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Automated content summarisation with an LLM

Source: R/tasks.R
llm_summarize.Rd

Submits each row of data to the Databoard summarize task, which asks an LLM to summarise the text in col. Optionally, a rules data frame can be provided to produce structured, per-category summaries. The databoard server pre-processes the rules to embed them in the prompt, and post-processes the LLM answer to split it into columns for a data frame.

Usage

llm_summarize(data, col, rules, options = list(), wait = 0)

Arguments

data

A data frame containing the texts to be summarised, or a data frame previously returned by llm_summarize() whose pending results should be fetched.

col

A column in data holding the input text. Tidy-evaluation is supported (pass the bare column name).

rules

Optional. Provide rules to produce multiple summaries. A data frame with the columns category, description, and example. One output is created for each category. The description should prompt what to summarize. Optionally, add an example of the expected output.

options

A named list of additional options passed to the Databoard server.

wait

Integer. Seconds to wait server-side per case for the task to complete before returning.

  • 0 (default): submit all tasks and return immediately with state PENDING. Fetch results later by calling llm_summarize(data) again.

  • > 0: wait up to that many seconds per case for the result.

Value

The input data frame with the columns .task_id and .task_state added. When results are available, they are unnested into additional result columns (e.g. llm_result).

Details

The function has two modes of operation, dispatched automatically:

  • Submit — if data does not yet contain a .task_id column, the texts in col are submitted as new summarisation tasks (via da_submit()).

  • Fetch — if data already contains a .task_id column (i.e. it was previously returned by llm_summarize()), the function fetches results for any tasks that are still pending (via da_fetch()). In this case, all other parameters are ignored.

Typical usage is to call llm_summarize() once to submit, and then call it repeatedly on the returned data frame until all results are in (see da_finished()).

To customize the prompts, provide them in the options:

options = list(
  prompts = list(
    system = "YOURSYSTEMPROMPT",
    user = "YOURUSERPROMPT"
  )
)

If the prompts are given as character vectors, their elements are collapsed using a line break (as in llm_prompt()).

If present in the prompts, the placeholder {{text}} is replaced by the value of the current case. The placeholder {{rules}} is replaced by a rule book generated from the rules data frame.

See the databoard documentation for further options.

See also

llm_prompt(), llm_code(), da_submit(), da_fetch()]

Examples

if (FALSE) { # \dontrun{
# Log in to the Databoard service (requires valid credentials)
da_login()

# Submit summarisation tasks
data <- llm_summarize(songs, text)

# Fetch results: call the same function again with the returned
# data frame as the only argument. Repeat until all results are in.
data <- llm_summarize(data)

# Inspect the summaries
head(data$llm_result)
} # }