Adaptive RAG: Dynamic Information Retrieval Engine
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The Adaptive RAG workflow dynamically tailors information retrieval based on the nature of user queries. It intelligently classifies queries and applies specialized strategies for factual, analytical, opinion, and contextual requests, ensuring more relevant and comprehensive responses.
About This Workflow
The Adaptive RAG workflow is a sophisticated system designed to revolutionize how you interact with information. At its core, it leverages advanced AI to understand the intent behind user queries. The system first classifies incoming questions into one of four categories: Factual, Analytical, Opinion, or Contextual. Based on this classification, it then deploys a tailored retrieval strategy. For factual queries, it focuses on precision. Analytical queries are met with comprehensive coverage through sub-question generation. Opinion-based queries are addressed by identifying diverse perspectives. Finally, contextual queries are handled by inferring and integrating user-specific context. This adaptive approach ensures that the most appropriate and effective information retrieval methods are employed, leading to significantly improved accuracy and user satisfaction.
Key Features
- Intelligent Query Classification: Automatically categorizes user queries into Factual, Analytical, Opinion, or Contextual.
- Dynamic Retrieval Strategies: Employs specialized approaches tailored to each query type for optimal results.
- Precision Focus for Factual Data: Enhances factual queries for more accurate and specific information retrieval.
- Comprehensive Analysis for Complex Questions: Breaks down analytical queries into sub-questions to ensure thorough coverage.
- Exploration of Diverse Viewpoints: Identifies and presents a range of perspectives for opinion-based queries.
- Contextual Understanding: Integrates user-specific context for more relevant and personalized responses.
How To Use
- Integrate the Workflow: Connect this n8n workflow to your data sources and user input mechanisms.
- Input User Queries: Ensure user queries are passed to the 'Query Classification' node.
- Review Output: Monitor the workflow's output to observe how different query types are handled and the strategies employed.
- Customize Strategies (Advanced): For deeper customization, you can modify the system messages within each 'Strategy' node (e.g., 'Factual Strategy - Focus on Precision') to further refine the AI's behavior and desired output format.
Apps Used
Workflow JSON
{
"id": "d748e22b-b7cc-4017-8a57-ef4894770604",
"name": "Adaptive RAG: Dynamic Information Retrieval Engine",
"nodes": 20,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: d748e22b-b7cc...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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