Adaptive RAG Query Processor

This n8n workflow implements an advanced, adaptive Retrieval-Augmented Generation (RAG) system. It classifies user queries into four types — Factual, Analytical, Opinion, and Contextual — and dynamically applies tailored query adaptation, document retrieval, and answer generation strategies. Powered by Google Gemini models and Qdrant as a vector database, the workflow produces highly relevant, context-aware responses that match the intent and complexity of each query.

Features

• Intelligent Query Classification
  Automatically determines the nature of each user query to ensure the best retrieval approach.
• Adaptive Strategies per Query Type
  • Factual: Enhances query precision for exact, verifiable answers.
  • Analytical: Breaks down complex questions into sub-questions for deeper coverage.
  • Opinion: Identifies diverse viewpoints and presents balanced perspectives.
  • Contextual: Infers implied or user-specific context to improve response relevance.
• Vector Database Integration (Qdrant)
  Searches for relevant documents using Gemini-generated embeddings for high-quality retrieval.
• Customizable Answer Prompts
  Adjusts the tone and focus of generated answers based on the query classification.
• Conversation Memory Support
  Maintains chat context across interactions using memory buffers keyed per session.
• Flexible Triggering
  Can be started via chat interface or called from other workflows with user_query, chat_memory_key, and vector_store_id inputs.
• End-to-End RAG Pipeline
  From classification → adaptation → retrieval → context assembly → answer generation → webhook response.