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/>.

Anti-Grain Geometry (AGG) is a high-quality and
    high-performance 2D drawing library. The 'ragg' package provides a set
    of graphic devices based on AGG to use as alternative to the raster
    devices provided through the 'grDevices' package.

Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.

Provides tools for implementing Retrieval-Augmented
    Generation (RAG) workflows with Large Language Models (LLM). Includes
    functions for document processing, text chunking, embedding
    generation, storage management, and content retrieval. Supports
    various document types and embedding providers ('Ollama', 'OpenAI'),
    with 'DuckDB' as the default storage backend. Integrates with the
    'ellmer' package to equip chat objects with retrieval capabilities.
    Designed to offer both sensible defaults and customization options
    with transparent access to intermediate outputs.  For a review of
    retrieval-augmented generation methods, see Gao et al. (2023)
    "Retrieval-Augmented Generation for Large Language Models: A Survey"
    <doi:10.48550/arXiv.2312.10997>.

Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) 
  <doi:10.18637/jss.v102.i04> and associated publications.