Adaptive RAG Workflow with Query Classification and Gemini-Qdrant Retrieval

Overview

This workflow leverages advanced query classification and Retrieval-Augmented Generation (RAG) strategies to deliver tailored responses based on user intent. By integrating Gemini and Qdrant with n8n's LangChain nodes, it dynamically selects the optimal retrieval strategy for each query.

Key Features

• Query Classification: Incoming queries are analyzed and classified into categories such as factual, analytical, opinion, or contextual.
• Adaptive Retrieval: Depending on classification, the workflow routes queries through specialized strategies—precision, comprehensive coverage, diverse perspectives, or user context integration.
• Gemini & Qdrant Integration: Utilizes Gemini for language understanding and Qdrant for vector-based retrieval, ensuring relevant and accurate information.
• Automated Chat Trigger: Initiates the workflow via chat, enabling seamless user interaction.
• Custom Prompts & Outputs: Each strategy generates tailored prompts and outputs for enhanced response quality.

Benefits

• Improved Accuracy: Delivers contextually appropriate and precise answers.
• Efficiency: Automates complex decision-making, reducing manual intervention.
• Scalability: Handles diverse query types and adapts to evolving business needs.

Use Cases

• Customer support automation
• Knowledge base enhancement
• Data-driven decision support
• Content management and retrieval

This workflow streamlines information retrieval, saving time and boosting productivity.