral-large-latest",
  .stream = FALSE,
  .seed = NULL,
  .json = FALSE,
  .temperature = 0.7,
  .top_p = 1,
  .stop = NULL,
  .safe_prompt = FALSE,
  .timeout = 120,
  .max_tries = 3,
  .max_tokens = 1024,
  .min_tokens = NULL,
  .dry_run = FALSE,
  .verbose = FALSE
)

Arguments

`.llm`	An `LLMMessage` object.
`.model`	The model identifier to use (default: `"mistral-large-latest"`).
`.stream`	Whether to stream back partial progress to the console. (default: `FALSE`).
`.seed`	The seed to use for random sampling. If set, different calls will generate deterministic results (optional).
`.json`	Whether the output should be in JSON mode(default: `FALSE`).
`.temperature`	Sampling temperature to use, between `0.0` and `1.5`. Higher values make the output more random, while lower values make it more focused and deterministic (default: `0.7`).
`.top_p`	Nucleus sampling parameter, between `0.0` and `1.0`. The model considers tokens with top_p probability mass (default: `1`).
`.stop`	Stop generation if this token is detected, or if one of these tokens is detected when providing a list (optional).
`.safe_prompt`	Whether to inject a safety prompt before all conversations (default: `FALSE`).
`.timeout`	When should our connection time out in seconds (default: `120`).
`.max_tries`	Maximum retries to peform request
`.max_tokens`	The maximum number of tokens to generate in the completion. Must be `⁠>= 0⁠` (default: `1024`).
`.min_tokens`	The minimum number of tokens to generate in the completion. Must be `⁠>= 0⁠` (optional).
`.dry_run`	If `TRUE`, perform a dry run and return the request object (default: `FALSE`).
`.verbose`	Should additional information be shown after the API call? (default: `FALSE`)

Value

Returns an updated LLMMessage object.

Generate Embeddings Using Mistral API

Description

Generate Embeddings Using Mistral API

Usage

mistral_embedding(
  .llm,
  .model = "mistral-embed",
  .timeout = 120,
  .max_tries = 3,
  .dry_run = FALSE
)
mistral_embedding(
  .llm,
  .model = "mistral-embed",
  .timeout = 120,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

`.llm`	An existing LLMMessage object (or a character vector of texts to embed)
`.model`	The embedding model identifier (default: "mistral-embed").
`.timeout`	Timeout for the API request in seconds (default: 120).
`.max_tries`	Maximum retries to peform request
`.dry_run`	If TRUE, perform a dry run and return the request object.

Value

A matrix where each column corresponds to the embedding of a message in the message history.

Interact with local AI models via the Ollama API

Description

Interact with local AI models via the Ollama API

Usage

ollama(
  .llm,
  .model = "gemma2",
  .stream = FALSE,
  .seed = NULL,
  .json = FALSE,
  .temperature = NULL,
  .num_ctx = 2048,
  .num_predict = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .min_p = NULL,
  .mirostat = NULL,
  .mirostat_eta = NULL,
  .mirostat_tau = NULL,
  .repeat_last_n = NULL,
  .repeat_penalty = NULL,
  .tfs_z = NULL,
  .stop = NULL,
  .ollama_server = "http://localhost:11434",
  .timeout = 120,
  .keep_alive = NULL,
  .dry_run = FALSE
)
ollama(
  .llm,
  .model = "gemma2",
  .stream = FALSE,
  .seed = NULL,
  .json = FALSE,
  .temperature = NULL,
  .num_ctx = 2048,
  .num_predict = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .min_p = NULL,
  .mirostat = NULL,
  .mirostat_eta = NULL,
  .mirostat_tau = NULL,
  .repeat_last_n = NULL,
  .repeat_penalty = NULL,
  .tfs_z = NULL,
  .stop = NULL,
  .ollama_server = "http://localhost:11434",
  .timeout = 120,
  .keep_alive = NULL,
  .dry_run = FALSE
)

