Enables examples run, updates template to freeze results, adds exampl…

…e to sentiment, updates freeze

.gitignore

@@ -46,3 +46,6 @@ po/*~
 rsconnect/
 
 /.quarto/
+
+docs/
+

R/llm-sentiment.R

@@ -49,6 +49,20 @@ llm_sentiment.data.frame <- function(.data,
 globalVariables("ai_analyze_sentiment")
 
 #' @rdname llm_sentiment
+#'@examples
+#'\dontrun{
+#'library(mall)
+#'
+#'llm_use("ollama", "llama3.1", seed = 100, .silent = TRUE)
+#'
+#'llm_vec_sentiment("I am happy")
+#'
+#'# Specify values to return per sentiment
+#'llm_vec_sentiment("I am happy", c("positive" ~ 1, "negative" ~ 0))
+#'
+#'llm_vec_sentiment("I am sad", c("positive" ~ 1, "negative" ~ 0))
+#'
+#'}
 #' @export
 llm_vec_sentiment <- function(x,
                               options = c("positive", "negative", "neutral"),

_freeze/reference/llm_sentiment/execute-results/html.json

@@ -0,0 +1,15 @@
+{
+  "hash": "ffb23648b4f6280b3e4c6a87d85973f4",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: \"Sentiment analysis\"\nexecute:\n  eval: true\n  freeze: true\n---\n\n\n\n[R/llm-sentiment.R](https://github.com/edgararuiz/mall/blob/main/R/llm-sentiment.R)\n\n## llm_sentiment\n\n## Description\n Use a Large Language Model (LLM) to perform sentiment analysis from the provided text \n\n\n## Usage\n```r\n \nllm_sentiment( \n  .data, \n  col, \n  options = c(\"positive\", \"negative\", \"neutral\"), \n  pred_name = \".sentiment\", \n  additional_prompt = \"\" \n) \n \nllm_vec_sentiment( \n  x, \n  options = c(\"positive\", \"negative\", \"neutral\"), \n  additional_prompt = \"\", \n  preview = FALSE \n) \n```\n\n## Arguments\n|Arguments|Description|\n|---|---|\n| .data | A `data.frame` or `tbl` object that contains the text to be analyzed |\n| col | The name of the field to analyze, supports `tidy-eval` |\n| options | A vector with the options that the LLM should use to assign a sentiment to the text. Defaults to: 'positive', 'negative', 'neutral' |\n| pred_name | A character vector with the name of the new column where the prediction will be placed |\n| additional_prompt | Inserts this text into the prompt sent to the LLM |\n| x | A vector that contains the text to be analyzed |\n| 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. |\n\n\n\n## Value\n `llm_sentiment` returns a `data.frame` or `tbl` object. `llm_vec_sentiment` returns a vector that is the same length as `x`. \n\n\n## Examples\n\n\n::: {.cell}\n\n```{.r .cell-code}\n \nlibrary(mall) \n \nllm_use(\"ollama\", \"llama3.1\", seed = 100, .silent = TRUE) \n \nllm_vec_sentiment(\"I am happy\") \n#> [1] \"positive\"\n \n# Specify values to return per sentiment \nllm_vec_sentiment(\"I am happy\", c(\"positive\" ~ 1, \"negative\" ~ 0)) \n#> [1] 1\n \nllm_vec_sentiment(\"I am sad\", c(\"positive\" ~ 1, \"negative\" ~ 0)) \n#> [1] 0\n```\n:::\n",
+    "supporting": [],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
+  }
+}

reference/llm_classify.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Categorize data as one of options given"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-classify.R](https://github.com/edgararuiz/mall/blob/main/R/llm-classify.R)

reference/llm_custom.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Send a custom prompt to the LLM"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-custom.R](https://github.com/edgararuiz/mall/blob/main/R/llm-custom.R)

reference/llm_extract.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Extract entities from text"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-extract.R](https://github.com/edgararuiz/mall/blob/main/R/llm-extract.R)

reference/llm_sentiment.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Sentiment analysis"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-sentiment.R](https://github.com/edgararuiz/mall/blob/main/R/llm-sentiment.R)
@@ -46,5 +49,21 @@ llm_vec_sentiment(
  `llm_sentiment` returns a `data.frame` or `tbl` object. `llm_vec_sentiment` returns a vector that is the same length as `x`. 
 
