Structured data • ellmer

When using an LLM to extract data from text or images, you can ask the chatbot to format it in JSON or any other format that you like. This works well most of the time, but there’s no guarantee that you’ll get the exact format you want. In particular, if you’re trying to get JSON, you’ll find that it’s typically surrounded in ```json, and you’ll occasionally get text that isn’t valid JSON. To avoid these problems, you can use a recent LLM feature: structured data (aka structured output). With structured data, you supply the type specification that defines the object structure you want and the LLM ensures that’s what you’ll get back.

library(ellmer)

Structured data basics

To extract structured data you call the $extract_data() method instead of the $chat() method. You’ll also need to define a type specification that describes the structure of the data that you want (more on that shortly). Here’s a simple example that extracts two specific values from a string:

chat <- chat_openai()
#> Using model = "gpt-4o".
chat$extract_data(
  "My name is Susan and I'm 13 years old",
  type = type_object(
    age = type_number(),
    name = type_string()
  )
)
#> $age
#> [1] 13
#> 
#> $name
#> [1] "Susan"

The same basic idea works with images too:

chat$extract_data(
  content_image_url("https://www.r-project.org/Rlogo.png"),
  type = type_object(
    primary_shape = type_string(),
    primary_colour = type_string()
  )
)
#> $primary_shape
#> [1] "oval"
#> 
#> $primary_colour
#> [1] "grey"

Data types basics

To define your desired type specification (also known as a schema), you use the type_() functions. (You might already be familiar with these if you’ve done any function calling, as discussed in vignette("function-calling")). The type functions can be divided into three main groups:

Scalars represent five types of single values, type_boolean(), type_integer(), type_number(), type_string(), and type_enum(), which represent a single logical, integer, double, string, and factor value respectively.
Arrays represent any number of values of the same type. They are created with type_array(). You must always supply the item argument which specifies the type for each individual element. Arrays of scalars are very similar to R’s atomic vectors:
```
type_logical_vector <- type_array(items = type_boolean())
type_integer_vector <- type_array(items = type_integer())
type_double_vector <- type_array(items = type_number())
type_character_vector <- type_array(items = type_string())
```
You can also have arrays of arrays and arrays of objects, which more closely resemble lists with well defined structures:
```
list_of_integers <- type_array(items = type_integer_vector)
```
Objects represent a collection of named values. They are created with type_object(). Objects can contain any number of scalars, arrays, and other objects. They are similar to named lists in R.
```
type_person <- type_object(
  name = type_string(),
  age = type_integer(),
  hobbies = type_array(items = type_string())
)
```

Using these type specifications ensures that the LLM will return JSON. But ellmer goes one step further to convert the results to the closest R analog. Currently, this converts arrays of boolean, integers, numbers, and strings into logical, integer, numeric, and character vectors. Arrays of objects are converted into data frames. You can opt-out of this and get plain lists by setting convert = FALSE in $extract_data().

In addition to defining types, you need to provide the LLM with some information about what you actually want. This is the purpose of the first argument, description, which is a string that describes the data that you want. This is a good place to ask nicely for other attributes you’ll like the value to have (e.g. minimum or maximum values, date formats, …). There’s no guarantee that these requests will be honoured, but the LLM will usually make a best effort to do so.

type_type_person <- type_object(
  "A person",
  name = type_string("Name"),
  age = type_integer("Age, in years."),
  hobbies = type_array(
    "List of hobbies. Should be exclusive and brief.",
    items = type_string()
  )
)

Now we’ll dive into some examples before coming back to talk more about the details of data types.

Examples

The following examples, which are closely inspired by the Claude documentation, hint at some of the ways you can use structured data extraction.

Example 1: Article summarisation

text <- readLines(system.file("examples/third-party-testing.txt", package = "ellmer"))
# url <- "https://www.anthropic.com/news/third-party-testing"
# html <- rvest::read_html(url)
# text <- rvest::html_text2(rvest::html_element(html, "article"))

type_summary <- type_object(
  "Summary of the article.",
  author = type_string("Name of the article author"),
  topics = type_array(
    'Array of topics, e.g. ["tech", "politics"]. Should be as specific as possible, and can overlap.',
    type_string(),
  ),
  summary = type_string("Summary of the article. One or two paragraphs max"),
  coherence = type_integer("Coherence of the article's key points, 0-100 (inclusive)"),
  persuasion = type_number("Article's persuasion score, 0.0-1.0 (inclusive)")
)

