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. 📌**
