From 2331b463e0606cd4ee49ecb353b89b163da06d9e Mon Sep 17 00:00:00 2001
From: Angelo D'Ambrosio <bakaburg1@gmail.com>
Date: Thu, 18 Jul 2024 18:59:47 +0200
Subject: [PATCH] refactor: rely on the llmR package for LLM interaction

Removed the llm related functions since they will be provided by the llmR package
---
 DESCRIPTION                         |   7 +-
 NAMESPACE                           |   2 -
 R/LLM_calls.R                       | 473 ----------------------------
 R/data_management.R                 |   2 +-
 R/summarization.R                   |  21 +-
 man/entity_extractor.Rd             |   3 +-
 man/infer_agenda_from_transcript.Rd |   2 +-
 man/process_messages.Rd             |  43 ---
 man/prompt_llm.Rd                   |  55 ----
 man/speech_to_summary_workflow.Rd   |   2 +-
 man/summarise_full_meeting.Rd       |   4 +-
 man/summarise_transcript.Rd         |   2 +-
 man/use_azure_llm.Rd                |  41 ---
 man/use_custom_llm.Rd               |  38 ---
 man/use_openai_llm.Rd               |  30 --
 15 files changed, 24 insertions(+), 701 deletions(-)
 delete mode 100644 R/LLM_calls.R
 delete mode 100644 man/process_messages.Rd
 delete mode 100644 man/prompt_llm.Rd
 delete mode 100644 man/use_azure_llm.Rd
 delete mode 100644 man/use_custom_llm.Rd
 delete mode 100644 man/use_openai_llm.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 2198a07..e54aa4f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: minutemaker
 Title: GenAI-based meeting and conferences minutes generator
-Version: 0.10.0
+Version: 0.11.0
 Authors@R: 
     person("Angelo", "D'Ambrosio", , "a.dambrosioMD@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-2045-5155"))
@@ -11,6 +11,7 @@ Imports:
     dplyr (>= 1.1.4),
     httr (>= 1.4.7),
     jsonlite (>= 1.8.8),
+    llmR (>= 1.1.0),
     lubridate (>= 1.9.3),
     purrr (>= 1.0.2),
     readr (>= 2.1.4),
@@ -19,10 +20,12 @@ Imports:
     styler (>= 1.10.2),
     tools (>= 4.3.2),
     vctrs (>= 0.6.5)
+Remotes: 
+    github::bakaburg1/llmR
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Suggests: 
     av (>= 0.9.0),
     devtools (>= 2.4.5),
diff --git a/NAMESPACE b/NAMESPACE
index 440d251..c72e775 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -13,7 +13,6 @@ export(infer_agenda_from_transcript)
 export(merge_transcripts)
 export(parse_transcript_json)
 export(perform_speech_to_text)
-export(prompt_llm)
 export(run_in_terminal)
 export(set_prompts)
 export(speech_to_summary_workflow)
@@ -23,5 +22,4 @@ export(summarise_transcript)
 export(validate_agenda)
 import(dplyr)
 importFrom(stats,setNames)
-importFrom(utils,hasName)
 importFrom(utils,tail)
diff --git a/R/LLM_calls.R b/R/LLM_calls.R
deleted file mode 100644
index 8745236..0000000
--- a/R/LLM_calls.R
+++ /dev/null
@@ -1,473 +0,0 @@
-#' Process chat message into standard format
-#'
-#' This function takes one or more (a list of) chat messages and processes them
-#' into a standard list format with role and content for each message to be fed
-#' to a large language model.
-#'
-#' The standard format is a list of chat messages with the following structure:
-#' message: `c(role = "system", content = "Welcome to the chat!")`
-#' list of messages: \code{list(
-#'    c(role = "system", content = "You are an useful AI assistant."),
-#'    c(role = "user", content = "Hi there!")
-#'  )}
-#'  list format: \code{list(
-#'    list(role = "system", content = "You are an useful AI assistant."),
-#'    list(role = "user", content = "Hi there!")
-#'  )}
-#'  list of lists format: \code{list(
-#'    list(
-#'      list(role = "system", content = "You are an useful AI assistant."),
-#'      list(role = "user", content = "Hi there!")
-#'    ),
-#'    list(
-#'      list(role = "system", content = "You are an useful AI assistant."),
-#'      list(role = "user", content = "Hi there!")
