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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. \ No newline at end of file