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In Native RAG, the most common implementation nowadays, the user query is processed through a pipeline that includes retrieval, reranking, synthesis, and generation of a response.
This process leverages retrieval and generation-based methods to provide accurate and contextually relevant answers.
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Agentic RAG is an advanced, agent-based approach to question answering over multiple documents in a coordinated manner. It involves comparing different documents, summarizing specific documents, or comparing various summaries.
Agentic RAG is a flexible framework that supports complex tasks requiring planning, multi-step reasoning, tool use, and learning over time.
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- Document Agents: Each document is assigned a dedicated agent capable of answering questions and summarizing within its own document.
- Meta-Agent: A top-level agent manages all the document agents, orchestrating their interactions and integrating their outputs to generate a coherent and comprehensive response.
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- Autonomy: Agents act independently to retrieve, process, and generate information.
- Adaptability: The system can adjust strategies based on new data and changing contexts.
- Proactivity: Agents can anticipate needs and take preemptive actions to achieve goals.
Applications
Agentic RAG is particularly useful in scenarios requiring thorough and nuanced information processing and decision-making.
A few days ago, I discussed how the future of AI lies in AI Agents. RAG is currently the most popular use case, and with an agentic architecture, you will supercharge RAG!
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