-#'    )
-#'  )}
-#'
-#' @param messages A character vector or list of chat messages. In can be a
-#'   vector, a specifically structured list or a list of both if the goal is the
-#'   have the API process multiple messages at once.
-#'
-#' @return A list of chat messages in standard format.
-#'
-#' @importFrom utils hasName
-#'
-process_messages <- function(messages) {
-
-  if (missing(messages) || is.null(messages) || length(messages) == 0) {
-    stop("User messages are required.")
-  }
-
-  # Assume that a single message is from the user
-  if (length(messages) == 1 &&
-      is.character(messages) &&
-      is.null(names(messages))) {
-    messages <- c(user = messages)
-  }
-
-  # Convert vector to list format
-  vector_to_list <- function(msg_vec) {
-
-    # Check if vector is in named format
-    check <- all(
-      names(msg_vec) %in%
-        c("system", "user", "assistant", "function")
-      , na.rm = TRUE)
-
-    check <- check && !is.null(names(msg_vec))
-
-    if (check) {
-
-      # Convert from vector to list format
-      msg_vec <- purrr::imap(msg_vec, function(msg, nm) {
-        list(role = nm, content = msg)
-      }) |> setNames(NULL)
-
-    } else {
-      stop("Invalid format for 'messages' vector.")
-    }
-  }
-
-  # Validate list format
-  validate_list_format <- function(msg_list) {
-
-    # Check if the message is in correct list format
-    check <- !purrr::every(msg_list, function(msg) {
-      vctrs::obj_is_list(msg) &&
-        hasName(msg, "role") &&
-        hasName(msg, "content") &&
-        msg$role %in% c("system", "user", "assistant", "function")
-    })
-
-    return(!check)
-  }
-
-  # Check if message is in a valid vector format
-  if (is.character(messages)) {
-    return(vector_to_list(messages))
-  }
-
-
-  if (vctrs::obj_is_list(messages)) {
-
-    # Check if valid list format
-    if (validate_list_format(messages)) {
-      return(messages)
-    }
-
-    # It turned out the API doesn't really support batch calls of
-    # multiple prompts
-
-    # # Check if list of vectors
-    # if (purrr::every(messages, is.character)) {
-    #
-    #   # Convert each to list
-    #   return(purrr::map(messages, vector_to_list))
-    #
-    # }
-    #
-    # # Check if list of lists
-    # if (purrr::every(messages, validate_list_format)) {
-    #
-    #   return(messages)
-    #
-    # }
-
-  }
-
-  stop("Message is neither a valid vector nor a valid list.")
-
-}
-
-#' Interrogate a Language Model
-#'
-#' This function sends requests to a specified language model provider (OpenAI,
-#' Azure, or a locally running LLM server) and returns the response. It handles
-#' rate limiting and retries the request if necessary, and also processes errors
-#' in the response.
-#'
-#' Users can provide their own models by writing a function with the following
-#' name pattern: `use_<model_name>_llm`. See the existing functions using the
-#' ::: operator for examples.
-#'
-#' @param messages Messages to be sent to the language model.
-#' @param provider The provider of the language model. Defaults to "openai".
-#'   Other options are "azure" and "local".
-#' @param params Additional parameters for the language model request. Defaults
-#'   to a list with `temperature = 0`.
-#' @param force_json A boolean to force the response in JSON format. Default is
-#'   FALSE. Works only for OpenAI and Azure endpoints.
-#' @param log_request A boolean to log the request time. Can be set up globally
-#'   using the `minutemaker_log_requests` option, which defaults to TRUE.
-#' @param ... Additional arguments passed to the language model provider
-#'   functions.
-#'
-#' @return Returns the content of the message from the language model response.
-#'
-#' @export
-#'
-#' @examples
-#' \dontrun{
-#' response <- prompt_llm(
-#'  messages = c(user = "Hello there!"),
-#'  provider = "openai")
-#'  }
-#'
-prompt_llm <- function(
-    messages = NULL,
-    provider = getOption("minutemaker_llm_provider"),
-    params = list(
-      temperature = 0
-    ),
-    force_json = FALSE,
-    log_request = getOption("minutemaker_log_requests", TRUE),
-    ...) {
-
-  messages <- process_messages(messages)
-
-  if (is.null(provider)) {
-    stop("Language model provider is not set. ",
-         "You can use the following option to set it globally:\n",
-         "minutemaker_llm_provider.")
