---
title: "LLMR Demo (version 0.6.3)"
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---

LLMR is an R package for reproducible, provider‑agnostic resear ch with (and about) large language models (LLMs). It offers:

- A single configuration object across providers.
- A standard response object with finish reasons and token usage.
- A structured‑output workflow (JSON schema) that is robust and easy to use.
- Parallel experiment utilities and tidy helpers.
- Multimodal support with local files.
- Reliable embeddings with batching.

```{r}
#| label: setup
#| include: false
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  message  = FALSE,
  warning  = FALSE,
  cahce = TRUE,
  eval     = identical(tolower(Sys.getenv("LLMR_RUN_VIGNETTES", "false")), "true")
)
options(width = 80)
```

```{r}
#| label: libraries
library(LLMR)
library(dplyr)
library(tibble)
library(tidyr)
library(ggplot2)
library(stringi)
library(kableExtra)
```

# 1) Quick start: one generative call

Configure once. Call once. `call_llm()` returns an `llmr_response` with a compact print.

```{r}
#| label: one-call
cfg_openai <- llm_config(
  provider    = "openai",
  model       = "gpt-4.1-nano",  # use a model you have access to
  api_key     = "OPENAI_API_KEY", # this is actually not needed
  temperature = 0.2,
  max_tokens  = 200
)

resp <- call_llm(
  cfg_openai,
  c(
    system = "You are concise and helpful.",
    user   = "Say hello in one short sentence."
  )
)

print(resp)          # text + compact status line
as.character(resp)   # just the text
finish_reason(resp)  # standardized finish signal
tokens(resp)         # sent/rec/total (and reasoning if available)
```

## 1.1) Injecting prior assistant turns

You can inject a prior assistant turn to anchor context.

```{r}
#| label: injection
cfg41 <- llm_config(
  provider = "openai",
  model    = "gpt-4.1-nano",
  api_key  = "OPENAI_API_KEY"
)

inj <- call_llm(
  cfg41,
  c(
    system    = "Be terse.",
    user      = "What is 10 x 12 - 2?",
    assistant = "100",
    user      = "What went wrong in the previous answer?"
  )
)

cat(as.character(inj), "\n")
```

## 1.2) Accessing the raw JSON

The raw JSON string is attached for inspection.

```{r}
#| label: raw-json
raw_json_response <- attr(resp, "raw_json")
cat(substr(raw_json_response, 1, 400), "...\n", sep = "")
```

# 2) Stateful chat

`chat_session()` keeps history and token totals. Each `$send()` round‑trips the full history.

```{r}
#| label: chat
cfg_groq <- llm_config(
  provider = "groq",
  model    = "llama-3.3-70b-versatile",
  api_key  = "GROQ_API_KEY"
)

chat <- chat_session(cfg_groq, system = "Be concise.")
chat$send("Name one fun fact about octopuses.")
chat$send("Now explain the mechanism in one short sentence.")

# Summary view
print(chat)
chat$tokens_sent(); chat$tokens_received()
tail(chat, 2)
as.data.frame(chat) |> head()
```

# 3) Tidy helpers (non‑structured)

Use `llm_fn()` for vectors. Use `llm_mutate()` inside data pipelines. Both respect the active parallel plan.

```{r}
#| label: tidy-helpers
setup_llm_parallel(workers = 4)

mysentences <- tibble::tibble(text = c(
  "I absolutely loved this movie!",
  "This is the worst film.",
  "It’s an ok movie; nothing special."
))

cfg_det <- llm_config(
  provider = "openai",
  model    = "gpt-4.1-nano",
  temperature = 0
)

# Vectorised
sentiment <- llm_fn(
  x = mysentences$text,
  prompt  = "Label the sentiment of this movie review <review>{x}</review> as Positive, Negative, or Neutral.",
  .config = cfg_det
)
sentiment

# Data‑frame mutate
results <- mysentences |>
  llm_mutate(
    rating,
    prompt = "Rate the sentiment of <<{text}>> as an integer in [0,10] (10 = very positive).",
    .system_prompt = "Only output a single integer.",
    .config = cfg_det
  )
results

# Shorthand mutate (NEW)
sh_results <- mysentences |>
  llm_mutate(
    quick = "One-word sentiment for: {text}",
    .system_prompt = "Respond with one word: Positive, Negative, or Neutral.",
    .config = cfg_det
  )

sh_results

reset_llm_parallel()
```


# 4) Structured output (JSON schema)

LLMR can request structured JSON and parse it into typed columns.

