Adaptive RAG in n8n: Classify & Tailor Intelligent AI Responses

1. Opening Problem Statement

Meet Alex, a knowledge manager at a fast-growing tech startup inundated daily with a flood of diverse questions from team members. These queries range from precise data checks to in-depth analyses, subjective opinions, or issues dependent on specific project contexts. Alex spends countless hours manually categorizing each question and tailoring research efforts to find relevant answers, losing precious time and risking inaccuracies due to the manual, one-size-fits-all approach.

This inefficiency not only wastes dozens of hours weekly but also causes delays in decision-making and frustrates team members seeking quick, meaningful insights tailored to their exact needs. Generic AI assistants fail to discern the unique nature of each query, producing vague or irrelevant answers.

To tackle this, the Adaptive Retrieval-Augmented Generation (RAG) workflow implemented in n8n intelligently classifies queries into four distinct types—Factual, Analytical, Opinion, and Contextual—and applies specialized strategies to deliver precise, comprehensive, diverse, or context-aware responses. This automation revolutionizes how Alex and his team handle information retrieval with AI, saving hours of manual work and enhancing knowledge accuracy.

2. What This Automation Does

When this workflow runs, here’s exactly what happens:

• Query Classification: It uses a Google Gemini-based AI agent to classify incoming queries into one of four categories—Factual, Analytical, Opinion, or Contextual.
• Adaptive Strategy Routing: Based on query type, the workflow directs the query to a specialized retrieval strategy tailored for that category.
• Query Adaptation: Each strategy either enhances factual queries for precision, breaks analytical queries into sub-questions, identifies diverse perspectives for opinion queries, or infers implied context for contextual queries.
• Intelligent Document Retrieval: The adapted query is used to fetch relevant documents from a Qdrant vector store embedding knowledge documents.
• Context Concatenation: Retrieved document contents are concatenated into a comprehensive context for answer generation.
• Tailored Answer Generation: A Google Gemini language model generates a final response customized to the query type, blending the query, retrieved context, and chat memory history.
• Webhook Response: The generated answer is sent back through a webhook to the requesting client or chatbot interface.

By automating these steps, it saves teams like Alex’s an estimated 10-15 hours per week previously spent on manual query triage and research, while improving answer accuracy and relevance significantly.

3. Prerequisites ⚙️

• An active n8n account for workflow automation.
• Google Gemini (PaLM) API account credentials for AI classification and generation nodes.
• A Qdrant vector store with embedded knowledge documents and appropriate collection ID.
• Basic familiarity with n8n node setup and connecting credentials.
• Optional but recommended: Access to the official self-hosting guide if you prefer managing n8n on your own server.

4. Step-by-Step Guide

Step 1: Trigger the Workflow via Chat or External Workflow

Navigate to the n8n dashboard, and either use the built-in Chat trigger node for direct user queries or configure another workflow to trigger this one using the Execute Workflow Trigger node.

Inputs expected: user_query (the user’s question), chat_memory_key (optional key to maintain conversation history), and vector_store_id (the Qdrant collection identifier).

After triggering, the workflow initializes by standardizing these inputs in the Combined Fields node.

Visual cue: You should see the incoming user query captured and split into the three variables in the Combined Fields node output.

Common mistake: Forgetting to supply the correct vector_store_id will cause document retrieval to fail later.

Step 2: Classify the User Query Type

The Query Classification node uses the @n8n/n8n-nodes-langchain.agent node powered by Google Gemini to analyze the text and classify it into exactly one of four categories: Factual, Analytical, Opinion, or Contextual.

It’s configured with a detailed system prompt that instructs the AI on classification criteria and expected output format.

Inspect the node’s output for a simple string category, e.g. “Factual”.

Common mistake: If the AI returns an unexpected value (e.g., misspelled category), the Switch node will not route properly.

Step 3: Use the Switch Node to Route Based on Classification

Review the Switch node settings which use strict case-sensitive string matching against the classification output (trimmed).

This node ensures the workflow dynamically adapts to the query category by sending it to one of the four specialized strategy nodes.

Visual: You should see the flow split into four distinct paths named after each query type.

Common mistake: Ensure the classification output exactly matches the expected strings “Factual,” “Analytical,” “Opinion,” or “Contextual.”

Step 4: Apply Strategy-Specific Query Adaptation

Each of the four strategy nodes (@n8n/n8n-nodes-langchain.agent) is tailored to refine the query differently:

• Factual Strategy – Focus on Precision: Rewrites the factual query to improve search precision by emphasizing key entities.
• Analytical Strategy – Comprehensive Coverage: Breaks down the analytical query into three detailed sub-questions to cover the topic breadth.
• Opinion Strategy – Diverse Perspectives: Identifies three different viewpoints relevant to the opinion query.
• Contextual Strategy – User Context Integration: Infers implied context supporting the query intent.

Each node uses a system prompt built for its role and processes the original user_query.

Common mistake: Inconsistent or missing chat memory keys can reduce context relevance in adaptation.

