data_fetcher

Provides the `fetch_data()` function, which extracts and structures content from:<ul>
<li>Local files (PDF, DOCX, PPTX, TXT, HTML)</li>
<li>Crawled websites (with optional BFS crawl depth)</li>
</ul>The returned data frame includes metadata columns like `title`, `author`, `publishedDate`,
and the main extracted `content`.
## Required Packages
<code>install.packages(c("pdftools", "officer", "rvest", "xml2", "dplyr", "stringi", "curl", "httr", "jsonlite", "magrittr"))</code>

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/>.

Kwadwo Daddy Nyame Owusu Boakye

RAGFlowChainR

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

data_fetcher function

Provides the `fetch_data()` function, which extracts and structures content from:<ul>
<li>Local files (PDF, DOCX, PPTX, TXT, HTML)</li>
<li>Crawled websites (with optional BFS crawl depth)</li>
</ul>

The returned data frame includes metadata columns like `title`, `author`, `publishedDate`,
and the main extracted `content`.
## Required Packages
<code>install.packages(c("pdftools", "officer", "rvest", "xml2", "dplyr", "stringi", "curl", "httr", "jsonlite", "magrittr"))</code>

data_fetcher: data_fetcher.R Overview

Description

Arguments