Arguments

`.llm`	An LLMMessage object containing the conversation history and system prompt.
`.model`	Character string specifying the Ollama model to use (default: "gemma2")
`.stream`	Logical; whether to stream the response (default: FALSE)
`.seed`	Integer; seed for reproducible generation (default: NULL)
`.json`	Logical; whether to format response as JSON (default: FALSE)
`.temperature`	Float between 0-2; controls randomness in responses (default: NULL)
`.num_ctx`	Integer; sets the context window size (default: 2048)
`.num_predict`	Integer; maximum number of tokens to predict (default: NULL)
`.top_k`	Integer; controls diversity by limiting top tokens considered (default: NULL)
`.top_p`	Float between 0-1; nucleus sampling threshold (default: NULL)
`.min_p`	Float between 0-1; minimum probability threshold (default: NULL)
`.mirostat`	Integer (0,1,2); enables Mirostat sampling algorithm (default: NULL)
`.mirostat_eta`	Float; Mirostat learning rate (default: NULL)
`.mirostat_tau`	Float; Mirostat target entropy (default: NULL)
`.repeat_last_n`	Integer; tokens to look back for repetition (default: NULL)
`.repeat_penalty`	Float; penalty for repeated tokens (default: NULL)
`.tfs_z`	Float; tail free sampling parameter (default: NULL)
`.stop`	Character; custom stop sequence(s) (default: NULL)
`.ollama_server`	String; Ollama API endpoint (default: "http://localhost:11434")
`.timeout`	Integer; API request timeout in seconds (default: 120)
`.keep_alive`	Character; How long should the ollama model be kept in memory after request (default: NULL - 5 Minutes)
`.dry_run`	Logical; if TRUE, returns request object without execution (default: FALSE)

Details

The function provides extensive control over the generation process through various parameters:

Temperature (0-2): Higher values increase creativity, lower values make responses more focused
Top-k/Top-p: Control diversity of generated text
Mirostat: Advanced sampling algorithm for maintaining consistent complexity
Repeat penalties: Prevent repetitive text
Context window: Control how much previous conversation is considered

Value

A new LLMMessage object containing the original messages plus the model's response

Examples

## Not run: 
llm_message("user", "Hello, how are you?")
response <- ollama(llm, .model = "gemma2", .temperature = 0.7)

# With custom parameters
response <- ollama(
  llm,
  .model = "llama2",
  .temperature = 0.8,
  .top_p = 0.9,
  .num_ctx = 4096
)

## End(Not run)

## Not run: 
llm_message("user", "Hello, how are you?")
response <- ollama(llm, .model = "gemma2", .temperature = 0.7)

# With custom parameters
response <- ollama(
  llm,
  .model = "llama2",
  .temperature = 0.8,
  .top_p = 0.9,
  .num_ctx = 4096
)

## End(Not run)

Download a model from the Ollama API

Description

This function sends a request to the Ollama API to download a specified model. It can operate in a streaming mode where it provides live updates of the download status and progress, or a single response mode.

Usage

ollama_download_model(.model, .ollama_server = "http://localhost:11434")
ollama_download_model(.model, .ollama_server = "http://localhost:11434")

Arguments

`.model`	The name of the model to download.
`.ollama_server`	The base URL of the Ollama API (default is "http://localhost:11434").

Generate Embeddings Using Ollama API

Description

Generate Embeddings Using Ollama API

Usage

ollama_embedding(
  .llm,
  .model = "all-minilm",
  .truncate = TRUE,
  .ollama_server = "http://localhost:11434",
  .timeout = 120,
  .dry_run = FALSE
)
ollama_embedding(
  .llm,
  .model = "all-minilm",
  .truncate = TRUE,
  .ollama_server = "http://localhost:11434",
  .timeout = 120,
  .dry_run = FALSE
)

Arguments

`.llm`	An existing LLMMessage object (or a charachter vector of texts to embed)
`.model`	The embedding model identifier (default: "all-minilm").
`.truncate`	Whether to truncate inputs to fit the model's context length (default: TRUE).
`.ollama_server`	The URL of the Ollama server to be used (default: "http://localhost:11434").
`.timeout`	Timeout for the API request in seconds (default: 120).
`.dry_run`	If TRUE, perform a dry run and return the request object.