-  }
-
-  if (log_request) {
-    check_and_install_dependencies("tictoc")
-  }
-
-  # Prepare the body of the request and merge with default
-  body <- purrr::list_modify(list(
-    temperature = 0
-  ), !!!params)
-
-  body$messages <- messages
-
-  # Force the LLM to answer in JSON format (not all models support this)
-  if (force_json) {
-    body$response_format <- list("type" = "json_object")
-  }
-
-  # Map provider to specific function
-  llm_fun <- paste0("use_", provider, "_llm")
-
-  if (!exists(llm_fun, mode = "function")) {
-    stop("Unsupported LLM provider.
-         You can set it project-wide using the minutemaker_llm_provider option.")
-  }
-
-  llm_fun <- get(llm_fun)
-
-  # Try to send the request
-  retry <- FALSE
-
-  while(!exists("response", inherits = FALSE) || retry) {
-
-    #message("Sending message to Azure GPT API.")
-    retry <- FALSE
-
-    if (log_request) tictoc::tic()
-    response <- llm_fun(body, ...)
-    if (log_request) elapsed <- tictoc::toc()
-
-    if (httr::status_code(response) == 429) {
-      warning("Rate limit exceeded. Waiting before retrying.",
-              immediate. = TRUE, call. = FALSE)
-
-      to_wait <- as.numeric(httr::headers(response)$`retry-after`)
-      message("Waiting for ", to_wait, " seconds.\n...")
-      Sys.sleep(to_wait)
-      message("Retrying...")
-      retry <- TRUE
-    }
-  }
-
-
-  # Check for errors in response
-  if (httr::http_error(response)) {
-    err_obj <- httr::content(response)$error
-
-    err_message <- if (is.character(err_obj)) {
-      err_obj
-    } else if (is.character(err_obj$message)) {
-      err_obj$message
-    } else {
-      httr::content(response)
-    }
-
-    stop("Error in LLM request: ", err_message)
-  }
-
-  # Return the response
-  parsed <- httr::content(response, as = "parsed", encoding = "UTF-8")
-
-  if (log_request) {
-    with(parsed$usage,
-         paste(
-           "Prompt tokens:", prompt_tokens,
-           "\nResponse tokens:", completion_tokens,
-           "\nGeneration speed:", paste(
-             signif(completion_tokens/(elapsed$toc - elapsed$tic), 3), "t/s"),
-           "\nTotal tokens:", total_tokens
-         )
-    ) |> message()
-  }
-
-  # Return the response
-  purrr::imap_chr(parsed$choices, \(ans, i) {
-    ans_content <- ans$message$content
-
-    # Manage the case when the answer is cut off due to exceeding the
-    # output token limit
-    if (ans$finish_reason == "length") {
-      i <- if (length(parsed$choices) > 1) paste0(" ", i, " ") else " "
-
-      warning("Answer", i, "exhausted the context window!")
-
-      file_name <- paste0("output_", Sys.time(), ".txt")
-
-      warning(
-        "Answer", i, "exhausted the context window!\n",
-        "The answer has been saved to a file: ", file_name
-      )
-
-      readr::write_lines(ans_content, file_name)
-
-      choice <- utils::menu(
-        c(
-          "Try to complete the answer",
-          "Keep the incomplete answer",
-          "Stop the process"),
-        title = "How do you want to proceed?"
-      )
-
-      if (choice == 1) {
-        # Ask the model to continue the answer
-        messages_new <- c(
-          messages,
-          list(list(
-            role = "assistant",
-            content = ans_content
-          )),
-          list(list(
-            role = "user",
-            content = "continue"
-          ))
-        )
-
-        ans_new <- prompt_llm(
-          messages_new, provider = provider, params = params,
-          force_json = force_json,
-          log_request = log_request, ...
-        )
-
-        return(paste0(ans_content, ans_new))
-      } else if (choice == 2) {
-        return(ans_content)
-      } else {
-        stop("The process has been stopped.")
-      }
-    } else ans_content
-  })
-}
-
-#' Use OpenAI Language Model
-#'
-#' Sends a request to the OpenAI API using the parameters in the `body`
-#' argument. It requires an API key and model identifier set in the R options.
