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>.
Package details |
|
---|---|
Author | Tomasz Kalinowski [aut, cre], Daniel Falbel [aut], Posit Software, PBC [cph, fnd] (ROR: <https://ror.org/03wc8by49>) |
Maintainer | Tomasz Kalinowski <tomasz@posit.co> |
License | MIT + file LICENSE |
Version | 0.2.0 |
URL | https://ragnar.tidyverse.org/ https://github.com/tidyverse/ragnar |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.