Agentic Solution
A RAG (Retriever-augmented Generation) system with a multi-agent setup refers to combining retrieval-based methods (which use external information) with generative models, while employing multiple agents to collaboratively or independently handle tasks. This setup can be applied in various areas like natural language processing (NLP), robotics, or decision-making systems.
Process
User Query
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Agent Coordinator → Query Routing
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Query Encoder Other Agents (parallel/specialized)
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Retriever (per agent or shared)
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Relevant Docs
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Generator / LLM (per agent or shared)
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Responses Aggregated or Selected
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Final Answer
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Retriever-Augmented Generation (RAG)
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Retriever: Searches a large knowledge base for relevant documents or data.
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Generator: Uses the retrieved information to generate a contextually accurate response or action, often utilizing transformer models like GPT.
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Purpose: Enhances generative models with external knowledge, improving response quality when internal knowledge is lacking.
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Multi-Agent System
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A system consisting of multiple agents (software or robotic entities) that can work either independently or collaboratively to achieve a goal.
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Each agent may have specialized functions such as data retrieval, information processing, or interaction with users, aiding in handling complex tasks.
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Combining RAG with Multi-Agent System
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Collaboration: Agents specialize in tasks, such as one retrieving information and another generating responses, leading to efficient and accurate outputs.
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Scalability: More agents can be added to handle diverse tasks or specialized domains.
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Specialization: Different agents can focus on various roles, enhancing task completion through targeted expertise.
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Observability & Evaluation Layer
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Customer Support: One agent retrieves knowledge, another uses RAG to generate responses.
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Research: Agents retrieve and summarize academic papers, generating reports based on data.
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Robotics: Agents control different robot functions, collaboratively retrieving and processing data for actions.