Value

A matrix where each column corresponds to the embedding of a message in the message history.

Retrieve and return model information from the Ollama API

Description

This function connects to the Ollama API and retrieves information about available models, returning it as a tibble.

Usage

ollama_list_models(.ollama_server = "http://localhost:11434")
ollama_list_models(.ollama_server = "http://localhost:11434")

Arguments

.ollama_server

The URL of the ollama server to be used

Value

A tibble containing model information, or NULL if no models are found.

Send LLM Messages to the OpenAI Chat Completions API

Description

This function sends a message history to the OpenAI Chat Completions API and returns the assistant's reply.

Usage

openai(
  .llm,
  .model = "gpt-4o",
  .max_completion_tokens = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = NULL,
  .top_p = NULL,
  .api_url = "https://api.openai.com/",
  .timeout = 60,
  .verbose = FALSE,
  .json = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .dry_run = FALSE,
  .compatible = FALSE,
  .api_path = "/v1/chat/completions"
)
openai(
  .llm,
  .model = "gpt-4o",
  .max_completion_tokens = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = NULL,
  .top_p = NULL,
  .api_url = "https://api.openai.com/",
  .timeout = 60,
  .verbose = FALSE,
  .json = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .dry_run = FALSE,
  .compatible = FALSE,
  .api_path = "/v1/chat/completions"
)

Arguments

`.llm`	An `LLMMessage` object containing the conversation history.
`.model`	The identifier of the model to use (default: "gpt-4o").
`.max_completion_tokens`	An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
`.frequency_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far.
`.logit_bias`	A named list modifying the likelihood of specified tokens appearing in the completion.
`.logprobs`	Whether to return log probabilities of the output tokens (default: FALSE).
`.top_logprobs`	An integer between 0 and 20 specifying the number of most likely tokens to return at each token position.
`.presence_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.
`.seed`	If specified, the system will make a best effort to sample deterministically.
`.stop`	Up to 4 sequences where the API will stop generating further tokens.
`.stream`	If set to TRUE, the answer will be streamed to console as it comes (default: FALSE).
`.temperature`	What sampling temperature to use, between 0 and 2. Higher values make the output more random.
`.top_p`	An alternative to sampling with temperature, called nucleus sampling.
`.api_url`	Base URL for the API (default: "https://api.openai.com/").
`.timeout`	Request timeout in seconds (default: 60).
`.verbose`	Should additional information be shown after the API call (default: FALSE).
`.json`	Should output be in JSON mode (default: FALSE).
`.json_schema`	A JSON schema object as R list to enforce the output structure (If defined has precedence over JSON mode).
`.max_tries`	Maximum retries to perform request
`.dry_run`	If TRUE, perform a dry run and return the request object (default: FALSE).
`.compatible`	If TRUE, skip API and rate-limit checks for OpenAI compatible APIs (default: FALSE).
`.api_path`	The path relative to the base `.api_url` for the API (default: "/v1/chat/completions").

Value

A new LLMMessage object containing the original messages plus the assistant's response.

Generate Embeddings Using OpenAI API

Description

Generate Embeddings Using OpenAI API

Usage

openai_embedding(
  .llm,
  .model = "text-embedding-3-small",
  .truncate = TRUE,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)
openai_embedding(
  .llm,
  .model = "text-embedding-3-small",
  .truncate = TRUE,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

`.llm`	An existing LLMMessage object (or a character vector of texts to embed)
`.model`	The embedding model identifier (default: "text-embedding-3-small").
`.truncate`	Whether to truncate inputs to fit the model's context length (default: TRUE).
`.timeout`	Timeout for the API request in seconds (default: 120).
`.dry_run`	If TRUE, perform a dry run and return the request object.
`.max_tries`	Maximum retry attempts for requests (default: 3).

Value

A matrix where each column corresponds to the embedding of a message in the message history.

