Save Costs in RAG Workflows with Q&A Tool & Multiple Models
This workflow enables cost-efficient RAG by uploading knowledge files via a form, embedding them with OpenAI, and using a Q&A tool for agent queries without full reprocessing.
This n8n workflow demonstrates a smart approach to implementing Retrieval-Augmented Generation (RAG) while minimizing costs. It starts with a Form Trigger node where users upload PDF or CSV files containing custom knowledge. These files are then processed through OpenAI Embeddings to create vector representations, stored in a Simple Vector Store for quick retrieval.
The core innovation is the Question and Answer (Q&A) Tool, which allows agents to query the vector store efficiently. Instead of r
- Platform
- n8n
- Category
- Surveys & Forms
- Price
- $14.99
- Creator
- Jonas Sato
- RAG
- AI Agent
- OpenAI Embeddings
- Vector Store
- Q&A Tool
- Cost Optimization
- PDF Upload
- Chatbot
- Knowledge Base
- n8n AI
How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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