-#'
-#' @param body The body of the request.
-#' @param model Model identifier for the OpenAI API. Obtained from R options.
-#' @param api_key API key for the OpenAI service. Obtained from R options.
-#' @param log_request A boolean to log the request time. Can be set up globally
-#'   using the `minutemaker_log_requests` option, which defaults to TRUE.
-#'
-#' @return The function returns the response from the OpenAI API.
-#'
-use_openai_llm <- function(
-    body,
-    model = getOption("minutemaker_openai_model_gpt"),
-    api_key = getOption("minutemaker_openai_api_key"),
-    log_request = getOption("minutemaker_log_requests", TRUE)
-) {
-
-  if (is.null(api_key) || is.null(model)) {
-    stop("OpenAI GPT model or API key are not set. ",
-         "Use the following options to set them:\n",
-         "minutemaker_openai_model_gpt, ",
-         "minutemaker_open_api_key options.")
-  }
-
-  if (log_request) {
-    message("Interrogating OpenAI: ", model, "...")
-  }
-
-  body$model = model
-
-  # Prepare the request
-  httr::POST(
-    url = "https://api.openai.com/v1/chat/completions",
-    httr::add_headers(
-      `Content-Type` = "application/json",
-      `Authorization` = paste0("Bearer ", api_key)
-    ),
-    body = jsonlite::toJSON(body, auto_unbox = TRUE),
-    encode = "json"
-  )
-
-}
-
-#' Use Azure Language Model
-#'
-#' Sends a request to the Azure API for language model completions using the
-#' parameters in the `body` argument. This function requires specific Azure
-#' configurations (deployment ID, resource name, API key, and API version) set
-#' in the R options.
-#'
-#' @param body The body of the request.
-#' @param deployment_id Azure deployment ID for the language model. Obtained
-#'   from R options.
-#' @param resource_name Azure resource name. Obtained from R options.
-#' @param api_key API key for the Azure language model service. Obtained from R
-#'   options.
-#' @param api_version API version for the Azure language model service. Obtained
-#'   from R options.
-#' @param log_request A boolean to log the request time. Can be set up globally
-#'   using the `minutemaker_log_requests` option, which defaults to TRUE.
-#'
-#' @return The function returns the response from the Azure API.
-use_azure_llm <- function(
-    body,
-    deployment_id = getOption("minutemaker_azure_deployment_gpt"),
-    resource_name = getOption("minutemaker_azure_resource_gpt"),
-    api_key = getOption("minutemaker_azure_api_key_gpt"),
-    api_version = getOption("minutemaker_azure_api_version"),
-    log_request = getOption("minutemaker_log_requests", TRUE)
-) {
-
-  if (is.null(resource_name) || is.null(deployment_id) ||
-      is.null(api_key) || is.null(api_version)) {
-    stop("Azure GPT resource name, deployment name,",
-         ", API key, or API version are not set. ",
-         "Use the following options to set them:\n",
-         "minutemaker_azure_deployment_gpt, ",
-         "minutemaker_azure_resource_gpt, ",
-         "minutemaker_azure_api_key_gpt, ",
-         "minutemaker_azure_api_version."
-    )
-  }
-
-  if (log_request) {
-    message(
-      "Interrogating Azure OpenAI: ", resource_name, "/", deployment_id,
-      " (", api_version, ")...")
-  }
-
-  # Prepare the request
-  httr::POST(
-    url = paste0(
-      "https://",
-      resource_name,
-      ".openai.azure.com/openai/deployments/",
-      deployment_id,
-      "/chat/completions?api-version=",
-      api_version),
-    httr::add_headers(`Content-Type` = "application/json", `api-key` = api_key),
-    body = jsonlite::toJSON(body, auto_unbox = TRUE)
-  )
-
-}
-
-#' Use Custom Language Model
-#'
-#' Sends a request to a custom (local or remote) language model endpoint
-#' compatible with the OpenAi API specification, using the parameters in the
-#' `body` argument. The user can provide an API key if required.
-#'
-#' @param body The body of the request.
-#' @param endpoint The local endpoint for the language model service. Can be
-#'   obtained from R options.
-#' @param model Model identifier for the custom API, if needed (some API have
-#'   one model per endpoint, some multiple ones). Obtained from R options.
