Merge pull request #11 from edgararuiz/updates

Updates

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mall
 Title: Run multiple 'Large Language Model' predictions against a table, or 
     vectors
-Version: 0.0.0.9005
+Version: 0.0.0.9006
 Authors@R: 
     person("Edgar", "Ruiz", , "[email protected]", role = c("aut", "cre"))
 Description: Run multiple 'Large Language Model' predictions against a table. The

NAMESPACE

@@ -41,4 +41,5 @@ importFrom(jsonlite,write_json)
 importFrom(ollamar,chat)
 importFrom(ollamar,list_models)
 importFrom(ollamar,test_connection)
+importFrom(utils,head)
 importFrom(utils,menu)

R/llm-classify.R

@@ -4,16 +4,20 @@
 #' Use a Large Language Model (LLM) to classify the provided text as one of the
 #' options provided via the `labels` argument.
 #'
-#' @param .data A `data.frame` or `tbl` object that contains the text to be analyzed
+#' @param .data A `data.frame` or `tbl` object that contains the text to be
+#' analyzed
 #' @param col The name of the field to analyze, supports `tidy-eval`
 #' @param x A vector that contains the text to be analyzed
 #' @param additional_prompt Inserts this text into the prompt sent to the LLM
 #' @param pred_name A character vector with the name of the new column where the
 #' prediction will be placed
 #' @param labels A character vector with at least 2 labels to classify the text
 #' as
-#' @returns `llm_classify` returns a `data.frame` or `tbl` object. `llm_vec_classify`
-#' returns a vector that is the same length as `x`.
+#' @param preview It returns the R call that would have been used to run the
+#' prediction. It only returns the first record in `x`. Defaults to `FALSE`
+#' Applies to vector function only.
+#' @returns `llm_classify` returns a `data.frame` or `tbl` object.
+#' `llm_vec_classify` returns a vector that is the same length as `x`.
 #' @export
 llm_classify <- function(.data,
                          col,
@@ -43,12 +47,14 @@ llm_classify.data.frame <- function(.data,
 #' @export
 llm_vec_classify <- function(x,
                              labels,
-                             additional_prompt = "") {
-  l_vec_prompt(
+                             additional_prompt = "",
+                             preview = FALSE) {
+  m_vec_prompt(
     x = x,
     prompt_label = "classify",
     additional_prompt = additional_prompt,
     labels = labels,
-    valid_resps = labels
+    valid_resps = labels,
+    preview = preview
   )
 }

R/llm-custom.R

@@ -6,11 +6,11 @@
 #'
 #' @inheritParams llm_classify
 #' @param prompt The prompt to append to each record sent to the LLM
-#' @param valid_resps If the response from the LLM is not open, but deterministic,
-#' provide the options in a vector. This function will set to `NA` any response
-#' not in the options
-#' @returns `llm_custom` returns a `data.frame` or `tbl` object. `llm_vec_custom`
-#' returns a vector that is the same length as `x`.
+#' @param valid_resps If the response from the LLM is not open, but
+#' deterministic, provide the options in a vector. This function will set to
+#' `NA` any response not in the options
+#' @returns `llm_custom` returns a `data.frame` or `tbl` object.
+#' `llm_vec_custom` returns a vector that is the same length as `x`.
 #' @export
 llm_custom <- function(
     .data,
@@ -40,7 +40,7 @@ llm_custom.data.frame <- function(.data,
 #' @rdname llm_custom
 #' @export
 llm_vec_custom <- function(x, prompt = "", valid_resps = NULL) {
-  l_vec_prompt(
+  m_vec_prompt(
     x = x,
     prompt = prompt,
     valid_resps = valid_resps

R/llm-extract.R

@@ -11,8 +11,8 @@
 #' `labels` is a named vector, this function will use those names as the
 #' new column names, if not, the function will use a sanitized version of
 #' the content as the name.
-#' @returns `llm_extract` returns a `data.frame` or `tbl` object. `llm_vec_extract`
-#' returns a vector that is the same length as `x`.
+#' @returns `llm_extract` returns a `data.frame` or `tbl` object.
+#' `llm_vec_extract` returns a vector that is the same length as `x`.
 #' @export
 llm_extract <- function(.data,
                         col,
@@ -76,11 +76,13 @@ llm_extract.data.frame <- function(.data,
 #' @export
 llm_vec_extract <- function(x,
                             labels = c(),
-                            additional_prompt = "") {
-  l_vec_prompt(
+                            additional_prompt = "",
+                            preview = FALSE) {
+  m_vec_prompt(
     x = x,
     prompt_label = "extract",
     labels = labels,
-    additional_prompt = additional_prompt
+    additional_prompt = additional_prompt,
+    preview = preview
   )
 }

R/llm-sentiment.R

@@ -52,12 +52,14 @@ globalVariables("ai_analyze_sentiment")
 #' @export
 llm_vec_sentiment <- function(x,
                               options = c("positive", "negative", "neutral"),
-                              additional_prompt = "") {
-  l_vec_prompt(
+                              additional_prompt = "",
+                              preview = FALSE) {
+  m_vec_prompt(
     x = x,
     prompt_label = "sentiment",
     additional_prompt = additional_prompt,
     valid_resps = options,
-    options = options
+    options = options,
+    preview = preview
   )
 }

R/llm-summarize.R

@@ -51,11 +51,13 @@ globalVariables("ai_summarize")
 #' @export
 llm_vec_summarize <- function(x,
                               max_words = 10,
-                              additional_prompt = "") {
-  l_vec_prompt(
+                              additional_prompt = "",
+                              preview = FALSE) {
+  m_vec_prompt(
     x = x,
     prompt_label = "summarize",
     additional_prompt = additional_prompt,
-    max_words = max_words
+    max_words = max_words,
+    preview = preview
   )
 }

