Real-time email RAG assistant with Gmail, OpenAI GPT, and PGVector
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) New RAG Agent (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Worth a look
- Gmail Trigger below v1.4 runs as v1.4 (drafts skipped, max emails per poll) (Gmail Trigger)
Nodes used (9)
- Gmail Trigger
n8n-nodes-base.gmailTrigger - Gmail
n8n-nodes-base.gmail - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Postgres PGVector Store
@n8n/n8n-nodes-langchain.vectorStorePGVector - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - AI Agent
@n8n/n8n-nodes-langchain.agent - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger
What it does
🧠 Email real time RAG Assistant with Gmail, OpenAI & PGVector 📌 Who’s it for This workflow is ideal for: - Professionals - Project managers - Sales and support teams - Anyone managing high volumes of Gmail messages It enables fast and intelligent search through your email inbox using natural language queries. --- ⚙️ How it works / What it does - Continuously monitors your Gmail inbox for new emails. - Extracts email content and metadata (subject, body, sender, date). - Converts email content into vector embeddings using OpenAI. - Stores embeddings in a PostgreSQL database with PGVector. - A conversational AI agent performs semantic search on your stored email history. - Supports time-sensitive and context-aware responses via OpenAI Chat model. --- 🚀 How to set up 1. Connect your Gmail account to the Gmail Trigger node (with API access enabled). 2. Configure OpenAI credentials for the Embedding and Chat nodes. 3. Set up a PostgreSQL database with the PGVector extension enabled. 4. Import the workflow into your n8n instance (Cloud or Self-hosted). 5. Customize parameters like polling frequency, embedding settings, or vector query depth. --- 📋 Requirements - ✅ n8n instance (Self-hosted or Cloud) - ✅ Gmail account with API access - ✅ OpenAI API Key - ✅ PostgreSQL database with PGVector extension installed --- 🛠️ How to customize the workflow - Email Filtering: Change filters in the Gmail Trigger to watch specific labels or senders. - Text Splitting Granularity: Adjust chunkSize and chunkOverlap in the text splitter node. - Query Depth: Modify topK in the vector search node to retrieve more or fewer similar results. - Prompt Tuning: Customize the system message or agent instructions in the RAG node. - Workflow Extensions: Add notifications, error logging, Slack/Telegram alerts, or data exports.