Adaptive RAG: Intelligent Query Refinement for Superior Information Retrieval
detail.loadingPreview
Adaptive RAG is a powerful n8n workflow that dynamically refines user queries based on their intent. By classifying queries into factual, analytical, opinion, or contextual categories, it employs tailored retrieval strategies to deliver more precise and relevant information.
About This Workflow
The Adaptive RAG workflow revolutionizes how you interact with information. It intelligently analyzes incoming user queries, categorizing them to understand the underlying intent. Whether a user seeks specific facts, needs in-depth analysis, wants to explore diverse opinions, or requires context-aware information, Adaptive RAG deploys a specialized strategy. This ensures that the subsequent information retrieval process is optimized for accuracy and relevance, going beyond generic search to provide truly actionable insights. By leveraging advanced AI and Langchain integration, this workflow significantly enhances the effectiveness of any knowledge-based system.
Key Features
- Intelligent Query Classification: Automatically categorizes user queries into Factual, Analytical, Opinion, or Contextual.
- Dynamic Retrieval Strategies: Employs tailored methods based on query type for precision, breadth, or perspective.
- Contextual Awareness: Integrates user-specific context for more personalized and relevant results.
- AI-Powered Enhancement: Utilizes Langchain agents for sophisticated query understanding and refinement.
- Automated Workflow: Seamlessly integrates into existing n8n automation pipelines.
How To Use
- Set up the 'Query Classification' node: This node uses a Langchain agent to analyze the incoming user query. Configure the
systemMessageto precisely define the four categories (Factual, Analytical, Opinion, Contextual) and ensure it returns only the category name. - Configure the 'Switch' node: This node routes the classified query to the appropriate strategy. Ensure each
outputKeymatches one of the categories defined in the classification node. TheleftValuefor each condition should reference the output of the 'Query Classification' node (e.g.,={{ $json.output.trim() }}). - Set up the specific strategy nodes: For each category, a dedicated Langchain agent node handles the retrieval strategy:
- Factual Strategy: The 'Factual Strategy - Focus on Precision' node refines factual queries for accuracy.
- Analytical Strategy: The 'Analytical Strategy - Comprehensive Coverage' node breaks down analytical queries into sub-questions.
- Opinion Strategy: The 'Opinion Strategy - Diverse Perspectives' node identifies various viewpoints on opinion-based queries.
- Contextual Strategy: The 'Contextual Strategy - User Context Integration' node infers and leverages user-specific context.
- Connect the nodes: Ensure the output of the 'Query Classification' node is connected to the input of the 'Switch' node. Then, connect each output of the 'Switch' node to its corresponding strategy node.
- Integrate with your data source: The output of each strategy node can then be used to query your knowledge base, database, or any other information source to retrieve the most relevant information.
Apps Used
Workflow JSON
{
"id": "e5e6d9a6-40d9-4b18-a6ba-ed35dc3065ec",
"name": "Adaptive RAG: Intelligent Query Refinement for Superior Information Retrieval",
"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.
Get This Workflow
ID: e5e6d9a6-40d9...
About the Author
N8N_Community_Pick
Curator
Hand-picked high quality workflows from the global community.
Statistics
Related Workflows
Discover more workflows you might like
Build a Custom OpenAI-Compatible LLM Proxy with n8n
This workflow transforms n8n into a powerful OpenAI-compatible API proxy, allowing you to centralize and customize how your applications interact with various Large Language Models. It enables a unified interface for diverse AI capabilities, including multimodal input handling and dynamic model routing.
Effortless Bug Reporting: Slack Slash Command to Linear Issue
Streamline your bug reporting process by instantly creating Linear issues directly from Slack using a simple slash command. This workflow enhances team collaboration by providing immediate feedback and a structured approach to logging defects, saving valuable time for development and QA teams.
Automate Qualys Report Generation and Retrieval
Streamline your Qualys security reporting by automating the generation and retrieval of reports. This workflow ensures timely access to crucial security data without manual intervention.