Package 'tidyllm'

Description

Stores a reference to a local audio file for use in multimodal messages. The file is not encoded until the message is formatted for a provider.

Usage

audio_file(.path)

Arguments

Value

A tidyllm_audio object.

Description

The azure_openai() function acts as an interface for interacting with the Azure OpenAI API through main tidyllm verbs.

Usage

azure_openai(..., .called_from = NULL)

Arguments

Details

azure_openai() currently routes messages only to azure_openai_chat() when used with chat().

send_batch(). It dynamically routes requests to OpenAI-specific functions like azure_openai_chat() and azure_openai_embedding() based on the context of the call.

Value

The result of the requested action, depending on the specific function invoked (currently, only an updated LLMMessage object for azure_openai_chat()).

Description

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

Usage

azure_openai_chat(
  .llm,
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .deployment = "gpt-4o-mini",
  .api_version = "2024-08-01-preview",
  .max_completion_tokens = NULL,
  .reasoning_effort = NULL,
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = NULL,
  .top_p = NULL,
  .timeout = 60,
  .verbose = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .dry_run = FALSE,
  .logprobs = NULL,
  .top_logprobs = NULL,
  .tools = NULL,
  .tool_choice = NULL,
  .max_tool_rounds = 10
)

Arguments

Value

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

Description

Generate Embeddings Using OpenAI API on Azure

Usage

azure_openai_embedding(
  .input,
  .deployment = "text-embedding-3-small",
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .api_version = "2023-05-15",
  .truncate = TRUE,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

Value

A tibble with two columns: input and embeddings. The input column contains the texts sent to embed, and the embeddings column is a list column where each row contains an embedding vector of the sent input.

Description

This function cancels an in-progress batch created through the OpenAI API. The batch will be moved to a "cancelling" state and, eventually, "cancelled."

Usage

cancel_openai_batch(.batch_id, .dry_run = FALSE, .max_tries = 3, .timeout = 60)

Arguments

Value

A list containing the response from the OpenAI API about the cancellation status.

Description

The chat() function sends a message to a language model via a specified provider and returns the response. It routes the provided LLMMessage object to the appropriate provider-specific chat function, while allowing for the specification of common arguments applicable across different providers.

Usage

chat(
  .llm,
  .provider = getOption("tidyllm_chat_default"),
  .dry_run = NULL,
  .stream = NULL,
  .temperature = NULL,
  .timeout = NULL,
  .top_p = NULL,
  .max_tries = NULL,
  .model = NULL,
  .verbose = NULL,
  .json_schema = NULL,
  .tools = NULL,
  .max_tool_rounds = NULL,
  .seed = NULL,
  .stop = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL
)

Arguments

Details

The chat() function provides a unified interface for interacting with different language model providers. Common arguments such as .temperature, .model, and .stream are supported by most providers and can be passed directly to chat(). If a provider does not support a particular argument, an error will be raised.

Advanced provider-specific configurations can be accessed via the provider functions.

Value

An updated LLMMessage object containing the response from the language model.

Examples

## Not run: 
# Basic usage with OpenAI provider
llm_message("Hello World") |>
   chat(ollama(.ollama_server = "https://my-ollama-server.de"),.model="mixtral")
   
   chat(mistral,.model="mixtral")

# Use streaming with Claude provider
llm_message("Tell me a story") |>
   chat(claude(),.stream=TRUE)

## End(Not run)

Description

Provider function for interacting with any server that implements the OpenAI Chat Completions wire format (vLLM, LiteLLM, Together, Anyscale, etc.). Supports the chat() verb only.

Usage

chat_completions_chat(
  .llm,
  .api_url,
  .api_key_env_var = NULL,
  .model = "default",
  ...
)

chat_completions(..., .called_from = NULL)

Arguments

Value

An updated LLMMessage object with the assistant's response appended.

Description

This function converts an LLMMessage to the turns ellmer chat object. The ellmer object is cloned and its turn history is cleared internatlly. This maintains tidyllms stateless style.

Usage

chat_ellmer(
  .llm,
  .ellmer_chat,
  .stream = FALSE,
  .timeout = 60,
  .verbose = FALSE,
  .dry_run = FALSE,
  .tools = NULL,
  .max_tries = 3
)

Arguments

Value

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

Description

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

Usage

check_azure_openai_batch(
  .llms = NULL,
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .batch_id = NULL,
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)

Arguments

Value

A tibble with information about the status of batch processing.

Description

This function retrieves the processing status and other details of a specified batchid or a list of LLMMessage objects with batch attribute. It routes the input to the appropriate provider-specific batch API function.

Usage

check_batch(
  .llms,
  .provider = getOption("tidyllm_cbatch_default"),
  .dry_run = NULL,
  .max_tries = NULL,
  .timeout = NULL
)

Arguments

Value

A tibble with information about the status of batch processing.

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
)

Arguments

Value

A tibble with information about the status of batch processing

Description

Retrieves processing status and metadata for a Gemini batch operation.

Usage

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

Arguments

Details

You can supply either the .batch_id string (e.g. "batches/xyz...") or a list of LLMMessage objects (.llms) with a "batch_id" attribute as returned by send_gemini_batch().

Value

A tibble with the operation's metadata, including name, state, creation time, completion time, and done status.

