Retrieval-Augmented Generation (RAG) Workflows in R with Local and Web Search • RAGFlowChainR

Overview

RAGFlowChainR is an R package for Retrieval-Augmented Generation (RAG) workflows with local retrieval backends (DuckDB and VectrixDB) plus optional web search.

The README is intentionally short. Full backend workflows are documented in vignettes.

Installation

install.packages("RAGFlowChainR")

Development version

install.packages("remotes")
remotes::install_github("knowusuboaky/RAGFlowChainR")

Backend Guides

DuckDB backend article: https://knowusuboaky.github.io/RAGFlowChainR/articles/duckdb-backend.html
VectrixDB backend article: https://knowusuboaky.github.io/RAGFlowChainR/articles/vectrixdb-backend.html
Function reference: https://knowusuboaky.github.io/RAGFlowChainR/reference

Quick Start

library(RAGFlowChainR)

rag <- create_rag_chain(
  llm = function(prompt) "mock answer",
  vector_database_directory = "my_vectors.duckdb",
  method = "DuckDB",
  use_web_search = FALSE
)

rag$invoke("What is RAG?")
rag$disconnect()

For complete ingestion, indexing, and backend-specific setup, use the two backend vignettes above.

Environment Setup

Sys.setenv(TAVILY_API_KEY    = "your-tavily-api-key")
Sys.setenv(OPENAI_API_KEY    = "your-openai-api-key")
Sys.setenv(GROQ_API_KEY      = "your-groq-api-key")
Sys.setenv(ANTHROPIC_API_KEY = "your-anthropic-api-key")

License

MIT (c) Kwadwo Daddy Nyame Owusu Boakye