This internal function parses duration strings as returned by the OpenAI API

Description

This internal function parses duration strings as returned by the OpenAI API

Usage

parse_duration_to_seconds(.duration_str)
parse_duration_to_seconds(.duration_str)

Arguments

.duration_str

A duration string.

Value

A numeric number of seconds

Batch Process PDF into LLM Messages

Description

This function processes a PDF file page by page. For each page, it extracts the text and converts the page into an image. It creates a list of LLMMessage objects with the text and the image for multimodal processing. Users can specify a range of pages to process and provide a custom function to generate prompts for each page.

Usage

pdf_page_batch(
  .pdf,
  .general_prompt,
  .system_prompt = "You are a helpful assistant",
  .page_range = NULL,
  .prompt_fn = NULL
)
pdf_page_batch(
  .pdf,
  .general_prompt,
  .system_prompt = "You are a helpful assistant",
  .page_range = NULL,
  .prompt_fn = NULL
)

Arguments

`.pdf`	Path to the PDF file.
`.general_prompt`	A default prompt that is applied to each page if `.prompt_fn` is not provided.
`.system_prompt`	Optional system prompt to initialize the LLMMessage (default is "You are a helpful assistant").
`.page_range`	A vector of two integers specifying the start and end pages to process. If NULL, all pages are processed.
`.prompt_fn`	An optional custom function that generates a prompt for each page. The function takes the page text as input and returns a string. If NULL, `.general_prompt` is used for all pages.

Value

A list of LLMMessage objects, each containing the text and image for a page.

Perform an API request to interact with language models

Description

Perform an API request to interact with language models

Usage

perform_api_request(
  .request,
  .api,
  .stream = FALSE,
  .timeout = 60,
  .max_tries = 3,
  .parse_response_fn = NULL,
  .dry_run = FALSE
)
perform_api_request(
  .request,
  .api,
  .stream = FALSE,
  .timeout = 60,
  .max_tries = 3,
  .parse_response_fn = NULL,
  .dry_run = FALSE
)

Arguments

`.request`	The httr2 request object.
`.api`	The API identifier (e.g., "claude", "openai").
`.stream`	Stream the response if TRUE.
`.timeout`	Request timeout in seconds.
`.max_tries`	Maximum retry attempts for requests (default: 3).
`.parse_response_fn`	A function to parse the assistant's reply.
`.dry_run`	If TRUE, perform a dry run and return the request object.

Value

A list containing the assistant's reply and response headers.

Get the current rate limit information for all or a specific API

Description

This function retrieves the rate limit details for the specified API, or for all APIs stored in the .tidyllm_rate_limit_env if no API is specified.

Usage

rate_limit_info(.api_name = NULL)
rate_limit_info(.api_name = NULL)

Arguments

.api_name

(Optional) The name of the API whose rate limit info you want to get If not provided, the rate limit info for all APIs in the environment will be returned

Value

A tibble containing the rate limit information.

Extract rate limit information from API response headers

Description

Extract rate limit information from API response headers

Usage

ratelimit_from_header(.response_headers, .api)
ratelimit_from_header(.response_headers, .api)

Arguments

`.response_headers`	Headers from the API response
`.api`	The API type ("claude", "openai","groq")

Value

A list containing rate limit information

Send a Batch of Messages to Claude API

Description

This function creates and submits a batch of messages to the Claude API for asynchronous processing.

Usage

send_claude_batch(
  .llms,
  .model = "claude-3-5-sonnet-20241022",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .stop_sequences = NULL,
  .api_url = "https://api.anthropic.com/",
  .verbose = FALSE,
  .dry_run = FALSE,
  .overwrite = FALSE,
  .max_tries = 3,
  .timeout = 60,
  .id_prefix = "tidyllm_claude_req_"
)
send_claude_batch(
  .llms,
  .model = "claude-3-5-sonnet-20241022",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .stop_sequences = NULL,
  .api_url = "https://api.anthropic.com/",
  .verbose = FALSE,
  .dry_run = FALSE,
  .overwrite = FALSE,
  .max_tries = 3,
  .timeout = 60,
  .id_prefix = "tidyllm_claude_req_"
)

