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🚀 How the System Works
This automation operates in three distinct phases: Ingestion, Storage, and Generation.
| Phase | Component | What Happens |
|---|---|---|
| 1. The Trigger | Google Drive | Every time you update your rag_posts.csv in your Drive folder, the system wakes up. |
| 2. The Brain | Gemini Embeddings | It turns your text into "Vectors" (numbers) so the AI understands the meaning of your writing style, not just the words. |
| 3. The Vault | MongoDB Atlas | Your posts are stored in a vector database, acting as a "Style Library" the AI can browse instantly. |
| 4. The Writer | AI Agents | When you ask for a post, the AI searches your vault, finds the best matches, and mimics the formatting exactly. |
🛠️ Step-by-Step Setup Guide
1. Prepare Your Data Source
- Create a Google Drive Folder and note its ID (the long string of characters in the URL).
- Create a CSV file named
rag_posts.csv. - Columns needed:
Post Text,Hook Type,Engagement,Category. - Upload it to that folder.
2. Configure MongoDB Atlas (The Vector Store)
- Sign up for a free MongoDB Atlas account.
- Create a Cluster and a Database named
n8n_rag_data. - Crucial Step: Create an Atlas Vector Search Index on your collection.
- Name the index
data_index.
3. Google Gemini API
- Go to the Google AI Studio.
- Generate an API Key. This will power both the "Embeddings" (understanding the text) and the "Chat" (writing the post).
4. Connect the n8n Nodes
- Google Drive Trigger: Paste your Folder ID and select
fileUpdated. - MongoDB Nodes: Enter your Connection String (SRV) and credentials.
- Gemini Nodes: Paste your API Key into the Credentials section.
- Google Sheets Tool: Link your specific spreadsheet ID so the "Knowledge Base Agent 1" can read specific rows.