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>.

Provides tools for document ingestion, embedding storage,
    retrieval-augmented generation (RAG), and evaluation of question-answering
    systems. The package includes an R-native vector store, wrappers for OpenAI
    embedding and chat-completion application programming interfaces (APIs),
    question-answering logging utilities, and large language model (LLM)-based
    evaluation metrics for context precision, context recall, answer relevance,
    and faithfulness. These metrics are based on the Retrieval-Augmented
    Generation Assessment (RAGAS) framework. The retrieval-augmented generation
    methodology is described by Lewis et al. (2020) "Retrieval-Augmented
    Generation for Knowledge-Intensive NLP Tasks"
    <doi:10.48550/arXiv.2005.11401>. The evaluation metrics are based on
    Es et al. (2024) "RAGAS: Automated Evaluation of Retrieval Augmented
    Generation" <doi:10.18653/v1/2024.eacl-demo.16>.