ragbits and Controllable-RAG-Agent
These are complements: ragbits provides the foundational building blocks and framework for constructing RAG systems, while Controllable-RAG-Agent offers a specialized, graph-based agentic RAG implementation that could be built on top of or integrated alongside ragbits' components.
About ragbits
deepsense-ai/ragbits
Building blocks for rapid development of GenAI applications
This project offers robust building blocks for quickly creating Generative AI applications. It allows you to feed various document types, like PDFs and spreadsheets, into an AI system to get accurate, context-aware answers. It's designed for AI developers and engineers looking to build scalable and reliable AI assistants, chatbots, or intelligent search tools.
About Controllable-RAG-Agent
NirDiamant/Controllable-RAG-Agent
This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.
This project helps people answer complex questions from their documents, like research papers or books, even when the answer isn't obvious. You provide your documents and ask a question, and it gives you a well-reasoned answer based only on your data. Anyone who needs to extract precise, detailed answers from large amounts of text, such as researchers, analysts, or educators, would find this useful.
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