chat <- chat_openai()
#> Using model = "gpt-4o".
data <- chat$extract_data(text, type = type_summary)
cat(data$summary)
#> The article from Anthropic underscores the critical need for a robust third-party testing regime for frontier AI systems like Claude, emphasizing this as vital for minimizing societal harm from AI misuse or accidents. A key argument is that existing sector-specific frameworks are inadequate to handle the vast potential and risks of contemporary and future AI models. Such a testing regime should involve collaborative inputs from industry, government, and academia, and should focus initially on large-scale, high-risk AI systems.
#> 
#> The article outlines the components of an ideal testing framework, such as effective and broadly-trusted tests administered by legitimate third parties, cooperation across national borders, and a balance of rigorous oversight with manageable administrative burden. Motivation is further provided by citing Anthropic's self-governance pathway, advocating that existing company-led safety measures are insufficient without an ecosystem of independent verification.
#> 
#> Anthropic projects an ecosystem where third-party testing complements sector-specific regulations, attributing safety challenges to potential emergent behaviors of AI systems that current frameworks may overlook. The organization also stresses the importance of government-sponsored research and evaluation to develop resilient AI safety standards, highlighting national security risks as an area necessitating government-led assessments.

str(data)
#> List of 5
#>  $ author    : chr "AI Policy Team at Anthropic"
#>  $ topics    : chr [1:5] "AI Policy" "AI Safety" "Third-Party Testing" "AI Governance" ...
#>  $ summary   : chr "The article from Anthropic underscores the critical need for a robust third-party testing regime for frontier A"| __truncated__
#>  $ coherence : int 85
#>  $ persuasion: num 0.92

Example 2: Named entity recognition

text <- "
  John works at Google in New York. He met with Sarah, the CEO of
  Acme Inc., last week in San Francisco.
"

type_named_entity <- type_object(
  name = type_string("The extracted entity name."),
  type = type_enum("The entity type", c("person", "location", "organization")),
  context = type_string("The context in which the entity appears in the text.")
)
type_named_entities <- type_array(items = type_named_entity)

chat <- chat_openai()
#> Using model = "gpt-4o".
chat$extract_data(text, type = type_named_entities)
#>            name         type                         context
#> 1          John       person     Works at Google in New York
#> 2        Google organization                Employer of John
#> 3      New York     location          Place where John works
#> 4         Sarah       person         Met with John last week
#> 5           CEO       person      Role of Sarah at Acme Inc.
#> 6     Acme Inc. organization  Company where Sarah is the CEO
#> 7 San Francisco     location Place where John met with Sarah

Example 3: Sentiment analysis

text <- "
  The product was okay, but the customer service was terrible. I probably
  won't buy from them again.
"

type_sentiment <- type_object(
  "Extract the sentiment scores of a given text. Sentiment scores should sum to 1.",
  positive_score = type_number("Positive sentiment score, ranging from 0.0 to 1.0."),
  negative_score = type_number("Negative sentiment score, ranging from 0.0 to 1.0."),
  neutral_score = type_number("Neutral sentiment score, ranging from 0.0 to 1.0.")
)

chat <- chat_openai()
#> Using model = "gpt-4o".
str(chat$extract_data(text, type = type_sentiment))
#> List of 3
#>  $ positive_score: num 0.1
#>  $ negative_score: num 0.7
#>  $ neutral_score : num 0.2

Note that while we’ve asked nicely for the scores to sum 1, which they do in this example (at least when I ran the code), this is not guaranteed.

Example 4: Text classification

text <- "The new quantum computing breakthrough could revolutionize the tech industry."

type_classification <- type_array(
  "Array of classification results. The scores should sum to 1.",
  type_object(
    name = type_enum(
      "The category name",
      values = c(
        "Politics",
        "Sports",
        "Technology",
        "Entertainment",
        "Business",
        "Other"
      )
    ),
    score = type_number(
      "The classification score for the category, ranging from 0.0 to 1.0."
    )
  )
)

chat <- chat_openai()
#> Using model = "gpt-4o".
data <- chat$extract_data(text, type = type_classification)
data
#>         name score
#> 1 Technology  0.85
#> 2   Business  0.10
#> 3      Other  0.05

Example 5: Working with unknown keys

type_characteristics <- type_object(
  "All characteristics",
  .additional_properties = TRUE
)

prompt <- "
  Given a description of a character, your task is to extract all the characteristics of that character.

  <description>
  The man is tall, with a beard and a scar on his left cheek. He has a deep voice and wears a black leather jacket.
  </description>
"

chat <- chat_claude()
#> Using model = "claude-3-7-sonnet-latest".
str(chat$extract_data(prompt, type = type_characteristics))
#>  list()

This example only works with Claude, not GPT or Gemini, because only Claude supports adding additional, arbitrary properties.