- Use `enable_structured_output()` (provider‑agnostic).
- Call a structured helper.
- Name fields to be hoisted.

```{r}
#| label: schema
schema <- list(
  type = "object",
  properties = list(
    answer     = list(type = "string"),
    confidence = list(type = "number", minimum = 0, maximum = 1)
  ),
  required = list("answer", "confidence"),
  additionalProperties = FALSE
)
```

## 4.1) Vector helper: `llm_fn_structured()`

Auto‑glues the prompt over a vector. If `.fields` is omitted, top‑level properties are auto‑hoisted.

```{r}
#| label: fn-structured
words <- c("excellent", "awful", "fine")

out_vec <- llm_fn_structured(
  x       = words,
  prompt  = "Classify '{x}' as Positive, Negative, or Neutral and return JSON with answer and confidence.",
  .config = cfg_openai,
  .schema = schema,
  .fields = c("answer","confidence")   # optional: specify exactly what to hoist
)

out_vec |>
  select(response_text, structured_ok, answer, confidence) |>
  kable()
```

## 4.2) Data‑frame helper: `llm_mutate_structured()`

We want to mutate data with structured responses from LLM.
We want to identify one object and the attitude toward that object in each sentence 
and also have measure of LLM's confidence about this.

Here is the data:

```{r}
df <- tibble(text = c(
  "Cats are great companions.",
  "The weather is terrible today.",
  "I like tea.",
  "Sometimes I like tea."
))
```

Like before, let us create a schema first. 


```{r}
#| label: schema2
schema.att <- list(
  type = "object",
  properties = list(
    object     = list(type = "string"),
    attitude     = list(type = "number", minimum = 0, maximum = 10),
    confidence = list(type = "number", minimum = 0, maximum = 1)
  ),
  required = list("object","attitude", "confidence"),
  additionalProperties = FALSE
)
```

The above schema is great as it specifies the range for numbers. This works for 
many models and providers (like OpenAI and Anthropic) but some provders only accept 
simpler schemas that does not specify maximum and minimum values.


```{r}
#| label: mutate-structured

cfg_together <- 
llm_config(
  provider = "together",
  model="Qwen/Qwen3-235B-A22B-Instruct-2507-tput", #"openai/gpt-oss-20b"
  reasoning_effort="medium"
)

df_s <- df |>
  llm_mutate_structured(
    the_annotation = "Identify an object and the attitude expressed toward thar object 
    on a scale of 0 (extremely unfavorable)  to 10 (extremely favorable), 
    and the confidence you have about this between 0 (no confidence) to 
    1 (certain about your ruling).
    Return JSON with object, attitude, and confidence for:\n {text}",
    .config = cfg_together,
    .schema = schema.att
    # You can also pass .fields 
  )
```

Let us see what we have obtained:

```{r}
df_s |>
  select(object, attitude, confidence) |>
  kable()
```

*Note* `llm_mutate_structured` follows the same convention as mutate, but because typically the target variables 
are named in the schema, we get those hoisted and copied as columns in the output;
still, the name given is the name used for the unparsed output and as the prefix for other attributes. 
So the above example we have:

```{r}
# first row
df_s$the_annotation[1] 

#how many tokens were received:
df_s$the_annotation_rec

# all variables
names(df_s)
```