Description

This function retrieves the processing status and other details of a specified Groq batch.

Usage

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

Arguments

Value

A tibble with information about the status of batch processing.

Description

check_job() dispatches to check_batch() for batch objects or perplexity_check_research() / openai_check_research() for tidyllm_research_job objects.

Usage

check_job(.job, ...)

Arguments

Value

Status information; type depends on .job class.

Description

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

Usage

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

Arguments

Value

A tibble with information about the status of batch processing.

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
)

Arguments

Value

A tibble with information about the status of batch processing.

Description

The claude() function acts as an interface for interacting with the Anthropic API through main tidyllm verbs such as chat(), embed(), and send_batch(). It dynamically routes requests to Claude-specific functions like claude_chat() and send_claude_batch() based on the context of the call.

Usage

claude(..., .called_from = NULL)

Arguments

Value

The result of the requested action, depending on the specific function invoked (e.g., an updated LLMMessage object for chat(), or a matrix for embed()).

Description

Interact with Claude AI models via the Anthropic API

Usage

claude_chat(
  .llm,
  .model = "claude-sonnet-4-6",
  .max_tokens = 2048,
  .temperature = NULL,
  .top_k = NULL,
  .top_p = NULL,
  .metadata = NULL,
  .stop_sequences = NULL,
  .tools = NULL,
  .json_schema = NULL,
  .file_ids = NULL,
  .api_url = "https://api.anthropic.com/",
  .verbose = FALSE,
  .max_tries = 3,
  .timeout = 60,
  .stream = FALSE,
  .dry_run = FALSE,
  .thinking = FALSE,
  .thinking_budget = 1024,
  .max_tool_rounds = 10
)

Arguments

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_chat(msg)

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

## End(Not run)

Description

Deletes a specific file from the Claude API using its file ID.

Usage

claude_delete_file(
  .file_id,
  .api_url = "https://api.anthropic.com/",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

Invisibly returns NULL. Prints a confirmation message upon successful deletion.

Description

Retrieves metadata for a specific file uploaded to the Claude API.

Usage

claude_file_metadata(
  .file_id,
  .api_url = "https://api.anthropic.com/",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

A tibble containing metadata fields such as file_id, filename, size, and MIME type.

Description

Lists metadata for files uploaded to the Claude API, supporting pagination.

Usage

claude_list_files(
  .limit = 20,
  .order = "desc",
  .api_url = "https://api.anthropic.com/",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

A tibble containing metadata for each file, including file_id, filename, size, and MIME type.

Description

List Available Models from the Anthropic Claude API

Usage

claude_list_models(
  .api_url = "https://api.anthropic.com",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE,
  .verbose = FALSE
)

Arguments

Value

A tibble containing model information (columns include type,id, display_name, and created_at), or NULL if no models are found.

Description

Uploads a file to the Claude API and returns its metadata as a tibble.

Usage

claude_upload_file(
  .file_path,
  .api_url = "https://api.anthropic.com/",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

A tibble containing metadata about the uploaded file, including its file_id, name, and size.

Description

Returns a TOOL object for Claude's builtin web_search tool.

Usage

claude_websearch()

Description

The deep_research() function sends a message to a provider's deep research endpoint. Currently supported: Perplexity (sonar-deep-research via async API).

Usage

deep_research(.llm, .provider, .background = FALSE, ...)

Arguments

Value

If .background = FALSE, an LLMMessage with the research reply. If .background = TRUE, a tidyllm_research_job for use with check_job()/fetch_job().

Description

The deepseek() function acts as a provider interface for interacting with the Deepseek API through tidyllm's chat() verb. It dynamically routes requests to deepseek-specific function. At the moment this is only deepseek_chat()

Usage

deepseek(..., .called_from = NULL)

Arguments

Value

The result of the requested action, depending on the specific function invoked (e.g., an updated LLMMessage object for chat()).

Description

This function sends a message history to the DeepSeek Chat API and returns the assistant's reply. Currently tool calls cause problems on the DeepSeek API

Usage

deepseek_chat(
  .llm,
  .model = "deepseek-v4-pro",
  .thinking = NULL,
  .max_tokens = 2048,
  .temperature = NULL,
  .top_p = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .stop = NULL,
  .stream = FALSE,
  .logprobs = NULL,
  .top_logprobs = NULL,
  .tools = NULL,
  .tool_choice = NULL,
  .api_url = "https://api.deepseek.com/",
  .timeout = 60,
  .verbose = FALSE,
  .dry_run = FALSE,
  .max_tries = 3,
  .max_tool_rounds = 10
)

Arguments

Value

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

Description

Delete a File from a Provider's File Store

Usage

delete_file(.provider, .file_id, ...)

Arguments

Value

Invisibly NULL; prints a confirmation message.

Description

This function converts a data frame into an LLMMessage object representing a conversation history. The data frame must have specific columns (role and content), with each row representing a message.

Usage

df_llm_message(.df)

Arguments

Value

An LLMMessage object representing the structured conversation.

