n8n AI Data Management Productivity
Contributed by Sabrina Ramonov 🍄. From the community directory at agents.sabrina.dev, republished with permission and full credit.
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 WorksReceive 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 viewpoints.
- Contextual: For questions relying on specific background.
Select & Adapt Strategy: Based on the classification, it selects a corresponding strategy to prepare for information retrieval. The example strategies aim to:
- Factual: Refine the query for precision.
- Analytical: Break the query into sub-questions for broad coverage.
- Opinion: Identify different viewpoints to look for.
- Contextual: Incorporate implied or user-specific context.
Retrieve Info: Uses the output of the selected strategy to search the specified knowledge base (Qdrant vector store - change as needed) for relevant documents.
Generate Response: Constructs a response using the retrieved documents, guided by a prompt tailored to the original query type.
By adapting the retrieval strategy, this workflow aims to provide more relevant results tailored to the user's intent.
RequirementsCredentials: You will need API credentials configured in your n8n instance for:
- Google Gemini (AI Models)
- Qdrant (Vector Store)
Tools used: Google Gemini, Qdrant
Download the workflow template (JSON)
Want this running in your business? KOBA42 builds and operates automations like this one.