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: 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. 📌