RAG AI Agent with Milvus & Cohere
Automates Retrieval-Augmented Generation (RAG) using Cohere embeddings, Milvus vector DB, OpenAI GPT-4o, and Google Drive. Upload PDFs to Drive, auto-embed and store in Milvus, then query via an AI chat agent for contextual AI responses.
Apps in this workflow
How it works
- Chat Trigger1
- Google Drive Trigger2
- AI Agent3
- Google Drive4
- Extract from File5
- Milvus Vector Store6
- Document Loader7
- Embeddings Cohere8
- OpenAI Chat Model9
- Chat Memory10
- Text Splitter11
The building blocks this workflow uses, in the order data flows through them.
What you'll need
- An n8n instance (self-hosted or n8n Cloud).
- A Google Drive account, connected in n8n as a credential.
- A Milvus account, connected in n8n as a credential.
- An API key for OpenAI in n8n: the workflow calls the model each time it runs.
Gydmation shows step-by-step credential setup for each app, so your keys go straight into n8n and never to Gydmation.
Make it yours in plain English
Open the template in Gydmation and describe the change. It edits the workflow, checks it against real n8n node schemas, and deploys it to your n8n. For example:
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