Save Costs In RAG Workflows using the Q&A Tool With Multiple Models
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
Nodes used (8)
- n8n Form Trigger
n8n-nodes-base.formTrigger - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Simple Vector Store
@n8n/n8n-nodes-langchain.vectorStoreInMemory - AI Agent
@n8n/n8n-nodes-langchain.agent - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Vector Store Question Answer Tool
@n8n/n8n-nodes-langchain.toolVectorStore - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
What it does
This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs 1. Add custom instructions to your Agent by changing the prompts in it. 2. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. 3. Describe your data properly in the Q&A tool 4. Exchange the Simple Vector Store nodes with your own vector store tools ready for production. 5. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.