Intelligent RAG: Dynamic Query Routing for Superior AI Answers
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Elevate your RAG (Retrieval Augmented Generation) system with dynamic query routing. This n8n workflow intelligently classifies user queries and applies a tailored retrieval strategy, ensuring more precise, comprehensive, and relevant AI-generated answers.
About This Workflow
Traditional RAG systems often struggle with a 'one-size-fits-all' approach to information retrieval. This advanced n8n workflow, named 'RAG 2.0 - Answer Architecture', revolutionizes this by introducing intelligent query classification and dynamic strategy selection. It analyzes incoming user queries, categorizes them as Factual, Analytical, Opinion, or Contextual, and then routes them to a specialized prompt-engineered Langchain agent. This ensures that the subsequent retrieval and generation steps are optimized for the specific intent of the query, leading to significantly improved answer quality, relevance, and user satisfaction across diverse AI applications.
Key Features
- Dynamic Query Classification: Automatically classifies user queries into four distinct categories: Factual, Analytical, Opinion, or Contextual using a sophisticated LLM agent.
- Intelligent Routing: Employs a Switch node to seamlessly direct queries to the most appropriate retrieval strategy based on their classification.
- Tailored Retrieval Strategies: Leverages specialized Langchain agents for each query type:
- Factual: Enhances queries for precision.
- Analytical: Generates sub-questions for comprehensive coverage.
- Opinion: Identifies diverse perspectives for balanced responses.
- Contextual: Infers and integrates user-specific context.
- Advanced Prompt Engineering: Each strategy agent is finely tuned with specific system messages to optimize query manipulation for superior RAG results.
- Scalable Architecture: Designed to be a foundational component of a more robust and intelligent RAG pipeline.
How To Use
- Input User Query: Ensure your user's query is fed into the workflow, typically via a preceding node that populates the
user_queryfield (e.g., from a web form, chat interface, or another n8n trigger). - Configure 'Query Classification' Node: This node is pre-configured to use an LLM (via Langchain) to classify the incoming
user_query. No additional setup is typically required here, but you can adjust the LLM model if needed. - Understand the 'Switch' Node: This node automatically takes the output category from 'Query Classification' and routes the workflow down one of its four distinct paths: 'Factual', 'Analytical', 'Opinion', or 'Contextual'.
- Customize Strategy Nodes (Optional): Each of the four strategy nodes ('Factual Strategy...', 'Analytical Strategy...', etc.) uses a Langchain agent to further refine the query based on its category. You can modify their
systemMessageparameters to fine-tune the prompt engineering for your specific RAG requirements. - Connect to your RAG System: Connect the output of the relevant strategy node(s) to your subsequent RAG components (e.g., vector database retrieval, LLM generation nodes) to leverage the dynamically enhanced query for superior answer architecture.
Apps Used
Workflow JSON
{
"id": "1abffea0-f588-41a5-aa25-d67f4df3da35",
"name": "Intelligent RAG: Dynamic Query Routing for Superior AI Answers",
"nodes": 11,
"category": "Operations",
"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: 1abffea0-f588...
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