 
+## Examples
+```{r}
+ 
+library(mall) 
+ 
+llm_use("ollama", "llama3.1", seed = 100, .silent = TRUE) 
+ 
+llm_vec_sentiment("I am happy") 
+ 
+# Specify values to return per sentiment 
+llm_vec_sentiment("I am happy", c("positive" ~ 1, "negative" ~ 0)) 
+ 
+llm_vec_sentiment("I am sad", c("positive" ~ 1, "negative" ~ 0)) 
+ 
+ 
+```
 
 

reference/llm_summarize.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Summarize text"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-summarize.R](https://github.com/edgararuiz/mall/blob/main/R/llm-summarize.R)

reference/llm_translate.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Translates text to a specific language"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-translate.R](https://github.com/edgararuiz/mall/blob/main/R/llm-translate.R)

reference/llm_use.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Specify the model to use"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/llm-use.R](https://github.com/edgararuiz/mall/blob/main/R/llm-use.R)

reference/m_backend_submit.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Functions to integrate different back-ends"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [R/m-backend-prompt.R, R/m-backend-submit.R](https://github.com/edgararuiz/mall/blob/main/R/m-backend-prompt.R, R/m-backend-submit.R)

utils/qmd-reference/reference.R

@@ -1,6 +1,7 @@
 source("utils/qmd-reference/utils-reference-index.R")
 source("utils/qmd-reference/utils-reference-list.R")
 source("utils/qmd-reference/utils-reference-pages.R")
+source("utils/qmd-reference/utils-context.R")
 source("utils/qmd-reference/pkgdown.R")
 library(pkgdown)
 library(magrittr)

utils/qmd-reference/reference.qmd

@@ -1,5 +1,8 @@
 ---
 title: "{{{{notitle.title}}}}"
+execute:
+  eval: true
+  freeze: true
 ---
 
 [{{{{notitle.source}}}}](https://github.com/edgararuiz/mall/blob/main/{{{{notitle.source}}}})

utils/qmd-reference/utils-context.R

@@ -0,0 +1,64 @@
+ecodown_context <- new.env(parent = emptyenv())
+
+ecodown_context_set <- function(id, vals = list()) {
+  ecodown_context[[id]] <- vals
+}
+
+ecodown_context_get <- function(id) {
+  if (id == "") {
+    return(NULL)
+  }
+  ecodown_context[[id]]
+}
+
+get_verbosity <- function() {
+  x <- ecodown_context_get("verbosity")
+  if (is.null(x)) x <- "verbose"
+  x[[1]]
+}
+
+set_verbosity <- function(x) {
+  ecodown_context_set("verbosity", x[[1]])  
+  invisible()
+}
+
+is_summary <- function() {
+  get_verbosity() == "summary"
+}
+
+get_clone_header <- function() {
+  x <- ecodown_context_get("clone_header")
+  if (is.null(x)) x <- 0
+  x[[1]]
+}
+
+clone_header <- function() {
+  get_clone_header() == 0
+}
+
+set_clone_header <- function(x = 1) {
+  ecodown_context_set("clone_header", x)
+}
+
+get_package_header <- function() {
+  x <- ecodown_context_get("package_header")
+  if (is.null(x)) x <- 0
+  x[[1]]
+}
+
+package_header <- function() {
+  get_package_header() == 0
+}
+
+set_package_header <- function(x = 1) {
+  ecodown_context_set("package_header", x)
+}
+
+set_package_name <- function(x) {
+  ecodown_context_set("package_name", x)
+}
+
+get_package_name <- function() {
+  ecodown_context_get("package_name")
+}
+

utils/qmd-reference/utils-reference-pages.R

@@ -13,6 +13,8 @@ reference_to_qmd <- function(file_in, pkg, template = NULL) {
 
   template |>
     map(parse_line_tag, con) %>%
+    discard(is.null) %>%
+    list_flatten() %>%
     list_c() %>%
     as.character()
 }
@@ -119,10 +121,10 @@ reference_convert <- function(x, output = "qmd") {
       } else {
         out <- list()
         if (!is.null(curr$code_run)) {
-          out <- c(out, "```{r, eval=ecodown::examples_run()}", curr$code_run, "```")
+          out <- c(out, "```{r}", curr$code_run, "```")
         }
         if (!is.null(curr$code_dont_run)) {
-          out <- c(out, "```{r, eval=ecodown::examples_not_run()}", curr$code_dont_run, "```")
+          out <- c(out, "```{r}", curr$code_dont_run, "```")
         }
       }
     }