Adaptive RAG with Google Gemini & Qdrant: Context-Aware Query Answering

Description This workflow automatically classifies user queries and retrieves the most relevant information based on the query type. 🌟 It uses adaptive strategies like; Factual, Analytical, Opinion, and Contextual to deliver more precise and meaningful responses by leveraging n8n's flexibility. Integrated with Qdrant vector store and Google Gemini, it processes each query faster and more effectively. 🚀 How It Works? Query Reception: A user query is triggered (e.g., through a chatbot interface). 💬 Classification: The query is classified into one of four categories: Factual: Queries seeking verifiable information. Analytical: Queries that require in-depth analysis or explanation. Opinion: Q

Help me set up the n8n workflow "Adaptive RAG with Google Gemini & Qdrant: Context-Aware Query Answering" (https://n8n.io/workflows/4043). 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.