See Also

llm_message()

Other Message Creation Utilities: llm_message()

Description

The chat_ellmer() function acts as a provider interface for interacting with ellmer chat objects through tidyllm's verb interface

Usage

ellmer(..., .called_from = NULL)

Arguments

Details

This function allows you to use any ellmer chat object (e.g., from ellmer::chat_anthropic(), ellmer::chat_openai(), etc.) as a stateless backend for tidyllm. The ellmer object is cloned for each interaction to maintain tidyllm's stateless approach.

Value

A provider function that can be used with tidyllm::chat().

Description

Converts an ellmer ToolDef or ToolBuiltIn object to a tidyllm TOOL object, allowing seamless integration of ellmer-defined tools and builtin provider tools with tidyllm workflows.

Usage

ellmer_tool(.ellmer_tool)

Arguments

Details

This function supports two types of ellmer tools:

Custom ToolDef objects: Extracts the function, description, and argument schemas from an ellmer tool and converts them to tidyllm's internal representation. Ellmer type objects are automatically converted to tidyllm field descriptors.

Builtin ToolBuiltIn objects: Converts provider-native builtin tools (like Claude's web search) to tidyllm format. For builtin tools, the tool definition is passed through as-is to the provider's API, which handles the tool execution natively.

Value

A TOOL class object that can be used with tidyllm chat() functions

Examples

## Not run: 
library(ellmer)

# Example 1: Custom tool
tool_rnorm <- ellmer::tool(
  rnorm,
  description = "Draw numbers from a random normal distribution",
  arguments = list(
    n = ellmer::type_integer("The number of observations"),
    mean = ellmer::type_number("The mean value"),
    sd = ellmer::type_number("The standard deviation")
  )
)

tidyllm_tool_rnorm <- ellmer_tool(tool_rnorm)

llm_message("Generate 100 random numbers") |> 
  chat(openai(), .tools = tidyllm_tool_rnorm)

# Example 2: Builtin tool
web_search <- ellmer_tool(ellmer::claude_tool_web_search())
llm_message("What are the latest AI developments?") |>
  chat(claude(), .tools = web_search)

## End(Not run)

Description

The embed() function allows you to embed a text via a specified provider. It routes the input to the appropriate provider-specific embedding function.

Usage

embed(
  .input,
  .provider = getOption("tidyllm_embed_default"),
  .model = NULL,
  .truncate = NULL,
  .timeout = NULL,
  .dry_run = NULL,
  .max_tries = NULL
)

Arguments

Value

A tibble with two columns: input and embeddings. The input column contains the texts sent to embed, and the embeddings column is a list column where each row contains an embedding vector of the sent input.

Examples

## Not run: 
c("What is the meaning of life, the universe and everything?",
 "How much wood would a woodchuck chuck?",
 "How does the brain work?") |>
 embed(gemini)
 
## End(Not run)

Description

This function retrieves the results of a completed Azure 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_azure_openai_batch().

Usage

fetch_azure_openai_batch(
  .llms,
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .batch_id = NULL,
  .dry_run = FALSE,
  .max_tries = 3,
  .timeout = 60
)

Arguments

Value

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

Description

This function retrieves the results of a completed 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_batch().

Usage

fetch_batch(
  .llms,
  .provider = getOption("tidyllm_fbatch_default"),
  .dry_run = NULL,
  .max_tries = NULL,
  .timeout = NULL
)

Arguments

Details

The function routes the input to the appropriate provider-specific batch API function.

Value

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

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
)

Arguments

Value

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

Description

Retrieves the results of a completed Gemini batch and updates the provided list of LLMMessage objects with the assistant's responses, matching by original list order.

Usage

fetch_gemini_batch(
  .llms,
  .batch_name = NULL,
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

A list of updated LLMMessage objects with the assistant response appended to each, in the same order.

Description

This function retrieves the results of a completed Groq batch and updates the provided list of LLMMessage objects with the responses.

Usage

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

Arguments

Value

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

Description

fetch_job() dispatches to fetch_batch() for batch objects or perplexity_fetch_research() / openai_fetch_research() for tidyllm_research_job objects.

Usage

fetch_job(.job, .provider = NULL, ...)

Arguments

Value

Fetched results; type depends on .job class.

Description

This function retrieves the results of a completed Mistral 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_mistral_batch().

Usage

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

Arguments

Value

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

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
)

Arguments

Value

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

Description

These functions create field descriptors used in tidyllm_schema() or field_object() to define JSON schema fields. They support character, factor, numeric, and logical types.

Usage

field_chr(.description = character(0), .vector = FALSE)

field_fct(.description = character(0), .levels, .vector = FALSE)

field_dbl(.description = character(0), .vector = FALSE)

field_lgl(.description = character(0), .vector = FALSE)

Arguments

Value

An S7 tidyllm_field object representing the field descriptor.

Examples

field_chr("A common street name")
field_fct("State abbreviation", .levels = c("CA", "TX", "Other"))
field_dbl("House number")
field_lgl("Is residential")
field_dbl("A list of appartment numbers at the address",.vector=TRUE )

Description

Define a nested object field

Usage

field_object(.description = character(0), ..., .vector = FALSE)

Arguments

Value

An S7 tidyllm_field object of type "object" containing nested fields.

