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

Description

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: Queries looking for different perspectives or subjective viewpoints. Contextual: Queries specific to the user or certain contextual conditions. Adaptive Strategy Application: Based on classification, the query is restructured using the relevant strategy for better results. Response Generation**: The most relevant documents and context are used to generate a tailored response. 🎯 Set Up Steps Estimated Time: ⏳ 10-15 minutes Prerequisites: You need an n8n account and a Qdrant vector store connection. Steps: Import the n8n workflow: Load the workflow into your n8n instance. Connect Google Gemini and Qdrant: Link these tools for query processing and data retrieval. Connect the Trigger Interface: Integrate with a chatbot or API to trigger the workflow. Customize: Adjust settings based on the query types you want to handle and the output format. 🔧 For more detailed instructions, please check the sticky notes inside the workflow. 📌. An n8n automation workflow template by Nisa.

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Categories

• Automation
• AI

Tags

• n8n
• workflow
• ai-agent
• simple-memory
• qdrant-vector-store
• embeddings-google-gemini
• google-gemini-chat-model

Capabilities

• 5 nodes
• 3,237 views

Information