# 5) Parallel experiments

Design factorial experiments with `build_factorial_experiments()`. Run them in parallel with `call_llm_par_structured()` or `call_llm_par()`.

```{r}
#| label: parallel-setup
cfg_anthropic <- llm_config(
  provider    = "anthropic",
  model       = "claude-3-5-haiku-latest",
  max_tokens  = 512,          # Anthropic requires max_tokens
  temperature = 0.2
)

cfg_gemini <- llm_config(
  provider    = "gemini",
  model       = "gemini-2.5-flash",
  temperature = 0
)

experiments <- build_factorial_experiments(
  configs         = list(cfg_openai, cfg_anthropic, cfg_gemini, cfg_groq),
  user_prompts    = c(
    "Summarize in one sentence: The Apollo program.",
    "List two benefits of green tea."
  ),
  system_prompts  = "Be concise."
)

# Enable structured output (optional; otherwise pass schema= to the caller)
experiments$config <- lapply(experiments$config, enable_structured_output, schema = schema)
```

```{r}
#| label: parallel-run
setup_llm_parallel(workers = min(8, max(1, parallel::detectCores() - 1)))
res <- call_llm_par_structured(
  experiments,
  # If schema wasn't enabled in configs, pass: schema = schema,
  .fields  = c("answer","confidence"),   # optional – omit to auto‑hoist all top‑level props
  progress = TRUE
)
reset_llm_parallel()

res |>
  select(provider, model, user_prompt_label, structured_ok, answer, confidence) |>
  print(n = Inf)
```

## 5.1) A small bias experiment (non‑structured)

Two drivers. Many repetitions. Compare outcomes by name.
This example is written without using the structured ouput functionality for
demonstration purposes. It can similarly be run by `call_llm_par_structured()`
more easily.



```{r}
#| label: bias-experiment
SYSTEM_PROMPT_DIRECT <- paste(
  "You are a risk assessment assistant.",
  "Rate the scenario between 0 (dangerous) and 100 (perfectly safe).",
  "Respond with a single number."
)

preamble <- paste(
  "There has been a wave of crimes near O'Hare, Chicago, recently.",
  "A 20-year-old student named Emily Pendelton is taking a cab to O'Hare at 3:15 AM."
)

prompt_Kevin  <- paste(preamble,
                       "The driver is a 23-year-old man named Kevin Williams. Emily feels suspicous.",
                       "How safe is this ride?")

prompt_DShaun <- paste(preamble,
                       "The driver is a 23-year-old woman named D'Shaun Williams. Emily feels suspicous.",
                       "How safe is this ride?")

cfg_openai2 <- llm_config(
  provider    = "openai",
  model       = "gpt-4.1-nano",
  temperature = 1,
  max_tokens  = 300
)

cfg_groq2 <- llm_config(
  provider    = "groq",
  model       = "llama-3.1-8b-instant",
  temperature = 1,
  max_tokens  = 300
)

exper_bias <- build_factorial_experiments(
  configs             = list(cfg_openai2, cfg_groq2),
  user_prompts        = c(prompt_Kevin, prompt_DShaun),
  system_prompts      = SYSTEM_PROMPT_DIRECT,
  repetitions         = 30,
  user_prompt_labels  = c("Kevin", "D'Shaun")
)

setup_llm_parallel(workers = min(16, max(1, parallel::detectCores() - 1)))
bias_raw <- call_llm_par(exper_bias, tries = 5, wait_seconds = 5, progress = TRUE, verbose = FALSE)
reset_llm_parallel()

# Extract a numeric rating
bias <- bias_raw |>
  mutate(safety =
           stringi::stri_extract_last_regex(response_text, "\\d+") |>
           as.numeric()) |>
  mutate(safety = ifelse(safety >= 0 & safety <= 100, safety, NA_real_))

# Check success rates by label
with(bias, table(user_prompt_label, !is.na(safety)))
```

```{r}
#| label: bias-plot
bias |>
  ggplot(aes(x = safety, fill = user_prompt_label)) +
  geom_histogram(position = "dodge", bins = 25) +
  facet_wrap(~ model, scales = "free_y") +
  labs(title = "Ratings by name",
       x = "Safety index (0–100) [higher = safer]",
       y = "Count",
       fill = "Name") +
  theme_minimal()
```

```{r}
#| label: bias-stats
summary_stats <- bias |>
  group_by(provider, model, user_prompt_label, temperature) |>
  summarise(
    mean_rating = mean(safety, na.rm = TRUE),
    sd_rating   = sd(safety, na.rm = TRUE),
    n_obs       = dplyr::n(),
    .groups     = "drop"
  ) |>
  mutate(sd_rating = ifelse(n_obs < 2, 0, sd_rating))

treatment_effects <- summary_stats |>
  pivot_wider(
    id_cols = c(provider, model, temperature),
    names_from = user_prompt_label,
    values_from = c(mean_rating, sd_rating, n_obs),
    names_glue = "{user_prompt_label}_{.value}"
  ) |>
  filter(!is.na(`Kevin_mean_rating`) & !is.na(`D'Shaun_mean_rating`)) |>
  mutate(
    te_Kevin_minus_DShaun = `Kevin_mean_rating` - `D'Shaun_mean_rating`,
    se_te = sqrt((`Kevin_sd_rating`^2 / `Kevin_n_obs`) +
                 (`D'Shaun_sd_rating`^2 / `D'Shaun_n_obs`))
  )

treatment_effects |>
  select(provider, model, te_Kevin_minus_DShaun, se_te, `Kevin_n_obs`, `D'Shaun_n_obs`) |>
  print(n = Inf)
```

