styler updates

articles/caching.qmd

@@ -80,10 +80,10 @@ combining the values in the request (`$request`).
 
 ```{r}
 jsonlite::read_json(
-  "_mall_cache/08/086214f2638f60496fd0468d7de37c59.json", 
-  simplifyVector = TRUE, 
+  "_mall_cache/08/086214f2638f60496fd0468d7de37c59.json",
+  simplifyVector = TRUE,
   flatten = TRUE
-  )
+)
 ```
 
 Re-running the same `mall` call, will complete significantly faster
@@ -150,7 +150,7 @@ process. To run this test, we will use the first 5 rows:
 tic()
 
 data_bookReviews |>
-  head(5)  |> 
+  head(5) |>
   llm_sentiment(review)
 
 toc()
@@ -174,7 +174,7 @@ original time!
 tic()
 
 data_bookReviews |>
-  head(5)  |> 
+  head(5) |>
   llm_sentiment(review)
 
 toc()
@@ -187,7 +187,7 @@ The other 5 will still be scored using their cached result
 tic()
 
 data_bookReviews |>
-  head(6)  |> 
+  head(6) |>
   llm_sentiment(review)
 
 toc()

articles/databricks.qmd

@@ -68,14 +68,14 @@ argument in the AI Summarize function:
 
 ```{r}
 tbl_reviews |>
-  llm_summarize(review, max_words = 5) |> 
+  llm_summarize(review, max_words = 5) |>
   show_query()
 ```
 
 `llm_classify()` for this back-end, will only accept unnamed options. 
 
 ```{r}
-tbl_reviews |> 
+tbl_reviews |>
   llm_classify(review, c("appliance", "computer"))
 ```
 

tests/testthat/test-llm-classify.R

@@ -67,4 +67,4 @@ test_that("Classify on Ollama works", {
       additional_prompt = "Consider all laptops as appliances."
     )
   )
-})
+})