Skill

r-ragnar

Use when code loads or uses ragnar (library(ragnar), ragnar::), implementing RAG in R, creating vector stores, embedding documents for semantic search, or building retrieval pipelines

npx claudepluginhub arthurgailes/r-package-skills --plugin r-package-skills

Tool Access

This skill uses the workspace's default tool permissions.

Preview

**ragnar implements RAG in R.** Create vector stores, embed documents, register retrieval tools with ellmer chat sessions. LLMs search your documents before answering.

Supporting Assets

references/API.mdreferences/package-docs.mdreferences/rag.md

SKILL.md

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r-package-skills

rag-implementation

Build RAG systems for LLM apps using vector databases, embeddings, and retrieval strategies. Use for document Q&A, grounded chatbots, and semantic search.

llm-application-dev

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Last CommitApr 24, 2026

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ragnar: Retrieval Augmented Generation in R

Overview

ragnar implements RAG in R. Create vector stores, embed documents, register retrieval tools with ellmer chat sessions. LLMs search your documents before answering.

Install: install.packages("ragnar")

References

Read references/API.md before writing code.

references/API.md - Complete function reference
references/package-docs.md - Vector store setup and usage
references/rag.md - RAG patterns and ellmer integration

When to Use

LLM needs to search your documentation
Implement semantic search over documents
Create vector database from documents
RAG workflows in R

When NOT to Use

Just need keyword search (use grep/Grep tool)
Documents fit in single prompt (<100k tokens)
Building chatbot without document search (use r-ellmer only)

Quick Reference

library(ragnar)

# Create store
docs <- read_as_markdown("docs/")
chunks <- markdown_chunk(docs)
store <- ragnar_store_create("docs.duckdb")
ragnar_store_insert(store, chunks)

# Register with ellmer
library(ellmer)
chat <- chat_openai()
ragnar_register_tool_retrieve(chat, store)

# Now chat searches docs before answering
chat$chat("What does the documentation say about...?")

# Local embeddings (Ollama)
store <- ragnar_store_create("local.duckdb",
  embed = ragnar_embed_ollama(model = "nomic-embed-text"))

Common Mistakes

Issue	Solution
Embedding mismatch	Store and retrieval must use same provider
Store not registered	Call `ragnar_register_tool_retrieve(chat, store)`
Ollama embeddings not working	Start `ollama serve` first
Poor retrieval quality	Check chunking strategy, embedding model

Core Functions

Store Management:

ragnar_store_create(): Create vector database
ragnar_store_connect(): Connect to existing store
ragnar_store_insert(): Add documents

Document Processing:

read_as_markdown(): Read documents
markdown_chunk(): Split into chunks

Integration:

ragnar_register_tool_retrieve(): Register with ellmer
ragnar_embed_ollama(): Local embeddings

Advanced

See references/ for:

API.md: Complete function reference
rag.md: RAG workflow details
Package docs: Full package documentation

Integration

With ellmer: ragnar_register_tool_retrieve(chat, store) With vitals (evaluation): See r-vitals skill for RAG testing Cross-package patterns: See r-ai meta-skill