Arguments

`.llms`	A list of LLMMessage objects containing conversation histories.
`.model`	Character string specifying the Claude model version (default: "claude-3-5-sonnet-20241022").
`.max_tokens`	Integer specifying the maximum tokens per response (default: 1024).
`.temperature`	Numeric between 0 and 1 controlling response randomness.
`.top_k`	Integer for diversity by limiting the top K tokens.
`.top_p`	Numeric between 0 and 1 for nucleus sampling.
`.stop_sequences`	Character vector of sequences that halt response generation.
`.api_url`	Base URL for the Claude API (default: "https://api.anthropic.com/").
`.verbose`	Logical; if TRUE, prints a message with the batch ID (default: FALSE).
`.dry_run`	Logical; if TRUE, returns the prepared request object without executing it (default: FALSE).
`.overwrite`	Logical; if TRUE, allows overwriting an existing batch ID associated with the request (default: FALSE).
`.max_tries`	Maximum number of retries to perform the request.
`.timeout`	Integer specifying the request timeout in seconds (default: 60).
`.id_prefix`	Character string to specify a prefix for generating custom IDs when names in `.llms` are missing. Defaults to "tidyllm_claude_req_".

Value

An updated and named list of .llms with identifiers that align with batch responses, including a batch_id attribute.

Send a Batch of Messages to OpenAI Batch API

Description

This function creates and submits a batch of messages to the OpenAI Batch API for asynchronous processing.

Usage

send_openai_batch(
  .llms,
  .model = "gpt-4o",
  .max_completion_tokens = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .temperature = NULL,
  .top_p = NULL,
  .dry_run = FALSE,
  .overwrite = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .timeout = 60,
  .verbose = FALSE,
  .id_prefix = "tidyllm_openai_req_"
)
send_openai_batch(
  .llms,
  .model = "gpt-4o",
  .max_completion_tokens = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .temperature = NULL,
  .top_p = NULL,
  .dry_run = FALSE,
  .overwrite = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .timeout = 60,
  .verbose = FALSE,
  .id_prefix = "tidyllm_openai_req_"
)

Arguments

`.llms`	A list of LLMMessage objects containing conversation histories.
`.model`	Character string specifying the OpenAI model version (default: "gpt-4o").
`.max_completion_tokens`	Integer specifying the maximum tokens per response (default: NULL).
`.frequency_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far.
`.logit_bias`	A named list modifying the likelihood of specified tokens appearing in the completion.
`.logprobs`	Whether to return log probabilities of the output tokens (default: FALSE).
`.top_logprobs`	An integer between 0 and 20 specifying the number of most likely tokens to return at each token position.
`.presence_penalty`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.
`.seed`	If specified, the system will make a best effort to sample deterministically.
`.stop`	Up to 4 sequences where the API will stop generating further tokens.
`.temperature`	What sampling temperature to use, between 0 and 2. Higher values make the output more random.
`.top_p`	An alternative to sampling with temperature, called nucleus sampling.
`.dry_run`	Logical; if TRUE, returns the prepared request object without executing it (default: FALSE).
`.overwrite`	Logical; if TRUE, allows overwriting an existing batch ID associated with the request (default: FALSE).
`.json_schema`	A JSON schema object as R list to enforce the output structure (default: NULL).
`.max_tries`	Maximum number of retries to perform the request (default: 3).
`.timeout`	Integer specifying the request timeout in seconds (default: 60).
`.verbose`	Logical; if TRUE, additional info about the requests is printed (default: FALSE).
`.id_prefix`	Character string to specify a prefix for generating custom IDs when names in `.llms` are missing (default: "tidyllm_openai_req_").

Value

An updated and named list of .llms with identifiers that align with batch responses, including a batch_id attribute.

Create a JSON schema for structured outputs

Description

This function creates a JSON schema suitable for use with the API functions in tidyllm.

Usage

tidyllm_schema(name, ...)
tidyllm_schema(name, ...)

Arguments

name

A character vector specifying the schema name. This serves as an identifier for the schema.