-#' @param api_key Optional API key for the custom language model services that
-#'   require it. Obtained from R options.
-#' @param log_request A boolean to log the request time. Can be set up globally
-#'   using the `minutemaker_log_requests` option, which defaults to TRUE.
-#'
-#' @return The function returns the response from the local language model
-#'   endpoint.
-use_custom_llm <- function(
-    body,
-    endpoint = getOption("minutemaker_custom_endpoint_gpt"),
-    model = getOption("minutemaker_custom_model_gpt", NULL),
-    api_key = getOption("minutemaker_custom_api_key"),
-    log_request = getOption("minutemaker_log_requests", TRUE)
-) {
-
-  if (is.null(endpoint)) {
-    stop("Local endpoint is not set. ",
-         "Use the following options to set it:\n",
-         "minutemaker_custom_endpoint_gpt"
-    )
-  }
-
-  if (log_request) {
-    message("Interrogating custom LLM: ", endpoint, "/", model, "...")
-  }
-
-  if (!is.null(model)) {
-    body$model = model
-  }
-
-  # Prepare the request
-  httr::POST(
-    url = endpoint,
-    httr::add_headers(
-      `Content-Type` = "application/json",
-      if (!is.null(api_key)) {
-        .headers = c(Authorization = paste0("Bearer ", api_key))
-      }),
-    body = jsonlite::toJSON(body, auto_unbox = TRUE)
-  )
-
-}
diff --git a/R/data_management.R b/R/data_management.R
index a5248fc..c0f6ac7 100644
--- a/R/data_management.R
+++ b/R/data_management.R
@@ -1100,7 +1100,7 @@ add_chat_transcript <- function(
 #' @param llm_provider A string indicating the LLM provider to use for the
 #'   summarization. See `summarise_transcript` for more details.
 #' @param extra_summarise_args Additional arguments passed to the
-#'   `prompt_llm` function. See `summarise_transcript` for more details.
+#'   `llmR::prompt_llm` function. See `summarise_transcript` for more details.
 #' @param summarization_window_size The size of the summarization window in
 #'   minutes if the "rolling"  method is used. See `summarise_transcript` for
 #'   more details.
diff --git a/R/summarization.R b/R/summarization.R
index cd42a1f..0eb05fc 100644
--- a/R/summarization.R
+++ b/R/summarization.R
@@ -111,7 +111,7 @@ generate_recording_details <- function(
 #'   get_prompts("output_rolling_aggregation") prompts depending on the task.
 #' @param prompt_only If TRUE, only the prompt is returned, the LLM is not
 #'   interrogated. Default is FALSE.
-#' @param ... Additional arguments passed to the `prompt_llm` function,
+#' @param ... Additional arguments passed to the `llmR::prompt_llm` function,
 #'   such as the LLM provider.
 #'
 #' @return A summary of the transcript.
@@ -252,7 +252,7 @@ summarise_transcript <- function(
     }
 
     # Interrogate the LLM
-    prompt_llm(
+    llmR::prompt_llm(
       c(
         system = get_prompts("persona"),
         user = prompt),
@@ -280,7 +280,7 @@ summarise_transcript <- function(
     args = args
   )
 
-  prompt_llm(
+  llmR::prompt_llm(
     c(
       system = get_prompts("persona"),
       user = aggregation_prompt),
@@ -328,8 +328,8 @@ summarise_transcript <- function(
 #'   `summarise_transcript` for more details and run `get_prompts()` to see the
 #'   defaults. See `summarise_transcript` for more details.
 #' @param overwrite Whether to overwrite existing summaries. Default is FALSE.
-#' @param ... Additional arguments passed to `prompt_llm` function, such as
-#'   the LLM provider.
+#' @param ... Additional arguments passed to `llmR::prompt_llm` function, such
+#'   as the LLM provider.
 #'
 #' @return The result tree of the meeting summary. Also saves the results in the
 #'   output file as side effect.
@@ -503,7 +503,7 @@ summarise_full_meeting <- function(
 #'   LLM context.
 #' @param output_file An optional file to save the results to. Default is NULL,
 #'   i.e., the results are not saved to a file.
-#' @param ... Additional arguments passed to the `prompt_llm` function.
+#' @param ... Additional arguments passed to the `llmR::prompt_llm` function.
