ai_text for ollama, small fix ai_validate

Author: SeraphineM

R/ai_salience.R

@@ -24,7 +24,7 @@
 #' }
 #' @export
 ai_salience <- function(.data, topics, chat_fn, ..., verbose = TRUE) {
-
+  
   # Predefine the type_object for salience classification
   predefined_type_object <- type_array(
     "Array of classification results. The scores should sum to 1.",
@@ -33,13 +33,13 @@ ai_salience <- function(.data, topics, chat_fn, ..., verbose = TRUE) {
       score = type_number("The classification score for the category, ranging from 0.0 to 1.0")
     )
   )
-
+  
   # Call ai_text with pre-set type_object and flexible arguments
   ai_text(
-      .data = .data,
-      chat_fn = chat_fn,
-      type_object = predefined_type_object,
-      verbose = verbose,
-      ...
+    .data = .data,
+    chat_fn = chat_fn,
+    type_object = predefined_type_object,
+    verbose = verbose,
+    ...
   )
 }

R/ai_text.R

@@ -1,4 +1,4 @@
-#' Structured AI analysis texts
+#' Structured AI analysis of texts
 #'
 #' This function applies AI-assisted analysis to each document in a character
 #' vector or a docvar in a [corpus][quanteda::corpus()], using a structured
@@ -37,7 +37,7 @@ ai_text <- function(.data, chat_fn, ..., type_object, few_shot_examples = NULL,
   if (!is.character(.data))
     stop("Unsupported data type for ai_text")
 
-  args <- rlang::list2()
+  args <- rlang::list2(...)
 
   if (is.null(names(.data))) names(.data) <- as.character(seq_along(.data))
 
@@ -65,10 +65,6 @@ ai_text <- function(.data, chat_fn, ..., type_object, few_shot_examples = NULL,
     }
   }
 
-  if (!"model" %in% names(args) && identical(chat_fn, chat_openai)) {
-    args <- c(args, list(model = "gpt-4o"))
-  }
-
   chat <- suppressMessages(do.call(chat_fn, args))
   model <- chat$get_model()
 
@@ -92,7 +88,9 @@ ai_text <- function(.data, chat_fn, ..., type_object, few_shot_examples = NULL,
     })
   }
 
-  df_results <- dplyr::bind_rows(as.list(result_env), .id = "id")
+  df_list <- lapply(names(.data), function(doc_id) result_env[[doc_id]])
+  names(df_list) <- names(.data)
+  df_results <- dplyr::bind_rows(df_list, .id = "id")
   rownames(df_results) <- NULL
   if (verbose) cat("Finished.\n")
 

R/ai_validate.R

@@ -1,13 +1,16 @@
-#' Starts an interactive app to manually validate the documents in a corpus
-#' which were summarized, labelled, or scored by an LLM.
+#' Starts an interactive app to manually validate the output of an LLM
+#' stored in a character vector 
 #'
+#' This function launches a Shiny app that allows users to manually validate
+#' the output of a LLM analysis, such as ai_score
 #' The comments from manual validation for each text are added as an additional
 #' docvar to the input data with all texts not yet validated as `NA`.
 #' @param text a character or [quanteda::corpus] object containing the
 #'   documents to be manually validated
 #' @param llm_output a character string; the name of the LLM output column which
 #'   contains the summaries, labels, or scores to be validated
-#' @param llm_evidence **to be documented**
+#' @param llm_evidence a character vector; the name of an additional LLM output
+#' such as evidence or justifications provided by the LLM 
 #' @param verbose logical; output a progress indicator if `TRUE`
 #' @inheritParams ai_text
 #' @return character; the response from the manual validation with a length