3h • 🧠 n8n Workflows
I Built a RAG Agent in n8n Using Gemini File Search API (No Vector DB)
This weekend I experimented with a different way to build RAG.
Instead of the typical setup:
- Generate embeddings
- Store in Pinecone / Supabase
- Manage vector DB infra
- Handle indexing + costs
I tested Gemini File Search API directly inside n8n.
And honestly… it simplified the entire pipeline.
🔧 What I Actually Built
Inside n8n, I used just 4 HTTP requests:
- Create a file store
- Upload a document
- Move the file into the store
- Query Gemini
That’s it.
Gemini handled:
- Chunking
- Embeddings
- Indexing
- Retrieval
No external vector database.No embedding model setup.
💰 Why This Is Interesting
- Storage is free
- No hourly DB cost
- Indexing is $0.15 per 1M tokens
For small projects, internal tools, or MVPs — this is extremely cost-efficient.
⚠️ Important Limitations I Noticed
This is not magic.
- No automatic version control (re-upload = duplicate data)
- Chunk-based retrieval struggles with full-document reasoning
- OCR works, but messy documents still need preprocessing
- Data is processed on Google servers (privacy considerations apply)
So architecture thinking still matters.
My Take
For:
- Internal AI assistants
- Automation workflows
- Startup prototypes
- Personal tools
This is a powerful alternative to traditional vector DB setups.
I wouldn’t blindly replace enterprise-grade systems yet — but for builders, this is very interesting.
If anyone here is experimenting with Gemini File Search or building RAG in n8n, I’d love to compare notes 👇
Happy to share the workflow structure if there’s interest.
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I Built a RAG Agent in n8n Using Gemini File Search API (No Vector DB)
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