 #'   Keep in consideration that this function needs LLMs that manages long
 #'   context and that produce valid JSON outputs. The `force_json` argument is
 #'   used with OpenAI based LLM but it's not accepted by other LLMs; therefore
@@ -686,7 +686,7 @@ infer_agenda_from_transcript <- function(
     }
 
     # Attempt to interrogate the LLM
-    result_json <- try(prompt_llm(
+    result_json <- try(llmR::prompt_llm(
       prompt_set,
       ...,
       force_json = TRUE
@@ -834,7 +834,7 @@ infer_agenda_from_transcript <- function(
       user = prompt
     )
 
-    result_json <- prompt_llm(
+    result_json <- llmR::prompt_llm(
       prompt_set,
       ..., force_json = TRUE
     )
@@ -877,7 +877,8 @@ infer_agenda_from_transcript <- function(
 #'   them.
 #' @param prompt_only If TRUE, only the prompt is returned, the LLM is not
 #'   interrogated. Default is FALSE.
-#' @param ... Additional arguments passed to the `prompt_llm` function.
+#' @param ... Additional arguments passed to the `llmR::prompt_llm`
+#'   function.
 #'
 #' @return A vector with the entities found in the text.
 #'
@@ -936,7 +937,7 @@ entity_extractor <- function(
     return(task)
   }
 
-  prompt_llm(
+  llmR::prompt_llm(
     c("system" = get_prompts("persona"), "user" = task),
     force_json = TRUE, ...) |>
     jsonlite::fromJSON() |>
diff --git a/man/entity_extractor.Rd b/man/entity_extractor.Rd
index 07fc1be..3026ab9 100644
--- a/man/entity_extractor.Rd
+++ b/man/entity_extractor.Rd
@@ -21,7 +21,8 @@ them.}
 \item{prompt_only}{If TRUE, only the prompt is returned, the LLM is not
 interrogated. Default is FALSE.}
 
-\item{...}{Additional arguments passed to the \code{prompt_llm} function.}
+\item{...}{Additional arguments passed to the \code{llmR::prompt_llm}
+function.}
 }
 \value{
 A vector with the entities found in the text.
diff --git a/man/infer_agenda_from_transcript.Rd b/man/infer_agenda_from_transcript.Rd
index e7e70a2..58dd95b 100644
--- a/man/infer_agenda_from_transcript.Rd
+++ b/man/infer_agenda_from_transcript.Rd
@@ -49,7 +49,7 @@ LLM context.}
 \item{output_file}{An optional file to save the results to. Default is NULL,
 i.e., the results are not saved to a file.}
 
-\item{...}{Additional arguments passed to the \code{prompt_llm} function.
+\item{...}{Additional arguments passed to the \code{llmR::prompt_llm} function.
 Keep in consideration that this function needs LLMs that manages long
 context and that produce valid JSON outputs. The \code{force_json} argument is
 used with OpenAI based LLM but it's not accepted by other LLMs; therefore
diff --git a/man/process_messages.Rd b/man/process_messages.Rd
deleted file mode 100644
index 8478326..0000000
--- a/man/process_messages.Rd
+++ /dev/null
@@ -1,43 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/LLM_calls.R
-\name{process_messages}
-\alias{process_messages}
-\title{Process chat message into standard format}
-\usage{
-process_messages(messages)
-}
-\arguments{
-\item{messages}{A character vector or list of chat messages. In can be a
-vector, a specifically structured list or a list of both if the goal is the
-have the API process multiple messages at once.}
-}
-\value{
-A list of chat messages in standard format.
-}
-\description{
-This function takes one or more (a list of) chat messages and processes them
-into a standard list format with role and content for each message to be fed
-to a large language model.
-}
-\details{
-The standard format is a list of chat messages with the following structure:
-message: \code{c(role = "system", content = "Welcome to the chat!")}
-list of messages: \code{list(
-   c(role = "system", content = "You are an useful AI assistant."),
-   c(role = "user", content = "Hi there!")
- )}
-list format: \code{list(
-   list(role = "system", content = "You are an useful AI assistant."),
-   list(role = "user", content = "Hi there!")
- )}
-list of lists format: \code{list(
-   list(
-     list(role = "system", content = "You are an useful AI assistant."),
-     list(role = "user", content = "Hi there!")
