Description
Retrieval-Augmented Generation (RAG) Workflows in R with Local and Web Search.
Description
Enables Retrieval-Augmented Generation (RAG) workflows in R by combining local vector search using 'DuckDB' with optional web search via the 'Tavily' API. Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW' (Hierarchical Navigable Small World) indexing, and modular large language model (LLM) invocation. Designed for advanced question-answering, chat-based applications, and production-ready AI pipelines. This package is the R equivalent of the 'python' package 'RAGFlowChain' available at <https://pypi.org/project/RAGFlowChain/>.
README.md
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.