Save Costs in RAG with Q&A Tool & Multi-Model n8n Workflow

Save Costs in RAG with an n8n Q&A Tool + Multi-Model Workflow

This n8n workflow template helps you “teach” your agents using RAG efficiently—using a Question and Answer tool designed to save costs in RAG use cases. Upload your knowledge PDF, start chatting, and let your agent answer questions based on your custom documents.

What this workflow does

Built for knowledge-powered agents, this template demonstrates how to:

• Start an interactive chat with a Question and Answer experience.
• Upload your knowledge document (PDF) containing custom information you want your agent to use.
• Store and retrieve knowledge via a vector store to support RAG-style answers.
• Reduce RAG-related spend by using the provided Q&A tool approach for cost-saving in RAG workflows.

Who this is for

This template is for everyone who wants to start giving knowledge to their Agents through RAG—especially n8n users, automation engineers, and SaaS operators.

Use cases

• Customer support knowledge base: Ask questions about your policies and procedures from a PDF.
• Internal SOP assistance: Enable agents to answer operational questions using your documentation.
• Product/engineering docs Q&A: Let users query technical guides stored as custom PDF knowledge.

Technical details (n8n + nodes)

• Q&A and chat triggers: n8n-nodes-langchainagent, n8n-nodes-langchainchat trigger
• Language model: n8n-nodes-langchainlm chat open ai
• Vector store: n8n-nodes-langchaintool vector store (uses a “Simple Vector Store” in the template)
• UI and input: sticky note + form trigger

Setup & customization

No setup required. Click Execute Workflow, upload your knowledge document, and start chatting. Customize by updating agent prompts, swapping the vector store nodes for production-ready ones, improving file ranking, and describing your data properly in the Q&A tool.