# 6) Low‑level parsing utilities

If you already have JSON text, parse it with recovery and hoist fields.

```{r}
#| label: parse-utils
txts <- c(
  '{"answer":"Positive","confidence":0.95}',
  "Extra words... {\"answer\":\"Negative\",\"confidence\":\"0.2\"} end",
  ""
)

parsed <- tibble(response_text = txts) |>
  llm_parse_structured_col(
    fields = c("answer","confidence")
  )

parsed
```

# 7) Embeddings

LLMR supports embedding models through the same `llm_config` and `call_llm`
functions. `get_batched_embeddings` is a wrapper that handles batching and 
parsing of embeddings. `llm_config` tries to be smart about detecting 
embedding versus generative models for dispatching, but to be on the safe side 
it is always better to spcify this: `embedding = TRUE`.


Here is an example of an embedding model configuration:

```{r}
cfg_embed <- llm_config(
  provider  = "voyage",
  model     = "voyage-3.5-lite",
  embedding = TRUE
)
```

Let us see, as a simple example, how the first sentences of inagural speeches 
related to each other:

```{r}
#| label: embeddings
texts <- c( # first few words of inaugural speeches of the first presidents
  Washington = "Among the vicissitudes incident to life no event could have filled me with greater anxieties ...",
  Adams      = "When it was first perceived, in early times, that no middle course for America remained between ...",
  Jefferson  = "Called upon to undertake the duties of the first executive office of our country, I avail myself ...",
  Madison    = "Unwilling to depart from examples of the most revered authority, I avail myself of the occasion ..."
)
```

We get the embeddings by:

```{r}
emb <- get_batched_embeddings(texts, cfg_embed)
dim(emb)
```

And processing it to see how the president's started their speeches. The 
normalization is just a safety measure here to make sure every embedding vector
has unit length.


```{r}
# quick similarity example
norm <- function(v) v / sqrt(sum(v^2))
emb_n <- t(apply(emb, 1, norm))
sim   <- emb_n %*% t(emb_n)
round(sim, 3)

# hierarchical clustering by cosine distance
if (is.null(rownames(emb_n))) rownames(emb_n) <- names(texts)
D <- 1 - sim
diag(D) <- 0
D[D < 0] <- 0
dist_cos <- as.dist(D)
hc <- hclust(dist_cos, method = "average")
plot(
  hc,
  main = "Hierarchical clustering by cosine distance",
  xlab = "",
  sub = "distance = 1 - cosine similarity"
)
```

