An extension for Oobabooga's Text-Generation Web UI that retrieves and adds web content to the context of prompts for more informative AI responses.
Walkthrough on building a Q and A pipeline using various tools, and distributing it with ModelKits for collaboration.
A method that uses instruction tuning to adapt LLMs for knowledge-intensive tasks. RankRAG simultaneously trains the models for context ranking and answer generation, enhancing their retrieval-augmented generation (RAG) capabilities.
NVIDIA and Georgia Tech researchers introduce RankRAG, a novel framework instruction-tuning a single LLM for top-k context ranking and answer generation. Aiming to improve RAG systems, it enhances context relevance assessment and answer generation.
This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
R2R is an open-source AI-powered answer engine that provides a comprehensive and SOTA RAG system for developers. It allows for multimodal support, hybrid search, graph RAG, app management, and more.
A mini python based tool designed to convert various types of files and GitHub repositories into LLM-ready Markdown documents with metadata, table of contents, and consistent heading styles. Supports multiple file types, handles zip files, and has GitHub integration.
A post discussing new techniques developed for parsing and searching PDFs, focusing on turning them into a hierarchical structure for RAG search. The approach involves dynamically generating chunks for searches, sending headers and sub-headers to the Language Model along with relevant chunks.
The llmsherpa project provides APIs to accelerate Large Language Model (LLM) projects. It includes features like LayoutPDFReader for PDF text parsing, smart chunking for vector search and Retrieval Augmented Generation, and table analysis. It is open-sourced under Apache 2.0 license.
A collection of RAG techniques to help you develop your RAG app into something robust that will last