Examples

# Define an address object with nested fields
address <- field_object("A mailing address",
  street = field_chr("Street name"),
  city = field_chr("City name"),
  zipcode = field_chr("Postal code")
)

# Create a vector of objects
addresses <- field_object("List of addresses", 
  street = field_chr("Street name"),
  city = field_chr("City name"),
  .vector = TRUE
)

Description

Get Metadata for a File Stored on a Provider

Usage

file_info(.provider, .file_id, ...)

Arguments

Value

A single-row tibble of file metadata.

Description

The gemini() function acts as a provider interface for interacting with the Google Gemini API through tidyllm's main verbs such as chat() and embed(). It dynamically routes requests to Gemini-specific functions like gemini_chat() and gemini_embedding() based on the context of the call.

Usage

gemini(..., .called_from = NULL)

Arguments

Details

Some functions, such as gemini_upload_file() and gemini_delete_file(), are specific to Gemini and do not have general verb counterparts.

Value

The result of the requested action, depending on the specific function invoked (e.g., an updated LLMMessage object for chat()).

Description

Send LLMMessage to Gemini API

Usage

gemini_chat(
  .llm,
  .model = "gemini-2.5-flash",
  .fileid = NULL,
  .temperature = NULL,
  .max_output_tokens = NULL,
  .top_p = NULL,
  .top_k = NULL,
  .grounding_threshold = NULL,
  .presence_penalty = NULL,
  .frequency_penalty = NULL,
  .stop_sequences = NULL,
  .safety_settings = NULL,
  .json_schema = NULL,
  .tools = NULL,
  .thinking_budget = NULL,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3,
  .verbose = FALSE,
  .stream = FALSE,
  .max_tool_rounds = 10
)

Arguments

Value

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

Description

Deletes a specific file from the Gemini API using its file ID.

Usage

gemini_delete_file(.file_name)

Arguments

Value

Invisibly returns NULL. Prints a confirmation message upon successful deletion.

Description

Generate Embeddings Using the Google Gemini API

Usage

gemini_embedding(
  .input,
  .model = "gemini-embedding-2-preview",
  .truncate = TRUE,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

Value

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

Description

Retrieves metadata for a specific file uploaded to the Gemini API.

Usage

gemini_file_metadata(.file_name)

Arguments

Value

A tibble containing metadata fields such as name, display name, MIME type, size, and URI.

Description

Lists metadata for files uploaded to the Gemini API, supporting pagination.

Usage

gemini_list_files(.page_size = 10, .page_token = NULL)

Arguments

Value

A tibble containing metadata for each file, including fields such as name, display name, MIME type, and URI.

Description

List Available Models from the Google Gemini API

Usage

gemini_list_models(.timeout = 60, .max_tries = 3, .dry_run = FALSE)

Arguments

Value

A tibble containing model information with columns including name, base_model_id, version, display_name, description, input_token_limit, output_token_limit, supported_generation_methods, thinking, temperature, max_temperature, top_p, and top_k, or NULL if no models are found.

Description

Uploads a file to the Gemini API and returns its metadata as a tibble.

Usage

gemini_upload_file(.file_path)

Arguments

Value

A tibble containing metadata about the uploaded file, including its name, URI, and MIME type.

Description

Extracts token log probabilities from assistant replies within an LLMMessage object. Each row represents a token with its log probability and top alternative tokens.

Usage

get_logprobs(.llm, .index = NULL)

Arguments

Details

An empty tibble is output if no logprobs were requested. Works with openai_chat(), llamacpp_chat(), and other providers that support logprobs.

Columns include:

• reply_index: The index of the assistant reply in the message history.

• token: The generated token.

• logprob: The log probability of the generated token.

• bytes: The byte-level encoding of the token.

• top_logprobs: A list column containing the top alternative tokens with their log probabilities.

Value

A tibble containing log probabilities for the specified assistant reply or all replies.

See Also

get_metadata()

Description

Retrieves metadata from assistant replies within an LLMMessage object. It returns the metadata as a tibble.

Usage

get_metadata(.llm, .index = NULL)

last_metadata(.llm)

Arguments

Details

Metadata columns may include:

• model: The model used for generating the reply.

• timestamp: The time when the reply was generated.

• prompt_tokens: The number of tokens in the input prompt.

• completion_tokens: The number of tokens in the assistant's reply.

• total_tokens: The total number of tokens (prompt + completion).

• api_specific: A list column with API-specific metadata.

For convenience, last_metadata() is provided to retrieve the metadata for the last message.

Value

A tibble containing metadata for the specified assistant reply or all replies.

See Also

last_metadata()

Description

Extracts the plain text content of the assistant's reply from an LLMMessage object. Use get_reply_data() for structured replies in JSON format.

Usage

get_reply(.llm, .index = NULL)

last_reply(.llm)

Arguments

Details

This function is the core utility for retrieving assistant replies by index. For convenience, last_reply() is provided as a wrapper to retrieve the latest assistant reply.

Value

Returns a character string containing the assistant's reply, or NA_character_ if no reply exists.

See Also

get_reply_data(), last_reply()

Description

Parses the assistant's reply as JSON and returns the corresponding structured data. If the reply is not marked as JSON, attempts to extract and parse JSON content from the text.

Usage

get_reply_data(.llm, .index = NULL)

last_reply_data(.llm)

Arguments

Details

For convenience, last_reply_data() is provided as a wrapper to retrieve the latest assistant reply's data.