Step 5: Set Output and Prompt for Final Response Generation

The four Prompt and Output nodes (Set nodes) capture the processed output and map it with a customized system prompt instructing the final answer generation AI how to tailor its output.

Example prompt snippet for the Factual node: “You are a helpful assistant providing factual information…”

Visual: You should see both the enhanced query/output and the tailored prompt saved.

Common mistake: Mixing outputs from different strategy nodes can confuse the final AI answer.

Step 6: Retrieve Relevant Documents from Qdrant Vector Store

The Retrieve Documents from Vector Store node queries the Qdrant collection specified in vector_store_id to find relevant documents matching the adapted query.

It uses the Embeddings node powered by Google Gemini text embeddings to encode the query for similarity search.

Expected outcome: top 10 relevant document chunks fetched.

Common mistake: Incorrect collection ID or lack of indexed documents leads to empty retrieval results.

Step 7: Concatenate Retrieved Document Context

The Concatenate Context node merges the pageContent fields from all retrieved documents into a single text block, separated by clear delimiters for readability.

Visual: The combined content flows downstream for final AI processing.

Common mistake: Failing to configure the concatenation separator could reduce answer clarity.

Step 8: Generate Final AI Answer Incorporating Chat History

The Answer node uses a Google Gemini AI agent with a system prompt defined in Step 5, the concatenated context, the original user_query, and chat history via a Chat Buffer Memory node keyed by chat_memory_key.

This agent composes a detailed, query-specific answer integrating retrieved knowledge and conversational context.

Common mistake: Missing or invalid chat memory keys will cause loss of conversation continuity.

Step 9: Respond Back to the User

The Respond to Webhook node sends the generated answer back to the client or chatbot interface that made the original request.

Visual: User receives a well-tailored response matching the query type, from precise facts to detailed analyses or context-aware insights.

Common mistake: Ensure the webhook response configuration matches the client expectations for smooth integration.

5. Customizations ✏️

• Adjust Classification Categories: In the Query Classification node, modify the system message prompt to add or refine categories based on your specific query needs.
• Tune Retrieval Quantity: Change the topK parameter in the Retrieve Documents from Vector Store node to fetch more or fewer document chunks for broader or tighter results.
• Modify Strategy Prompts: Customize system messages in any of the strategy nodes (Factual, Analytical, Opinion, Contextual) to fine-tune how queries are adapted or expanded.
• Switch Vector Store: Replace Qdrant with other vector databases supported by n8n if desired, updating the retrieval node accordingly.
• Memory Window Size: Adjust the contextWindowLength parameter in Chat Buffer Memory nodes to alter how much chat history impacts the answer.

6. Troubleshooting 🔧

Problem: “No documents retrieved from vector store.”

Cause: Incorrect or missing vector_store_id or empty collection.

Solution: Verify that the Qdrant collection is properly indexed and the vector_store_id matches the collection ID in the Retrieve Documents node.

Problem: “Query classification returns unexpected output.”

Cause: The classification AI prompt does not strictly limit output to expected categories.

Solution: Refine the system prompt in Query Classification node to ensure exact category names are returned without extra text.

Problem: “Final answer generation lacks conversational context.”

Cause: Missing or incorrect chat_memory_key in input data.

Solution: Validate input fields and ensure Chat Buffer Memory nodes are configured with consistent session keys.

7. Pre-Production Checklist ✅

• Ensure Google Gemini API credentials are correctly set up and permissions granted.
• Test classification outputs by inputting sample queries into the Query Classification node.
• Verify the vector_store_id points to a populated Qdrant collection.
• Test each query type path independently to confirm adaptive strategy flows.
• Check chaining of memory buffer nodes to maintain chat history accurately.
• Conduct mock requests through both Chat and external workflow triggers.

8. Deployment Guide

Once thoroughly tested, activate the workflow via the toggle in n8n. You can embed this workflow behind APIs or integrate it with chatbots by connecting to the webhook URL exposed by the Chat node.

Monitor logs in n8n for errors or latency issues. Use the sticky notes embedded in the workflow canvas as handy reminders of strategy intents during maintenance.

9. FAQs

• Can I use a different language model? Yes, but the workflow is optimized for Google Gemini models; switching requires updating node credentials and model identifiers.
• Does using Google Gemini increase API costs? Yes, these nodes consume API credits based on usage, so monitor your consumption accordingly.
• Is my data secure? The workflow keeps data within your infrastructure and trusted API services, but always review your data privacy policies.
• Can this handle high query volumes? The stateless design scales well, but performance depends on API rate limits and database response times.

10. Conclusion

By implementing this Adaptive RAG workflow, you have built a powerful AI assistant that intelligently classifies and adapts responses to diverse query types, significantly improving relevance and clarity of information delivery. Teams save valuable hours weekly while enhancing user satisfaction and decision-making accuracy.

Next steps? Consider integrating personalized user profiles for richer context, adding multi-lingual support for global queries, or expanding to include document updating triggers for keeping your knowledge base fresh.

Keep experimenting and refining — your smarter AI assistant is just a workflow away!