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Agentic RAG: What is it and How it works?
- Agentic Retrieval-Augmented Generation (RAG) builds on the foundation of traditional RAG systems by introducing intelligent agents.
- Agentic RAG leverages agents to perform advanced tasks, making the system more interactive and responsive.
- Key components of Agentic RAG include retrieval, reasoning, planning, and generation agents.
- Single-agent systems and multi-agent systems are Agentic RAG design structures for different levels of complexity
- Agentic RAG systems steps include user query decomposition, data retrieval, information aggregation, and response generation.
- Agentic Retrieval-Augmented Generation (RAG) agents can be categorized based on their function, ranging from simple routing tasks to complex dynamic planning.
- Several frameworks facilitate development of Agentic RAG systems, such as CrewAI, AutoGen, LangChain, LlamaIndex, and LangGraph.
- Agentic RAG provides context-aware retrieval, advanced reasoning, and adaptive decision-making.
- Developers can ensure that Agentic RAG systems achieve their full potential in delivering dynamic, intelligent, and reliable AI solutions by careful planning, design, optimization, and the use of robust frameworks.
- Agentic RAG is poised to redefine how AI systems interact with data, tools, and users, offering a scalable and flexible solution for the ever-evolving needs of modern industries.
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