Value

Returns the parsed data from the assistant's reply, or NULL if parsing fails.

See Also

get_reply(), last_reply_data()

Description

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

Usage

get_user_message(.llm, .index = NULL)

last_user_message(.llm)

Arguments

Details

For convenience, last_user_message() is provided as a wrapper to retrieve the latest user message without specifying an index.

Value

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

See Also

last_user_message()

Description

The groq() function acts as an interface for interacting with the Groq API through tidyllm's main verbs. Currently, Groq only supports groq_chat() for chat-based interactions and groq_transcribe() for transcription tasks.

Usage

groq(..., .called_from = NULL)

Arguments

Details

Since groq_transcribe() is unique to Groq and does not have a general verb counterpart, groq() currently routes messages only to groq_chat() when used with verbs like chat().

Value

The result of the requested action, depending on the specific function invoked (currently, only an updated LLMMessage object for groq_chat()).

Description

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

Usage

groq_chat(
  .llm,
  .model = "openai/gpt-oss-120b",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .stop = NULL,
  .seed = NULL,
  .tools = NULL,
  .tool_choice = NULL,
  .api_url = "https://api.groq.com/",
  .json_schema = NULL,
  .timeout = 60,
  .verbose = FALSE,
  .stream = FALSE,
  .dry_run = FALSE,
  .max_tries = 3,
  .max_tool_rounds = 10
)

Arguments

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_chat(msg)

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

## End(Not run)

Description

List Available Models from the Groq API

Usage

groq_list_models(
  .api_url = "https://api.groq.com",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE,
  .verbose = FALSE
)

Arguments

Value

A tibble containing model information (columns include id, created, owned_by, and context_window), or NULL if no models are found.

Description

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

Usage

groq_transcribe(
  .audio_file,
  .model = "playai-tts",
  .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

Value

A character vector containing the transcription.

Examples

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

## End(Not run)

Description

This function reads an image file from disk, encodes it in base64, and returns a tidyllm_image object that can be used in multimodal embedding requests or attached to messages via .media.

Usage

img(.path)

Arguments

Value

A tidyllm_image object.

Description

Retrieves batch request details from the Azure OpenAI Batch API.

Usage

list_azure_openai_batches(
  .endpoint_url = Sys.getenv("AZURE_ENDPOINT_URL"),
  .limit = 20,
  .max_tries = 3,
  .timeout = 60
)

Arguments

Value

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

Description

List all Batch Requests on a Batch API

Usage

list_batches(.provider = getOption("tidyllm_lbatch_default"))

Arguments

Value

A tibble with information about the status of batch processing.

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
)

Arguments

Value

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

Description

List Files Stored on a Provider

Usage

list_files(.provider, ...)

Arguments

Value

A tibble of file metadata.

Description

Returns a tibble with recent Gemini batch operations and their metadata.

Usage

list_gemini_batches(
  .filter = NULL,
  .page_size = 20,
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

A tibble with columns: name, state, done, create_time, complete_time.

Description

Retrieves batch request details from the Groq API.

Usage

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

Arguments

Value

A tibble with batch details including batch ID, status, creation time, and request counts.

Description

Calls the Hugging Face Hub API to list all .gguf files in a repository, along with their sizes. Useful for choosing a quantization level before calling llamacpp_download_model().

Usage

list_hf_gguf_files(.repo, .timeout = 30)

Arguments

Value

A tibble with columns filename, size_gb, and url.

Description

Retrieves batch request details from the OpenAI Batch API.

Usage

list_mistral_batches(
  .limit = 100,
  .max_tries = 3,
  .timeout = 60,
  .status = NULL,
  .created_after = NULL
)

Arguments

Value

A tibble with batch details for all batches fitting the request

Description

The list_models() function retrieves available models from the specified provider.

Usage

list_models(.provider = getOption("tidyllm_lmodels_default"), ...)

Arguments

Value

A tibble containing model information.

Description

Retrieves batch request details from the OpenAI Batch API.

Usage

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

Arguments

Value

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

Description

The llamacpp() provider connects tidyllm to a locally running llama.cpp server. It exposes the same verb/provider pattern as every other tidyllm provider while also offering llama.cpp-specific features: BNF grammar constraints (.grammar), token logprobs (.logprobs), and model management helpers.

The server must be started separately before calling llamacpp(). See llamacpp_health() to verify the server is running, and llamacpp_download_model() / list_hf_gguf_files() to obtain models.

Usage

llamacpp(..., .called_from = NULL)

Arguments

Value

The result of the requested action (e.g., an updated LLMMessage for chat(), a tibble for embed() or list_models()).

Description

Sends a message history to a local llama.cpp server using the OpenAI-compatible Chat Completions API. Supports BNF grammar constraints (a llama.cpp-specific feature that enforces output format at the token sampling level) and token logprobs for uncertainty quantification.