Example 6: Extracting data from an image

The final example comes from Dan Nguyen (you can see other interesting applications at that link). The goal is to extract structured data from this screenshot:

Screenshot of schedule A: a table showing assets and “unearned” income

Even without any descriptions, ChatGPT does pretty well:

type_asset <- type_object(
  assert_name = type_string(),
  owner = type_string(),
  location = type_string(),
  asset_value_low = type_integer(),
  asset_value_high = type_integer(),
  income_type = type_string(),
  income_low = type_integer(),
  income_high = type_integer(),
  tx_gt_1000 = type_boolean()
)
type_assets <- type_array(items = type_asset)

chat <- chat_openai()
#> Using model = "gpt-4o".
image <- content_image_file("congressional-assets.png")
data <- chat$extract_data(image, type = type_assets)
data
#>                                 assert_name owner
#> 1  11 Zinfandel Lane - Home & Vineyard [RP]    JT
#> 2 25 Point Lobos - Commercial Property [RP]    SP
#>                              location asset_value_low asset_value_high
#> 1             St. Helena/Napa, CA, US         5000001         25000000
#> 2 San Francisco/San Francisco, CA, US         5000001         25000000
#>   income_type income_low income_high tx_gt_1000
#> 1 Grape Sales     100001     1000000      FALSE
#> 2        Rent     100001     1000000      FALSE

Advanced data types

Now that you’ve seen a few examples, it’s time to get into more specifics about data type declarations.

Required vs optional

By default, all components of an object are required. If you want to make some optional, set required = FALSE. This is a good idea if you don’t think your text will always contain the required fields as LLMs may hallucinate data in order to fulfill your spec.

For example, here the LLM hallucinates a date even though there isn’t one in the text:

type_article <- type_object(
  "Information about an article written in markdown",
  title = type_string("Article title"),
  author = type_string("Name of the author"),
  date = type_string("Date written in YYYY-MM-DD format.")
)

prompt <- "
  Extract data from the following text:

  <text>
  # Structured Data
  By Hadley Wickham

  When using an LLM to extract data from text or images, you can ask the chatbot to nicely format it, in JSON or any other format that you like.
  </text>
"

chat <- chat_openai()
#> Using model = "gpt-4o".
chat$extract_data(prompt, type = type_article)
#> $title
#> [1] "Structured Data"
#> 
#> $author
#> [1] "Hadley Wickham"
#> 
#> $date
#> [1] "2023-10-11"
str(data)
#> 'data.frame':    2 obs. of  9 variables:
#>  $ assert_name     : chr  "11 Zinfandel Lane - Home & Vineyard [RP]" "25 Point Lobos - Commercial Property [RP]"
#>  $ owner           : chr  "JT" "SP"
#>  $ location        : chr  "St. Helena/Napa, CA, US" "San Francisco/San Francisco, CA, US"
#>  $ asset_value_low : int  5000001 5000001
#>  $ asset_value_high: int  25000000 25000000
#>  $ income_type     : chr  "Grape Sales" "Rent"
#>  $ income_low      : int  100001 100001
#>  $ income_high     : int  1000000 1000000
#>  $ tx_gt_1000      : logi  FALSE FALSE

Note that I’ve used more of an explict prompt here. For this example, I found that this generated better results and that it’s a useful place to put additional instructions.

If I let the LLM know that the fields are all optional, it’ll return NULL for the missing fields:

type_article <- type_object(
  "Information about an article written in markdown",
  title = type_string("Article title", required = FALSE),
  author = type_string("Name of the author", required = FALSE),
  date = type_string("Date written in YYYY-MM-DD format.", required = FALSE)
)
chat$extract_data(prompt, type = type_article)
#> $title
#> [1] "Structured Data"
#> 
#> $author
#> [1] "Hadley Wickham"
#> 
#> $date
#> NULL

Data frames

If you want to define a data frame like object, you might be tempted to create a definition similar to what R uses: an object (i.e., a named list) containing multiple vectors (i.e., an array):

type_my_df <- type_object(
  name = type_array(items = type_string()),
  age = type_array(items = type_integer()),
  height = type_array(items = type_number()),
  weight = type_array(items = type_number())
)

This, however, is not quite right becuase there’s no way to specify that each array should have the same length. Instead, you’ll need to turn the data structure “inside out” and create an array of objects:

type_my_df <- type_array(
  items = type_object(
    name = type_string(),
    age = type_integer(),
    height = type_number(),
    weight = type_number()
  )
)

If you’re familiar with the terms row-oriented and column-oriented data frames, this is the same idea. Since most languages don’t possess vectorisation like R, row-oriented structures tend to be much more common in the wild.

Token usage

name	input	output
OpenAI-api.openai.com/v1	6009	692
Claude	480	97