...

Named arguments where each name represents a field in the schema and each value specifies the type. Supported types include R data types:

"character": Represents a charcater type
"string": Allowed shorthand for charachter type
"factor(...)": A string with specific allowable values, represented as enum in JSON. Specify options as factor(option1, option2).
"logical": Represents a boolean.
"numeric": Represents a number.
"type[]": Appending [] allows for vector of a given type, e.g., "character[]".

Details

The tidyllm_schema() function is designed to make defining JSON schemas for tidyllm more concise and user-friendly. It maps R-like types to JSON schema types and validates inputs to enforce tidy data principles. Nested structures are not allowed to maintain compatibility with tidy data conventions.

Value

A list representing the JSON schema with the specified fields and types, suitable for passing to openai()'s .json_schema parameter.

Note

Factor types (factor(...)) are treated as enumerations in JSON and are limited to a set of allowable string values. Arrays of a given type can be specified by appending [] to the type.

Examples

## Not run: 
# Define a schema with tidy data principles
json_schema <- tidyllm_schema(
  name = "DocumentAnalysisSchema",
  Title = "character",
  Authors = "character[]",
  SuggestedFilename = "character",
  Type = "factor(Policy, Research)",
  Answer_Q1 = "character",
  Answer_Q2 = "character",
  Answer_Q3 = "character",
  Answer_Q4 = "character",
  KeyCitations = "character[]"
)

# Pass the schema to openai()
result <- openai(
  .llm = msg,
  .json_schema = json_schema
)

## End(Not run)
## Not run: 
# Define a schema with tidy data principles
json_schema <- tidyllm_schema(
  name = "DocumentAnalysisSchema",
  Title = "character",
  Authors = "character[]",
  SuggestedFilename = "character",
  Type = "factor(Policy, Research)",
  Answer_Q1 = "character",
  Answer_Q2 = "character",
  Answer_Q3 = "character",
  Answer_Q4 = "character",
  KeyCitations = "character[]"
)

# Pass the schema to openai()
result <- openai(
  .llm = msg,
  .json_schema = json_schema
)

## End(Not run)

Update the standard API rate limit info in the hidden .tidyllm_rate_limit_env environment

Description

This function initializes stores ratelimit information from API functions for future use

Usage

update_rate_limit(.api_name, .response_object)
update_rate_limit(.api_name, .response_object)

Arguments

`.api_name`	The name of the API for which to initialize or retrieve the environment.
`.response_object`	A preparsed response object cotaining info on remaining requests, tokens and rest times

Package 'tidyllm'

Help Index

Send LLM Messages to an OpenAI Chat Completions endpoint on Azure

Description

Usage

Arguments

Value

Examples

ChatGPT Wrapper (Deprecated)

Description

Usage

Arguments

Details

Value

See Also

Examples

Check Batch Processing Status for Claude API

Description

Usage

Arguments

Value

Check Batch Processing Status for OpenAI Batch API

Description

Usage

Arguments

Value

Interact with Claude AI models via the Anthropic API

Description

Usage

Arguments

Value

Examples

Convert a Data Frame to an LLMMessage Object

Description

Usage

Arguments

Value

Fetch Results for a Claude Batch

Description

Usage

Arguments

Value

Fetch Results for an OpenAI Batch

Description

Usage

Arguments

Value

Generate API-Specific Callback Function for Streaming Responses

Description

Usage

Arguments

Details

Value

Get Assistant Reply as Text

Description

Usage

Arguments

Value

Get Data from an Assistant Reply by parsing structured JSON responses

Description

Usage

Arguments

Value

Retrieve a User Message by Index

Description

Usage

Arguments

Value

Send LLM Messages to the Groq Chat API

Description

Usage

Arguments

Value

Examples

Transcribe an Audio File Using Groq transcription API

Description

Usage

Arguments

Value

Examples

Method `new()`

Method `clone_deep()`

Method `add_message()`

Method `to_api_format()`

Method `has_image()`

Method `remove_message()`

Method `print()`

Method `clone()`