Usage

llamacpp_chat(
  .llm,
  .model = "local-model",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .stop = NULL,
  .stream = FALSE,
  .tools = NULL,
  .tool_choice = NULL,
  .json_schema = NULL,
  .grammar = NULL,
  .logprobs = FALSE,
  .top_logprobs = NULL,
  .seed = NULL,
  .thinking = NULL,
  .thinking_budget = NULL,
  .server = Sys.getenv("LLAMACPP_SERVER", "http://localhost:8080"),
  .api_key = Sys.getenv("LLAMACPP_API_KEY", ""),
  .timeout = 120,
  .verbose = FALSE,
  .dry_run = FALSE,
  .max_tries = 3,
  .max_tool_rounds = 10
)

Arguments

Value

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

Description

Deletes a GGUF file from disk. Issues a warning confirming the deletion. Mirrors the ollama_delete_model() pattern.

Usage

llamacpp_delete_model(.path)

Arguments

Value

Invisibly returns TRUE on success.

Description

Downloads a single GGUF file from a Hugging Face repository with a streaming progress bar. Use list_hf_gguf_files() first to browse available quantizations.

Usage

llamacpp_download_model(
  .repo,
  .filename,
  .dir = Sys.getenv("LLAMACPP_MODEL_DIR", "~/models"),
  .timeout = 3600
)

Arguments

Value

Invisibly returns the full path of the downloaded file.

Description

Sends text to the ⁠/v1/embeddings⁠ endpoint of a running llama.cpp server and returns embedding vectors.

Usage

llamacpp_embedding(
  .input,
  .model = "local-model",
  .server = Sys.getenv("LLAMACPP_SERVER", "http://localhost:8080"),
  .api_key = Sys.getenv("LLAMACPP_API_KEY", ""),
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

Value

A tibble with columns input (text) and embeddings (list of numeric vectors).

Description

Calls the ⁠/health⁠ endpoint of a running llama.cpp server. Returns the status string ("ok", "loading model", "no model loaded", or "error") along with the full parsed response body as a named list.

Usage

llamacpp_health(
  .server = Sys.getenv("LLAMACPP_SERVER", "http://localhost:8080"),
  .timeout = 10
)

Arguments

Value

A named list with at least a status element.

Description

Scans a directory for .gguf files and returns a tibble with their names, sizes, and modification times. No server needed.

Usage

llamacpp_list_local_models(
  .path = Sys.getenv("LLAMACPP_MODEL_DIR", "~/models")
)

Arguments

Value

A tibble with columns filename, size_gb, modified, and path.

Description

Calls the ⁠/v1/models⁠ endpoint of a running llama.cpp server and returns the currently loaded model(s) as a tibble. In normal operation this is one row; two rows appear when speculative decoding is active (main + draft model).

Usage

llamacpp_list_models(
  .server = Sys.getenv("LLAMACPP_SERVER", "http://localhost:8080"),
  .api_key = Sys.getenv("LLAMACPP_API_KEY", ""),
  .timeout = 30,
  .max_tries = 3
)

Arguments

Value

A tibble with columns id, object, and created.

Description

Calls the ⁠/v1/reranking⁠ endpoint of a running llama.cpp server to score documents by relevance to a query. Useful for building fully-local RAG pipelines (embed → cosine search → rerank → chat, all with llamacpp()).

Usage

llamacpp_rerank(
  .query,
  .documents,
  .model = "local-model",
  .server = Sys.getenv("LLAMACPP_SERVER", "http://localhost:8080"),
  .api_key = Sys.getenv("LLAMACPP_API_KEY", ""),
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE
)

Arguments

Value

A tibble with columns index (original position), document (text), and relevance_score, sorted by descending score.

Description

This function creates a new LLMMessage object or updates an existing one. It supports adding text prompts and various media types, such as images, PDFs, text files, or plots.

Usage

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

Arguments

Value

Returns an updated or new LLMMessage object.

See Also

df_llm_message()

Other Message Creation Utilities: df_llm_message()

Description

LLMMessage is an S7 class for managing a conversation history intended for use with large language models (LLMs). Please use llm_message()to create or modify LLMMessage objects.

Usage

LLMMessage(message_history = list(), system_prompt = character(0))

Arguments

Details

The LLMMessage class includes the following features:

• Stores message history in a structured format.

• Supports attaching media and metadata to messages.

• Provides generics like add_message(), has_image(), and remove_message() for interaction.

• Enables API-specific formatting through the to_api_format() generic.

• message_history: A list containing messages. Each message is a named list with keys like role, content, media, etc.

• system_prompt: A character string representing the default system prompt used for the conversation.

Description

The mistral() function acts as an interface for interacting with the Mistral API through main tidyllm verbs such as chat() and embed(). It dynamically routes requests to Mistral-specific functions like mistral_chat() and mistral_embedding() based on the context of the call.

Usage

mistral(..., .called_from = NULL)

Arguments

Value

The result of the requested action, depending on the specific function invoked (e.g., an updated LLMMessage object for chat(), or a matrix for embed()).

Description

Send LLMMessage to Mistral API

Usage

mistral_chat(
  .llm,
  .model = "mistral-large-latest",
  .frequency_penalty = NULL,
  .logit_bias = NULL,
  .presence_penalty = NULL,
  .seed = NULL,
  .stop = NULL,
  .stream = FALSE,
  .temperature = 0.7,
  .top_p = 1,
  .min_tokens = NULL,
  .max_tokens = NULL,
  .json_schema = NULL,
  .safe_prompt = FALSE,
  .reasoning_effort = NULL,
  .timeout = 120,
  .max_tries = 3,
  .dry_run = FALSE,
  .verbose = FALSE,
  .tools = NULL,
  .tool_choice = NULL,
  .max_tool_rounds = 10
)

Arguments

Value

Returns an updated LLMMessage object.