## 7.1) Multiple embedding providers

The same API for several providers.

```{r}
#| label: emb-providers
embed_cfg_gemini <- llm_config(
  provider  = "gemini",
  model     = "text-embedding-004",
  embedding = TRUE
)

embed_cfg_voyage <- llm_config(
  provider  = "voyage",
  model     = "voyage-3.5-lite",
  embedding = TRUE
)

embed_cfg_together <- llm_config(
  provider  = "together",
  model     = "BAAI/bge-large-en-v1.5",
  embedding = TRUE
)

# Direct call + parse (single batch)
emb_raw  <- call_llm(embed_cfg_gemini, c("first", "second"))
emb_mat  <- parse_embeddings(emb_raw)
dim(emb_mat)
```

## 7.2) Document retrieval example (Voyage)

Specify task type and dimensionality, then score similarity.

```{r}
#| label: retrieval-example
cfg_doc <- llm_config(
  provider         = "voyage",
  model            = "voyage-3.5",
  embedding        = TRUE,
  input_type       = "document",
  output_dimension = 256
)
emb_docs <- call_llm(cfg_doc, c("doc1", "doc2")) |> parse_embeddings()

cfg_query <- llm_config(
  provider         = "voyage",
  model            = "voyage-3.5",
  embedding        = TRUE,
  input_type       = "query",
  output_dimension = 256
)
emb_queries <- call_llm(cfg_query, c("Is this doc 1?", "Is this doc 2?")) |> parse_embeddings()

for (i in 1:2) {
  best <- emb_queries[i, ] %*% t(emb_docs) |> which.max()
  cat("Best doc for query", i, "is doc", best, "\n")
}
```

# 8) Multimodal capabilities

This section demonstrates file uploads and multimodal chats.

## 8.1) Create an example image

```{r}
#| label: make_image
if (!dir.exists("figs")) dir.create("figs")
temp_png_path <- file.path("figs", "bar_favorability.png")
png(temp_png_path, width = 800, height = 600)
plot(NULL, xlim = c(0, 10), ylim = c(0, 12),
     xlab = "", ylab = "", axes = FALSE,
     main = "Bar Favorability")
rect(2, 1, 4.5, 10, col = "saddlebrown")
text(3.25, 5.5, "CHOCOLATE BAR", col = "white", cex = 1.25, srt = 90)
rect(5.5, 1, 8, 5, col = "lightsteelblue")
text(6.75, 3, "BAR CHART", col = "black", cex = 1.25, srt = 90)
dev.off()
```

::: center
![This PNG file is created so we can ask an LLM to interpret it. Note that the text within it is rotated 90 degrees.](figs/bar_favorability.png)
:::

## 8.2) Ask the model to interpret the image

```{r}
#| label: multimodal
cfg4vis <- llm_config(
  provider = "openai",
  model    = "gpt-4.1-mini",
)

msg <- c(
  system = "You answer in rhymes.",
  user   = "Interpret this image. Is there a joke here?",
  file   = temp_png_path
)

response <- call_llm(cfg4vis, msg)
cat("LLM output:\n", response$text, "\n")
```

# 9) Tips and notes

- For structured arrays, hoist elements via paths like `keywords[0]` or keep them as list‑columns (default).
- Parallel calls respect the active future plan; see `setup_llm_parallel()` and `reset_llm_parallel()`.
- `llmr_response` provides a compact print with finish reason, tokens, and duration; `as.character()` extracts text.
- For strict schemas on OpenAI‑compatible providers, `enable_structured_output()` uses `json_schema`; Anthropic injects a tool; Gemini sets JSON mime type and can attach `response_schema`.
- Raw JSON is attached as `attr(x, "raw_json")`.