-   ),
-   list(
-     list(role = "system", content = "You are an useful AI assistant."),
-     list(role = "user", content = "Hi there!")
-   )
- )}
-}
diff --git a/man/prompt_llm.Rd b/man/prompt_llm.Rd
deleted file mode 100644
index 471cb35..0000000
--- a/man/prompt_llm.Rd
+++ /dev/null
@@ -1,55 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/LLM_calls.R
-\name{prompt_llm}
-\alias{prompt_llm}
-\title{Interrogate a Language Model}
-\usage{
-prompt_llm(
-  messages = NULL,
-  provider = getOption("minutemaker_llm_provider"),
-  params = list(temperature = 0),
-  force_json = FALSE,
-  log_request = getOption("minutemaker_log_requests", TRUE),
-  ...
-)
-}
-\arguments{
-\item{messages}{Messages to be sent to the language model.}
-
-\item{provider}{The provider of the language model. Defaults to "openai".
-Other options are "azure" and "local".}
-
-\item{params}{Additional parameters for the language model request. Defaults
-to a list with \code{temperature = 0}.}
-
-\item{force_json}{A boolean to force the response in JSON format. Default is
-FALSE. Works only for OpenAI and Azure endpoints.}
-
-\item{log_request}{A boolean to log the request time. Can be set up globally
-using the \code{minutemaker_log_requests} option, which defaults to TRUE.}
-
-\item{...}{Additional arguments passed to the language model provider
-functions.}
-}
-\value{
-Returns the content of the message from the language model response.
-}
-\description{
-This function sends requests to a specified language model provider (OpenAI,
-Azure, or a locally running LLM server) and returns the response. It handles
-rate limiting and retries the request if necessary, and also processes errors
-in the response.
-}
-\details{
-Users can provide their own models by writing a function with the following
-name pattern: \verb{use_<model_name>_llm}. See the existing functions using the
-::: operator for examples.
-}
-\examples{
-\dontrun{
-response <- prompt_llm(
- messages = c(user = "Hello there!"),
- provider = "openai")
- }
-
-}
diff --git a/man/speech_to_summary_workflow.Rd b/man/speech_to_summary_workflow.Rd
index 2700ffd..1496d54 100644
--- a/man/speech_to_summary_workflow.Rd
+++ b/man/speech_to_summary_workflow.Rd
@@ -184,7 +184,7 @@ defaults. See \code{summarise_transcript} for more details.}
 summarization. See \code{summarise_transcript} for more details.}
 
 \item{extra_summarise_args}{Additional arguments passed to the
-\code{prompt_llm} function. See \code{summarise_transcript} for more details.}
+\code{llmR::prompt_llm} function. See \code{summarise_transcript} for more details.}
 
 \item{summarization_window_size}{The size of the summarization window in
 minutes if the "rolling"  method is used. See \code{summarise_transcript} for
diff --git a/man/summarise_full_meeting.Rd b/man/summarise_full_meeting.Rd
index e2ce52c..59d1e2b 100644
--- a/man/summarise_full_meeting.Rd
+++ b/man/summarise_full_meeting.Rd
@@ -68,8 +68,8 @@ defaults. See \code{summarise_transcript} for more details.}
 
 \item{overwrite}{Whether to overwrite existing summaries. Default is FALSE.}
 
-\item{...}{Additional arguments passed to \code{prompt_llm} function, such as
-the LLM provider.}
+\item{...}{Additional arguments passed to \code{llmR::prompt_llm} function, such
+as the LLM provider.}
 }
 \value{
 The result tree of the meeting summary. Also saves the results in the
diff --git a/man/summarise_transcript.Rd b/man/summarise_transcript.Rd
index f489344..0f23e2e 100644
--- a/man/summarise_transcript.Rd
+++ b/man/summarise_transcript.Rd
@@ -71,7 +71,7 @@ get_prompts("output_rolling_aggregation") prompts depending on the task.}
 \item{prompt_only}{If TRUE, only the prompt is returned, the LLM is not
 interrogated. Default is FALSE.}
 
-\item{...}{Additional arguments passed to the \code{prompt_llm} function,
+\item{...}{Additional arguments passed to the \code{llmR::prompt_llm} function,
 such as the LLM provider.}
 }
 \value{
diff --git a/man/use_azure_llm.Rd b/man/use_azure_llm.Rd
deleted file mode 100644
