Adaptive RAG Strategy with Query Classification & Retrieval (Gemini & Qdrant)
Last edited 39 days ago
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 Works
- Receive 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.
⚙️ Usage & Flexibility
- Sub-Workflow: Designed to be called from other n8n workflows, passing
user_query
,chat_memory_key
, andvector_store_id
as inputs. - Chat Testing: Can also be triggered directly via the n8n Chat interface for easy testing and interaction.
- Customizable Framework: The query categories (Factual, Analytical, etc.) and the associated retrieval strategies are examples. You can modify or replace them entirely to fit your specific domain or requirements.
🛠️ Requirements
- Credentials: You will need API credentials configured in your n8n instance for:
- Google Gemini (AI Models)
- Qdrant (Vector Store)
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