Adaptive RAG Response Routing

This workflow is for anyone who wants to provide enhanced AI responses based on the user's intent, using custom knowledge bases. This workflow classifies user questions into different types. It then adapts its search and answer methods based on the question type. This helps it give more accurate and helpful answers from your data. Set up a Qdrant vector store with your documents. Connect an n8n chatbot or another workflow as the starting point. Update the vector_store_id in the "Combined Fields" node to match your Qdrant ID. Add your Google Gemini API key to the credentials. You need an n8n instance, a Google Gemini API key, and a Qdrant vector store populated with your data. You can change the categories for query classification and adjust the AI prompts for each strategy. You can also modify the vector store search parameters (e.g., topK). Key Technologies: n8n,Google Gemini (Large Language Model),Qdrant (Vector Store),LangChain (for AI orchestration) Automation Value: Smarter AI Responses: It delivers more accurate and relevant AI answers by understanding the user's intent. Efficient Information Retrieval: It tailors the search for information, saving time and resources. Scalable Knowledge Base: It uses a vector store to quickly find answers from large amounts of data. Improved User Experience: Users receive answers that directly address their specific needs. Best Practices: Clear AI Prompts: Craft clear and concise system messages for each AI node to guide its behavior effectively. Relevant Context: Ensure your Qdrant vector store contains high-quality, relevant documents for accurate retrieval. Memory Management: Use chat memory wisely; adjust contextWindowLength based on the conversation complexity. Error Handling: Add error handling to gracefully manage situations where classification fails or no documents are retrieved (not explicitly shown in this workflow but recommended practice). Scalability: Use efficient models and optimize vector store queries for performance as your usage grows.