Structured output support

[dhicks]

This PR provides a minimal implementation of the approach to structured outputs I sketched in #39. Here's a not-quite-minimal working example of using structured outputs:

library(mall)

library(rjson) # For parsing structured JSON output

library(dplyr) # For efficiently wrangling parsed JSON columns
library(tidyr)
library(purrr)

# Define a JSON schema as a list to constrain a model's output
format <- list(
  type = "object",
  properties = list(
    name = list(type = "string"),
    capital = list(type = "string"),
    languages = list(type = "array",
                     items = list(type = "string")
    )
  ),
  required = list("name", "capital", "languages")
)

# Set up the model, passing `output` and `format`
llm_use('ollama', 'llama3.2', 
        output = 'structured', 
        format = format, 
        seed = 2025-03-23)

# Vectorized version can be piped directly into JSON parser
llm_vec_custom('Canada', 'tell me about the following country') |> 
  fromJSON()
# $name
# [1] "Canada"
# 
# $capital
# [1] "Ottawa"
# 
# $languages
# [1] "English"              "French"              
# [3] "indigenous languages"

# Data frame version requires more wrangling
dataf = data.frame(country = c('Canada', 'Mexico', 'United States'))

llm_custom(dataf, country, 'tell me about the following country') |>
  mutate(output = map(.pred, fromJSON)) |> 
  unnest_wider(output) |> 
  str()
# tibble [3 × 5] (S3: tbl_df/tbl/data.frame)
# $ country  : chr [1:3] "Canada" "Mexico" "United States"
# $ .pred    : chr [1:3] "{ \"name\": \"Canada\", \"capital\": \"Ottawa\", \"languages\": [\"English\",\"French\" , \"indigenous language"| __truncated__ "{\"name\": \"Mexico\", \"capital\": \"Mexico City\", \"languages\": [\"Spanish\", \"Maya\", \"Nahua\", \"Zapote"| __truncated__ "{\n\"name\": \"United States of America\",\n\"capital\": \"Washington D.C.\",\n\"languages\": [\"English\", \"S"| __truncated__
# $ name     : chr [1:3] "Canada" "Mexico" "United States of America"
# $ capital  : chr [1:3] "Ottawa" "Mexico City" "Washington D.C."
# $ languages:List of 3
# ..$ : chr [1:3] "English" "French" "indigenous languages"
# ..$ : chr [1:5] "Spanish" "Maya" "Nahua" "Zapotec" ...
# ..$ : chr [1:14] "English" "Spanish" "French" "Chinese" ...

Text outputs appear to be working as before. Four tests fail, three due to small differences in a code snapshot, eg, order of arguments:

• Failure (test-llm-classify.R:38:3): Preview works
• Failure (test-llm-use.R:28:3): Stops cache
• Failure (test-llm-verify.R:36:3): Preview works

The fourth test appears to relate to the number of objects in Ollama's cache:

• Failure (test-zzz-cache.R:3:3): Ollama cache exists and delete (actual is 61.0 vs. expected 59.0)

Since I'm not sure how these tests work, at this time I'm not going to update the snapshots or make other changes to the tests themselves. I'm also not adding a test for the structured output use case. It seems like it would be desirable to define functions llm_structured and llm_structured_vec specifically for vectored output, and then write tests for those.