oreilly-retrieval-augmented-gen-ai and Controllable-RAG-Agent
About oreilly-retrieval-augmented-gen-ai
sinanuozdemir/oreilly-retrieval-augmented-gen-ai
See how to augment LLMs with real-time data for dynamic, context-aware apps - Rag + Agents + GraphRAG.
This project helps AI developers build applications that can answer questions using up-to-date, external information. You'll learn how to feed real-time data into large language models (LLMs) to get more accurate and context-aware responses. It's designed for developers with Python skills and some background in machine learning and natural language processing who want to create dynamic, intelligent applications.
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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