R/llm-translate.R

@@ -38,11 +38,13 @@ llm_translate.data.frame <- function(.data,
 llm_vec_translate <- function(
     x,
     language,
-    additional_prompt = "") {
-  l_vec_prompt(
+    additional_prompt = "",
+    preview = FALSE) {
+  m_vec_prompt(
     x = x,
     prompt_label = "translate",
     additional_prompt = additional_prompt,
-    language = language
+    language = language,
+    preview = preview
   )
 }

R/llm-use.R

@@ -13,8 +13,8 @@
 #' R session
 #' @param .cache The path to save model results, so they can be re-used if
 #' the same operation is ran again. To turn off, set this argument to an empty
-#' character: `""`. 'It defaults to '_mall_cache'. If this argument is left `NULL`
-#' when calling this function, no changes to the path will be made.
+#' character: `""`. 'It defaults to '_mall_cache'. If this argument is left
+#' `NULL` when calling this function, no changes to the path will be made.
 #'
 #' @returns A `mall_defaults` object
 #'
@@ -69,7 +69,7 @@ llm_use <- function(
       ...
     )
   }
-  if (!.silent | not_init) {
+  if (!.silent || not_init) {
     print(defaults_get())
   }
   invisible(defaults_get())

R/m-backend-prompt.R

@@ -8,13 +8,17 @@ m_backend_prompt <- function(backend, additional) {
 m_backend_prompt.mall_defaults <- function(backend, additional = "") {
   list(
     sentiment = function(options) {
-      options <- paste0(options, collapse = ", ")
+      options <- process_labels(
+        x = options,
+        if_character = "Return only one of the following answers: {x}",
+        if_formula = "- If the text is {f_lhs(x)}, return {f_rhs(x)}"
+      )
       list(
         list(
           role = "user",
           content = glue(paste(
             "You are a helpful sentiment engine.",
-            "Return only one of the following answers: {options}.",
+            "{options}.",
             "No capitalization. No explanations.",
             "{additional}",
             "The answer is based on the following text:\n{{x}}"
@@ -37,13 +41,17 @@ m_backend_prompt.mall_defaults <- function(backend, additional = "") {
       )
     },
     classify = function(labels) {
-      labels <- paste0(labels, collapse = ", ")
+      labels <- process_labels(
+        x = labels,
+        if_character = "Determine if the text refers to one of the following: {x}",
+        if_formula = "- For {f_lhs(x)}, return {f_rhs(x)}"
+      )
       list(
         list(
           role = "user",
           content = glue(paste(
             "You are a helpful classification engine.",
-            "Determine if the text refers to one of the following: {labels}.",
+            "{labels}.",
             "No capitalization. No explanations.",
             "{additional}",
             "The answer is based on the following text:\n{{x}}"
@@ -54,8 +62,6 @@ m_backend_prompt.mall_defaults <- function(backend, additional = "") {
     extract = function(labels) {
       no_labels <- length(labels)
       col_labels <- paste0(labels, collapse = ", ")
-      json_labels <- paste0("\"", labels, "\":your answer", collapse = ",")
-      json_labels <- paste0("{{", json_labels, "}}")
       plural <- ifelse(no_labels > 1, "s", "")
       text_multi <- ifelse(
         no_labels > 1,
@@ -94,55 +100,17 @@ m_backend_prompt.mall_defaults <- function(backend, additional = "") {
   )
 }
 
-l_vec_prompt <- function(x,
-                         prompt_label = "",
-                         additional_prompt = "",
-                         valid_resps = NULL,
-                         prompt = NULL,
-                         ...) {
-  # Initializes session LLM
-  backend <- llm_use(.silent = TRUE, .force = FALSE)
-  # If there is no 'prompt', then assumes that we're looking for a
-  # prompt label (sentiment, classify, etc) to set 'prompt'
-  if (is.null(prompt)) {
-    defaults <- m_backend_prompt(
-      backend = backend,
-      additional = additional_prompt
-    )
-    fn <- defaults[[prompt_label]]
-    prompt <- fn(...)
-  }
-  # If the prompt is a character, it will convert it to
-  # a list so it can be processed
-  if (!inherits(prompt, "list")) {
-    p_split <- strsplit(prompt, "\\{\\{x\\}\\}")[[1]]
-    if (length(p_split) == 1 && p_split == prompt) {
-      content <- glue("{prompt}\n{{x}}")
-    } else {
-      content <- prompt
-    }
-    prompt <- list(
-      list(role = "user", content = content)
-    )
-  }
-  # Submits final prompt to the LLM
-  resp <- m_backend_submit(
-    backend = backend,
-    x = x,
-    prompt = prompt
-  )
-  # Checks for invalid output and marks them as NA
-  if (!is.null(valid_resps)) {
-    errors <- !resp %in% valid_resps
-    resp[errors] <- NA
-    if (any(errors)) {
-      cli_alert_warning(
-        c(
-          "There were {sum(errors)} predictions with ",
-          "invalid output, they were coerced to NA"
-        )
-      )
-    }
+all_formula <- function(x) {
+  all(map_lgl(x, inherits, "formula"))
+}
+
+process_labels <- function(x, if_character = "", if_formula = "") {
+  if (all_formula(x)) {
+    labels_mapped <- map_chr(x, \(x) glue(if_formula))
+    out <- paste0(labels_mapped, collapse = ", ")
+  } else {
+    x <- paste0(x, collapse = ", ")
+    out <- glue(if_character)
   }
-  resp
+  out
 }