Description

Generate Embeddings Using Mistral API

Usage

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

Arguments

Value

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

Description

List Available Models from the Mistral API

Usage

mistral_list_models(
  .api_url = "https://api.mistral.ai",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE,
  .verbose = FALSE
)

Arguments

Value

A tibble containing model information (columns include id and created), or NULL if no models are found.

Description

The ollama() function acts as an interface for interacting with local AI models via the Ollama API. It integrates seamlessly with the main tidyllm verbs such as chat() and embed().

Usage

ollama(..., .called_from = NULL)

Arguments

Details

Some functionalities, like ollama_download_model() or ollama_list_models() are unique to the Ollama API and do not have a general verb counterpart. These functions can be only accessed directly.

Supported Verbs:

• chat(): Sends a message to an Ollama model and retrieves the model's response.

• embed(): Generates embeddings for input texts using an Ollama model.

• send_batch(): Behaves different than the other send_batch() verbs since it immediately processes the answers

Value

The result of the requested action:

• For chat(): An updated LLMMessage object containing the model's response.

• For embed(): A matrix where each column corresponds to an embedding.

Description

Interact with local AI models via the Ollama API

Usage

ollama_chat(
  .llm,
  .model = "qwen3.5:4b",
  .stream = FALSE,
  .seed = NULL,
  .json_schema = NULL,
  .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,
  .tools = NULL,
  .max_tool_rounds = 10,
  .tfs_z = NULL,
  .stop = NULL,
  .think = NULL,
  .ollama_server = "http://localhost:11434",
  .timeout = 120,
  .keep_alive = NULL,
  .dry_run = FALSE
)

Arguments

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_chat(llm, .model = "gemma2", .temperature = 0.7)

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

## End(Not run)

Description

This function sends a DELETE request to the Ollama API to remove a specified model.

Usage

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

Arguments

Description

This function sends a request to the Ollama API to download a specified model from Ollama's large online library of models.

Usage

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

Arguments

Description

Generate Embeddings Using Ollama API

Usage

ollama_embedding(
  .input,
  .model = "qwen3-embedding:0.6b",
  .truncate = TRUE,
  .ollama_server = "http://localhost:11434",
  .timeout = 120,
  .dry_run = FALSE
)

Arguments

Value

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

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")

Arguments

Value

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

Description

The openai() function acts as an interface for interacting with the OpenAI API through main tidyllm verbs such as chat(), embed(), and send_batch(). It dynamically routes requests to OpenAI-specific functions like openai_chat() and openai_embedding() based on the context of the call.

Usage

openai(..., .called_from = NULL)

openai(..., .called_from = NULL)

Arguments

Value

The result of the requested action, depending on the specific function invoked (e.g., an updated LLMMessage object for chat(), or a matrix for embed()).

Result of the requested action.

Description

Sends a message history to the OpenAI Responses API (POST /v1/responses) and returns the assistant's reply. Supports streaming, tool use, structured output, and reasoning models (o-series) via .reasoning_effort.

Usage

openai_chat(
  .llm,
  .model = "gpt-5.5",
  .max_output_tokens = NULL,
  .temperature = NULL,
  .seed = NULL,
  .stream = FALSE,
  .timeout = 60,
  .verbose = FALSE,
  .json_schema = NULL,
  .max_tries = 3,
  .dry_run = FALSE,
  .reasoning_effort = NULL,
  .tools = NULL,
  .tool_choice = NULL,
  .max_tool_rounds = 10,
  .stateful = FALSE
)

Arguments

Value

A new LLMMessage object with the assistant's response appended.

Description

Polls the status of an OpenAI background response created by openai_deep_research(.background = TRUE).

Usage

openai_check_research(.job, .max_tries = 3)

Arguments

Value

An updated tidyllm_research_job with ⁠$status⁠ set. If completed, ⁠$response⁠ is also populated and ready for fetch_job().

Description

OpenAI built-in code interpreter tool (server-executed)

Usage

openai_code_interpreter()

Value

A TOOL object declaring the code_interpreter built-in.

Description

Sends a research request to OpenAI using the deep research models (o3-deep-research or o4-mini-deep-research) via the Responses API with background: true. The model autonomously searches the web and synthesises a long-form answer, which can take 5-30 minutes.

Usage

openai_deep_research(
  .llm,
  .model = "o4-mini-deep-research",
  .background = FALSE,
  .reasoning_effort = "medium",
  .json_schema = NULL,
  .max_output_tokens = NULL,
  .timeout = 1800,
  .max_tries = 3
)

Arguments

Value

If .background = FALSE, an updated LLMMessage with the research reply. If .background = TRUE, a tidyllm_research_job for use with check_job()/fetch_job().

Description

Generate Embeddings Using OpenAI API

Usage

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

Arguments

Value

A tibble with two columns: input and embeddings. The input column contains the texts sent to embed, and the embeddings column is a list column where each row contains an embedding vector of the sent input.

Description

Extracts the assistant reply from a completed tidyllm_research_job returned by openai_deep_research(.background = TRUE).

