agentic-rag-for-dummies and oreilly-retrieval-augmented-gen-ai
One project provides a modular LangGraph-based Agentic RAG implementation for learning, while the other offers a broader educational resource demonstrating RAG with agents and GraphRAG, making them complementary for understanding and implementing Agentic RAG systems.
About agentic-rag-for-dummies
GiovanniPasq/agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
This project helps developers build advanced AI assistants that can intelligently answer questions using custom data. It takes your documents (like PDFs or Markdown files) and processes them into a searchable format, then uses an AI to interpret user questions, find relevant information, and generate clear, coherent answers. It's designed for AI developers or data scientists who want to create sophisticated conversational agents.
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.
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