index 17a2207..0000000
--- a/man/use_azure_llm.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/LLM_calls.R
-\name{use_azure_llm}
-\alias{use_azure_llm}
-\title{Use Azure Language Model}
-\usage{
-use_azure_llm(
-  body,
-  deployment_id = getOption("minutemaker_azure_deployment_gpt"),
-  resource_name = getOption("minutemaker_azure_resource_gpt"),
-  api_key = getOption("minutemaker_azure_api_key_gpt"),
-  api_version = getOption("minutemaker_azure_api_version"),
-  log_request = getOption("minutemaker_log_requests", TRUE)
-)
-}
-\arguments{
-\item{body}{The body of the request.}
-
-\item{deployment_id}{Azure deployment ID for the language model. Obtained
-from R options.}
-
-\item{resource_name}{Azure resource name. Obtained from R options.}
-
-\item{api_key}{API key for the Azure language model service. Obtained from R
-options.}
-
-\item{api_version}{API version for the Azure language model service. Obtained
-from R options.}
-
-\item{log_request}{A boolean to log the request time. Can be set up globally
-using the \code{minutemaker_log_requests} option, which defaults to TRUE.}
-}
-\value{
-The function returns the response from the Azure API.
-}
-\description{
-Sends a request to the Azure API for language model completions using the
-parameters in the \code{body} argument. This function requires specific Azure
-configurations (deployment ID, resource name, API key, and API version) set
-in the R options.
-}
diff --git a/man/use_custom_llm.Rd b/man/use_custom_llm.Rd
deleted file mode 100644
index 71845b7..0000000
--- a/man/use_custom_llm.Rd
+++ /dev/null
@@ -1,38 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/LLM_calls.R
-\name{use_custom_llm}
-\alias{use_custom_llm}
-\title{Use Custom Language Model}
-\usage{
-use_custom_llm(
-  body,
-  endpoint = getOption("minutemaker_custom_endpoint_gpt"),
-  model = getOption("minutemaker_custom_model_gpt", NULL),
-  api_key = getOption("minutemaker_custom_api_key"),
-  log_request = getOption("minutemaker_log_requests", TRUE)
-)
-}
-\arguments{
-\item{body}{The body of the request.}
-
-\item{endpoint}{The local endpoint for the language model service. Can be
-obtained from R options.}
-
-\item{model}{Model identifier for the custom API, if needed (some API have
-one model per endpoint, some multiple ones). Obtained from R options.}
-
-\item{api_key}{Optional API key for the custom language model services that
-require it. Obtained from R options.}
-
-\item{log_request}{A boolean to log the request time. Can be set up globally
-using the \code{minutemaker_log_requests} option, which defaults to TRUE.}
-}
-\value{
-The function returns the response from the local language model
-endpoint.
-}
-\description{
-Sends a request to a custom (local or remote) language model endpoint
-compatible with the OpenAi API specification, using the parameters in the
-\code{body} argument. The user can provide an API key if required.
-}
diff --git a/man/use_openai_llm.Rd b/man/use_openai_llm.Rd
deleted file mode 100644
index 2833e3b..0000000
--- a/man/use_openai_llm.Rd
+++ /dev/null
@@ -1,30 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/LLM_calls.R
-\name{use_openai_llm}
-\alias{use_openai_llm}
-\title{Use OpenAI Language Model}
-\usage{
-use_openai_llm(
-  body,
-  model = getOption("minutemaker_openai_model_gpt"),
-  api_key = getOption("minutemaker_openai_api_key"),
-  log_request = getOption("minutemaker_log_requests", TRUE)
-)
-}
-\arguments{
-\item{body}{The body of the request.}
-
-\item{model}{Model identifier for the OpenAI API. Obtained from R options.}
-
-\item{api_key}{API key for the OpenAI service. Obtained from R options.}
-
-\item{log_request}{A boolean to log the request time. Can be set up globally
-using the \code{minutemaker_log_requests} option, which defaults to TRUE.}
-}
-\value{
-The function returns the response from the OpenAI API.
-}
-\description{
-Sends a request to the OpenAI API using the parameters in the \code{body}
-argument. It requires an API key and model identifier set in the R options.
-}