Retrieval-Augmented Generation (RAG) Workflows

Provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLM). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers ('Ollama', 'OpenAI'), with 'DuckDB' as the default storage backend. Integrates with the 'ellmer' package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs. For a review of retrieval-augmented generation methods, see Gao et al. (2023) "Retrieval-Augmented Generation for Large Language Models: A Survey" .

ragnar

ragnar is an R package that helps implement Retrieval-Augmented Generation (RAG) workflows. It focuses on providing a complete solution with sensible defaults, while still giving the knowledgeable user precise control over each step. We don’t believe that you can fully automate the creation of a good RAG system, so it’s important that ragnar is not a black box. ragnar is designed to be transparent. You can easily inspect outputs at intermediate steps to understand what’s happening.

Installation

You can install ragnar from CRAN with:

install.packages("ragnar")

You can install the development version from GitHub with:

# install.packages("pak")
pak::pak("tidyverse/ragnar")

Key Steps

1. Document Processing

ragnar works with a wide variety of document types, using MarkItDown to convert content to Markdown.

Key functions:

read_as_markdown(): Convert a file or URL to markdown
ragnar_find_links(): Find all links in a webpage

2. Text Chunking

Next we divide each document into chunks. ragnar defaults to a strategy that preserves some of the semantics of the document, but provides plenty of opportunities to tweak the approach.

Key functions:

markdown_chunk(): Full-featured chunker that identifies semantic boundaries and intelligently chunks text.

3. Context Augmentation (Optional)

RAG applications benefit from augmenting text chunks with additional context, such as document headings and subheadings. ragnar makes it easy to keep track of headings and subheadings as part of chunking.

markdown_chunk() automatically associates each chunk with the headings that are in scope for that chunk.

4. Embedding

ragnar can help compute embeddings for each chunk. The goal is for ragnar to provide access to embeddings from popular LLM providers.

Key functions:

embed_ollama()
embed_openai()
embed_bedrock()
embed_databricks()
embed_google_vertex()

Note that calling the embedding function directly is typically not necessary. Instead, the embedding function is specified when a store is first created, and then automatically called when needed by ragnar_retrieve() and ragnar_store_insert().

5. Storage

Processed data is stored in a format optimized for efficient searching, using DuckDB. allowing additional packages to implement support for different storage providers.

Key functions:

ragnar_store_create()
ragnar_store_connect()
ragnar_store_insert()

6. Retrieval

Given a prompt, retrieve related chunks based on embedding distance or bm25 text search.

Key functions:

ragnar_retrieve(): high-level function that performs both vss and bm25 search and de-overlaps retrieved results.
ragnar_retrieve_vss(): Retrieve using vss DuckDB extension
ragnar_retrieve_bm25(): Retrieve using full-text search DuckDB extension
chunks_deoverlap(): Consolidates retrieved chunks that overlap.

7. Chat Augmentation

ragnar can equip an ellmer::Chat object with a retrieve tool that enables an LLM to retrieve content from a store on-demand.

ragnar_register_tool_retrieve(chat, store).

Usage

Here’s an example of using ragnar to create a knowledge store from the R for Data Science (2e) book:

library(ragnar)

base_url <- "https://r4ds.hadley.nz"
pages <- ragnar_find_links(base_url, url_filter = \(url) {
  url[startsWith(url, "https://r4ds.hadley.nz")]
})

store_location <- "r4ds.ragnar.duckdb"

store <- ragnar_store_create(
  store_location,
  embed = \(x) ragnar::embed_openai(x, model = "text-embedding-3-small")
)

for (page in pages) {
  message("ingesting: ", page)
  chunks <- page |> read_as_markdown() |> markdown_chunk()
  ragnar_store_insert(store, chunks)
}
#> ingesting: https://r4ds.hadley.nz/arrow.html
#> ingesting: https://r4ds.hadley.nz/base-R.html
#> ingesting: https://r4ds.hadley.nz/communicate.html
#> ingesting: https://r4ds.hadley.nz/communication.html
#> ingesting: https://r4ds.hadley.nz/data-import.html
#> ingesting: https://r4ds.hadley.nz/data-tidy.html
#> ingesting: https://r4ds.hadley.nz/data-transform.html
#> ingesting: https://r4ds.hadley.nz/data-visualize.html
#> ingesting: https://r4ds.hadley.nz/databases.html
#> ingesting: https://r4ds.hadley.nz/datetimes.html
#> ingesting: https://r4ds.hadley.nz/EDA.html
#> ingesting: https://r4ds.hadley.nz/factors.html
#> ingesting: https://r4ds.hadley.nz/functions.html
#> ingesting: https://r4ds.hadley.nz/import.html
#> ingesting: https://r4ds.hadley.nz/intro.html
#> ingesting: https://r4ds.hadley.nz/iteration.html
#> ingesting: https://r4ds.hadley.nz/joins.html
#> ingesting: https://r4ds.hadley.nz/layers.html
#> ingesting: https://r4ds.hadley.nz/logicals.html
#> ingesting: https://r4ds.hadley.nz/missing-values.html
#> ingesting: https://r4ds.hadley.nz/numbers.html
#> ingesting: https://r4ds.hadley.nz/preface-2e.html
#> ingesting: https://r4ds.hadley.nz/program.html
#> ingesting: https://r4ds.hadley.nz/quarto-formats.html
#> ingesting: https://r4ds.hadley.nz/quarto.html
#> ingesting: https://r4ds.hadley.nz/rectangling.html
#> ingesting: https://r4ds.hadley.nz/regexps.html
#> ingesting: https://r4ds.hadley.nz/spreadsheets.html
#> ingesting: https://r4ds.hadley.nz/strings.html
#> ingesting: https://r4ds.hadley.nz/transform.html
#> ingesting: https://r4ds.hadley.nz/visualize.html
#> ingesting: https://r4ds.hadley.nz/webscraping.html
#> ingesting: https://r4ds.hadley.nz/whole-game.html
#> ingesting: https://r4ds.hadley.nz/workflow-basics.html
#> ingesting: https://r4ds.hadley.nz/workflow-help.html
#> ingesting: https://r4ds.hadley.nz/workflow-scripts.html
#> ingesting: https://r4ds.hadley.nz/workflow-style.html