Usage

openai_fetch_research(.job, .max_tries = 3)

Arguments

Value

An updated LLMMessage with the research reply appended.

Description

List Available Models from the OpenAI API

Usage

openai_list_models(
  .api_url = "https://api.openai.com",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE,
  .verbose = FALSE
)

Arguments

Value

A tibble containing model information (columns include id, created, and owned_by), or NULL if no models are found.

Description

OpenAI built-in web search tool (server-executed)

Usage

openai_websearch()

Value

A TOOL object declaring the web_search_preview built-in.

Description

The openrouter() function provides access to hundreds of AI models from different providers through the OpenRouter API, using a single OpenAI-compatible interface.

Usage

openrouter(..., .called_from = NULL)

Arguments

Value

The result of the requested action (e.g., an updated LLMMessage for chat()).

Description

Sends a message history to the OpenRouter API, which provides access to hundreds of models from different providers through a single OpenAI-compatible endpoint.

Usage

openrouter_chat(
  .llm,
  .model = "anthropic/claude-sonnet-4-6",
  .max_tokens = 2048,
  .temperature = NULL,
  .top_p = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .stop = NULL,
  .stream = FALSE,
  .json_schema = NULL,
  .tools = NULL,
  .tool_choice = NULL,
  .reasoning = NULL,
  .provider = NULL,
  .route = NULL,
  .models = NULL,
  .api_url = "https://openrouter.ai",
  .timeout = 60,
  .verbose = FALSE,
  .dry_run = FALSE,
  .max_tries = 3,
  .max_tool_rounds = 10
)

Arguments

Value

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

Description

Returns the total credits purchased and total usage so far for the current API key.

Usage

openrouter_credits(
  .api_url = "https://openrouter.ai",
  .timeout = 60,
  .max_tries = 3
)

Arguments

Value

A tibble with columns total_credits (USD purchased), total_usage (USD consumed so far), and remaining (USD remaining).

Description

Sends text to an embedding model accessible via OpenRouter and returns embedding vectors. Note that embedding models are not listed in list_models(openrouter()) — specify the model ID directly. Known supported models include "openai/text-embedding-3-small", "openai/text-embedding-3-large", and "mistralai/mistral-embed".

Usage

openrouter_embedding(
  .input,
  .model = "openai/text-embedding-3-small",
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

Value

A tibble with columns input (text) and embeddings (list of numeric vectors).

Description

Fetches cost, latency, and token details for a past generation using its ID. The generation ID is available in get_metadata(result)$api_specific$id after a chat() call.

Usage

openrouter_generation(
  .id,
  .api_url = "https://openrouter.ai",
  .timeout = 60,
  .max_tries = 3
)

Arguments

Value

A named list with generation details including id, model, total_cost, tokens_prompt, tokens_completion, and latency.

Description

Retrieves the list of models available through the OpenRouter API, including pricing and context window information.

Usage

openrouter_list_models(
  .api_url = "https://openrouter.ai",
  .timeout = 60,
  .max_tries = 3,
  .dry_run = FALSE,
  .verbose = FALSE
)

Arguments

Value

A tibble with columns id, name, context_length, prompt_price_per_million, and completion_price_per_million, or NULL if no models are found.

Description

Stores a reference to a local PDF file along with a pdftools text extraction. Binary encoding is deferred to format time; providers that support binary PDF encode on demand, others use the stored text fallback.

Usage

pdf_file(.path, pages = NULL, .text_extract = FALSE, engine = NULL)

Arguments

Value

A tidyllm_pdf object.

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
)

Arguments

Value

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

Description

The perplexity() function acts as a provider interface for interacting with the Perplexity API through tidyllm's chat() verb. It dynamically routes requests to Perplxeity-specific function. At the moment this is only perplexity_chat()

Usage

perplexity(..., .called_from = NULL)

Arguments

Value

The result of the requested action, depending on the specific function invoked (e.g., an updated LLMMessage object for chat()).

Description

Sends a chat message history to the Perplexity Chat API, supporting all documented API parameters.

Usage

perplexity_chat(
  .llm,
  .model = "sonar",
  .max_tokens = 1024,
  .temperature = NULL,
  .top_p = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL,
  .stop = NULL,
  .search_domain_filter = NULL,
  .search_language_filter = NULL,
  .language_preference = NULL,
  .return_images = FALSE,
  .image_domain_filter = NULL,
  .image_format_filter = NULL,
  .search_recency_filter = NULL,
  .search_mode = "web",
  .search_after_date_filter = NULL,
  .search_before_date_filter = NULL,
  .last_updated_after_filter = NULL,
  .last_updated_before_filter = NULL,
  .disable_search = FALSE,
  .enable_search_classifier = FALSE,
  .reasoning_effort = NULL,
  .return_related_questions = FALSE,
  .user_location = NULL,
  .search_context_size = NULL,
  .search_type = NULL,
  .stream_mode = NULL,
  .json_schema = NULL,
  .top_k = NULL,
  .web_search_options = NULL,
  .api_url = "https://api.perplexity.ai/",
  .timeout = 60,
  .stream = FALSE,
  .verbose = FALSE,
  .max_tries = 3,
  .dry_run = FALSE
)