This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classification and model selection to optimize both performance and cost-efficiency in AI-driven conversations.

It dynamically routes requests to specialized AI models based on content type, optimizing response quality and efficiency.

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Benefits

• Smart Model Routing: Reduces costs by using lighter models for general tasks and reserving heavier models for complex needs.
• Scalability: Easily expandable by adding more request types or LLMs.
• Maintainability: Clear logic separation between classification, model routing, and execution.
• Personalization: Can be integrated with session IDs for per-user memory, enabling personalized conversations.
• Speed Optimization: Fast models like GPT-4.1 mini or Gemini Flash are chosen for tasks where speed is a priority.

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How It Works

1. Input Handling:

   • The workflow starts with the "When chat message received" node, which triggers the process when a chat message is received. The input includes the chat message (chatInput) and a session ID (sessionId).

2. Request Classification:

   • The "Request Type" node uses an OpenAI model (gpt-4.1-mini) to classify the incoming request into one of four categories:
     • general: For general queries.
     • reasoning: For reasoning-based questions.
     • coding: For code-related requests.
     • search: For queries requiring search tools.
   • The classification is structured using the "Structured Output Parser" node, which enforces a consistent output format.

3. Model Selection:

   • The "Model Selector" node routes the request to one of four AI models based on the classification:
     • Opus 4 (Claude 4 Sonnet): Used for coding requests.
     • Gemini Thinking Pro: Used for reasoning requests.
     • GPT 4.1 mini: Used for general requests.
     • Perplexity: Used for search (Google-related) requests.

4. AI Processing:

   • The selected model processes the request via the "AI Agent" node, which includes intermediate steps for complex tasks.
   • The "Simple Memory" node retains session context using the provided sessionId, enabling multi-turn conversations.

5. Output:

   • The final response is generated by the chosen model and returned to the user.

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Set Up Steps

1. Configure Trigger:

   • Ensure the "When chat message received" node is set up with the correct webhook ID to receive chat inputs.

2. Define Classification Logic:

   • Adjust the prompt in the "Request Type" node to refine classification accuracy.
   • Verify the output schema in the "Structured Output Parser" node matches expected categories (general, reasoning, coding, search).

3. Connect AI Models:

   • Link each model node (Opus 4, Gemini Thinking Pro, GPT 4.1 mini, Perplexity) to the "Model Selector" node.
   • Ensure credentials (API keys) for each model are correctly configured in their respective nodes.

4. Set Up Memory:

   • Configure the "Simple Memory" node to use the sessionId from the input for context retention.

5. Test Workflow:

   • Send test inputs to verify classification and model routing.
   • Check intermediate outputs (e.g., request_type) to ensure correct model selection.

6. Activate Workflow:

   • Toggle the workflow to "Active" in n8n after testing.

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