ragnar_store_build_index(store)

Once the store is set up, you can then retrieve the most relevant text chunks.

#' ## Retrieving Chunks

library(ragnar)
store_location <- "r4ds.ragnar.duckdb"
store <- ragnar_store_connect(store_location, read_only = TRUE)

text <- "How can I subset a dataframe with a logical vector?"


#' # Retrieving Chunks
#' Once the store is set up, retrieve the most relevant text chunks like this:
(relevant_chunks <- ragnar_retrieve(store, text))

#> # A tibble: 3 × 9
#>   origin         doc_id chunk_id start   end cosine_distance bm25  context text 
#>   <chr>           <int> <list>   <int> <int> <list>          <lis> <chr>   <chr>
#> 1 https://r4ds.…      2 <int>     3237  4779 <dbl [1]>       <dbl> "# 27 … "```…
#> 2 https://r4ds.…     19 <int>     1622  4205 <dbl [2]>       <dbl> "# 12 … "```…
#> 3 https://r4ds.…     19 <int>    19388 21644 <dbl [2]>       <dbl> "# 12 … "Tha…



#' # Register ellmer tool
#' You can register an ellmer tool to let the LLM retrieve chunks.
system_prompt <- stringr::str_squish(
  "
  You are an expert R programmer and mentor. You are concise.

  Before responding, retrieve relevant material from the knowledge store. Quote or
  paraphrase passages, clearly marking your own words versus the source. Provide a
  working link for every source cited, as well as any additional relevant links.
  Do not answer unless you have retrieved and cited a source.
  "
)
chat <- ellmer::chat_openai(
  system_prompt,
  model = "gpt-5.2",
  api_args = list(
    reasoning = list(effort = "low"),
    text = list(verbosity = "low")
  )
)

ragnar_register_tool_retrieve(chat, store, top_k = 10)

chat$chat("How can I subset a dataframe?")
#> ◯ [tool call] search_store_001(text = c("R subset data frame using [ ,
#> subset(), dplyr filter select", ...)
#> ● #> [
#>   #> {
#>   #> "origin": "https://r4ds.hadley.nz/base-R.html",
#>   #> "doc_id": 2,
#>   #> "chunk_id": [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],
#>   #> …

#> ### Base R (works for `data.frame` and tibbles)
#> 
#> **Source (quoted/paraphrased):** *R for Data Science* explains that `[` 
#> “extract[s] sub-components from vectors and data frames” and that for data 
#> frames the key form is `df[rows, cols]`, with `df[rows, ]` selecting rows and 
#> `df[, cols]` selecting columns; it also shows examples like `df[df$x > 1, ]` 
#> for conditional row filtering, and notes `drop = FALSE` to keep a 1-column 
#> result as a data frame.  
#> Source: https://r4ds.hadley.nz/base-R.html
#> 
#> ```r
#> # rows by position
#> df[1:5, ]
#> 
#> # columns by name
#> df[, c("x", "y")]
#> 
#> # rows by condition (keep rows where x > 1)
#> df[df$x > 1, ]
#> 
#> # single column:
#> df[,"x"]              # may return a vector for data.frame
#> df[,"x", drop = FALSE]# always keep as data frame
#> ```
#> 
#> To pull a single column:
#> ```r
#> df$x        # or df[["x"]]
#> ```
#> 
#> ### `subset()` (base R convenience)
#> 
#> **Source (quoted/paraphrased):** *R for Data Science* notes base R also has 
#> `subset()` combining row filtering + column selection, e.g. `df |> subset(x > 
#> 1, c(y, z))`.  
#> Source: https://r4ds.hadley.nz/base-R.html
#> 
#> ```r
#> subset(df, x > 1, c(y, z))
#> ```
#> 
#> ### dplyr (tidyverse)
#> 
#> **Source (quoted/paraphrased):** *R for Data Science* describes `filter()` for 
#> keeping rows based on conditions and `select()` for choosing columns.  
#> Source: https://r4ds.hadley.nz/data-transform.html
#> 
#> ```r
#> library(dplyr)
#> 
#> df |> filter(x > 1)
#> df |> select(x, y)
#> df |> filter(x > 1) |> select(y, z)
#> ```
#> 
#> Additional links:
#> - Base `[` help: https://rdrr.io/r/base/Extract.html
#> - Base `subset()` help: https://rdrr.io/r/base/subset.html
#> - dplyr `filter()`: https://dplyr.tidyverse.org/reference/filter.html
#> - dplyr `select()`: https://dplyr.tidyverse.org/reference/select.html