GraphRAG, Outperforms Traditional RAG ( Retrieval-Augmented Generation ) - Solve Black-box hallucination by Adding Knowledge to GenAI: Opensource AI . of data discovery and to enhance LLMs

Khan, Furqan

ISBN 13: 9798340292643

Edité par Independently published, 2024

A propos de cet article

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Détails bibliographiques

• Titre: GraphRAG, Outperforms Traditional RAG ( Retrieval-Augmented Generation ) - Solve Black-box hallucination by Adding Knowledge to GenAI: Opensource AI . of data discovery and to enhance LLMs
• Éditeur: Independently published
• Date de parution: 2024
• Langue: anglais
• ISBN à 13 chiffres: 9798340292643
• Reliure: paperback
• État de l'article: Good

A propos de ce titre

Synopsis

Retrieval Augmented Generation (RAG) has dominated the discussion around making GenAI applications useful since ChatGPT’s advent exploded the AI hype

In recent evaluations, GraphRAG demonstrated its ability to answer “global questions” that address the entire dataset, a task where naive RAG approaches often fail.

“We need an alternative retrieval method that allows us to answer these “Global”, aggregative questions in addition to the “Local” extractive questions”. Welcome to Graph RAG!

GraphRAG, Outperforms traditional RAG ( Retrieval-Augmented Generation ) for Query Focused Summarization

Opensource research of Knowledge Graph to support human sensemaking, improving the accuracy of data discovery, solving RAG pain points, and to enhance LLMs ( Large Language Models )

• Cost-effective solutions with opensource
• AI Agent PDF , private knowledge, local LLMs, Langchain, LlamaIndex
• The GraphRAG Manifesto: Adding Knowledge to GenAI
• Solving Black-box hallucination

Topics:

• GraphRAG
• Problems with LLMs (Large Language Models)
• RAG Systemise: creation of intelligent natural language processing (NLP) models
• RAG (Retrieval Augmented Generation)
• RAG Core
• Vector Databases are Amazingly Great
• The Retrieval Augmented Generation (RAG) Pipeline
• Research for challenges & Solutions
• RAG Benefits
• Limitations Of RAG
• Knowledge Graphs and Large Language Models (LLMs) Together at the Enterprise Level
• How to Implement Graph RAG Using Knowledge Graphs and Vector Databases
• Build Semantic Search LLM application with RAG - case-study of PDF AI Agent
• Knowledge extraction and ingestion For Graph Database
• Microsoft Supercharges RAG with Knowledge Graphs
• Embeddings and Vector Search in LLMs
• Research For LLMs & RAG With Questions Answered
• AI Chatbots and RAG
• OpenSource Graph Databases
• OpenSource LLMs
• Key Challenges and Future Insights

This computer science book is for programmers, researchers and developers who want to understand the machine learning techniques and advancement for Generative AI and Large Language Models (LLMs) specifically the recent GraphRAG.

Whether you are a beginner looking to learn the most latest practices for LLMs concisely or an experienced programmer looking to explore cutting-edge topics in data science, machine learning, and AI Models, you'll find this book useful.

Basic Python programming experience, machine learning concepts and knowledge of LLMs ( Large Language Models ) is a must, knowledge of data science will be helpful but not necessary.