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)