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<p align="left">
<a href="https://r2r-docs.sciphi.ai"><img src="https://img.shields.io/badge/docs.sciphi.ai-3F16E4" alt="Docs"></a>
<a href="https://discord.gg/p6KqD2kjtB"><img src="https://img.shields.io/discord/1120774652915105934?style=social&logo=discord" alt="Discord"></a>
<a href="https://github.com/SciPhi-AI"><img src="https://img.shields.io/github/stars/SciPhi-AI/R2R" alt="Github Stars"></a>
<a href="https://github.com/SciPhi-AI/R2R/pulse"><img src="https://img.shields.io/github/commit-activity/w/SciPhi-AI/R2R" alt="Commits-per-week"></a>
<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-purple.svg" alt="License: MIT"></a>
</p>
<img src="../assets/r2r.png" alt="R2R Answer Engine">
<h3 align="center">
Ingesting a library
</h3>
# About
R2R was designed to bridge the gap between local LLM experimentation and scalable, production-ready Retrieval-Augmented Generation (RAG). R2R provides a comprehensive and SOTA RAG system for developers, built around a RESTful API for ease of use.
ingest_my_data.py in the directory [[ingesting]] shows a process you can use to place multiple pdf files into the RAGs context.
Because there are limits to how much data can be uploaded and processed by different LLM providers, there are wait times coded into the ingest process.
In the code there are many directories, because the upload limit was around 35 megabytes, and references existed across many years.
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