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RAGFlowChainR is an R package that brings Retrieval-Augmented Generation (RAG) capabilities to R, inspired by LangChain. It enables intelligent retrieval of documents from a local vector store (DuckDB), optional web search, and seamless integration with Large Language Models (LLMs).
Features include:
Python version: RAGFlowChain
(PyPI)
GitHub (Python): RAGFlowChain
install.packages("RAGFlowChainR")To get the latest features or bug fixes, you can install the
development version of RAGFlowChainR from GitHub:
# If needed
install.packages("remotes")
remotes::install_github("knowusuboaky/RAGFlowChainR")See the full function reference or the package website for more details.
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")To persist across sessions, add these to your
~/.Renviron file.
library(RAGFlowChainR)
local_files <- c("tests/testthat/test-data/sprint.pdf",
"tests/testthat/test-data/introduction.pptx",
"tests/testthat/test-data/overview.txt")
website_urls <- c("https://www.r-project.org")
crawl_depth <- 1
response <- fetch_data(
local_paths = local_files,
website_urls = website_urls,
crawl_depth = crawl_depth
)response
#> source title ...
#> 1 documents/sprint.pdf <NA> ...
#> 2 documents/introduction.pptx <NA> ...
#> 3 documents/overview.txt <NA> ...
#> 4 https://www.r-project.org R: The R Project for Statistical Computing ...
#> ...
cat(response$content[1])
#> Getting Started with Scrum\nCodeWithPraveen.com ...con <- create_vectorstore("tests/testthat/test-data/my_vectors.duckdb", overwrite = TRUE)
docs <- data.frame(head(response)) # reuse from fetch_data()
insert_vectors(
con = con,
df = docs,
embed_fun = embed_openai(),
chunk_chars = 12000
)
build_vector_index(con, type = c("vss", "fts"))
response <- search_vectors(con, query_text = "Tell me about R?", top_k = 5)response
#> id page_content dist
#> 1 5 [Home]\nDownload\nCRAN\nR Project...\n... 0.2183
#> 2 6 [Home]\nDownload\nCRAN\nR Project...\n... 0.2183
#> ...
cat(response$page_content[1])
#> [Home]\nDownload\nCRAN\nR Project\nAbout R\nLogo\n...rag_chain <- create_rag_chain(
llm = call_llm,
vector_database_directory = "tests/testthat/test-data/my_vectors.duckdb",
method = "DuckDB",
embedding_function = embed_openai(),
use_web_search = FALSE
)
response <- rag_chain$invoke("Tell me about R")response
#> $input
#> [1] "Tell me about R"
#>
#> $chat_history
#> [[1]] $role: "human", $content: "Tell me about R"
#> [[2]] $role: "assistant", $content: "R is a programming language..."
#>
#> $answer
#> [1] "R is a programming language and software environment commonly used for statistical computing and graphics..."
cat(response$answer)
#> R is a programming language and software environment commonly used for statistical computing and graphics...call_llm(
prompt = "Summarize the capital of France.",
provider = "groq",
model = "llama3-8b",
temperature = 0.7,
max_tokens = 200
)chatLLMThe chatLLM
package (now available on CRAN 🎉) offers a modular interface for
interacting with LLM providers including OpenAI,
Groq, Anthropic,
DeepSeek, DashScope, and
GitHub Models.
install.packages("chatLLM")Features:
verbose = TRUE/FALSE)list_models()RAGFlowChainR.Renviron-based key managementMIT © Kwadwo Daddy Nyame Owusu Boakye
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.