Adaptive RAG Strategy with Query Classification & Retrieval (Gemini & Qdrant)

This n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) framework. It recognizes that the best way to retrieve information often depends on the type of question asked. Instead of a one-size-fits-all approach, this workflow adapts its strategy based on the user's query intent. 🌟 How it Works Receive Query: Takes a user query as input (along with context like a chat session ID and Vector Store collection ID if used as sub-workflow). Classify Query: First, the workflow classifies the query into a predefined category. This template uses four examples: Factual: For specific facts. Analytical: For deeper explanations or comparisons. Opinion: For subjective view

Help me set up the n8n workflow "Adaptive RAG Strategy with Query Classification & Retrieval (Gemini & Qdrant)" (https://n8n.io/workflows/3459). It uses: AI Agent, Simple Memory, Qdrant Vector Store, Embeddings Google Gemini, Google Gemini Chat Model. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.