🧠 RAG with Full Gmail history + Real time email updates in RAG using OpenAI & Qdrant

Summary:
This workflow listens for new Gmail messages, extracts and cleans email content, generates embeddings via OpenAI, stores them in a Qdrant vector database, and then enables a Retrieval‑Augmented‑Generation (RAG) agent to answer user queries against those stored emails. It’s designed for teams or bots that need conversational access to past emails.

🧑‍🤝‍🧑 Who’s it for

• Support teams who want to surface past customer emails in chatbots or help‑desk portals
• Sales ops that need AI‑powered summaries and quick lookup of email histories
• Developers building RAG agents over email archives

⚙️ How it works / What it does

1. Trigger
   • Gmail Trigger polls every minute for new messages.
2. Fetch & Clean
   • Get Mail Data pulls full message metadata and body.
   • Code node normalizes the body (removes line breaks, collapses spaces).
3. Embed & Store
   • Embeddings OpenAI node computes vector embeddings.
   • Qdrant Vector Store inserts embeddings + metadata into the emails_history collection.
4. Batch Processing
   • SplitInBatches handles large inbox loads in chunks of 50.
5. RAG Interaction
   • When chat message received → RAG Agent → uses Qdrant Email Vector Store as a tool to retrieve relevant email snippets before responding.
6. Memory
   • Simple Memory buffer ensures the agent retains recent context.

🛠️ How to set up

1. n8n Instance
   • Deploy n8n (self‑hosted or via Coolify/Docker).
2. Credentials
   • Create an OAuth2 credential in n8n for Gmail (with Gmail API scopes).
   • Add your OpenAI API key in n8n credentials.
3. Qdrant
   • Stand up a Qdrant instance (self‑hosted or Qdrant Cloud).
   • Note your host, port, and API key (if any).
4. Import Workflow
   • In n8n, go to Workflows → Import → paste the JSON you provided.
   • Ensure each credential reference (Gmail & OpenAI) matches your n8n credential IDs.
5. Test
   • Click Execute Workflow or send a test email to your Gmail.
   • Monitor n8n logs: you should see new points in Qdrant and RAG responses.

📋 Requirements

• n8n (Self-hosted or Cloud)
• Gmail API enabled on a Google Cloud project
• OpenAI API access (with Embedding & Chat endpoints)
• Qdrant (hosted or cloud) with a collection named emails_history

🎨 How to customize the workflow

• Change Collection Name
  • Update the qdrantCollection.value in all Qdrant nodes if you prefer a different collection.
• Adjust Polling Frequency
  • In the Gmail Trigger node, switch from everyMinute to everyFiveMinutes or a webhook‑style trigger.
• Metadata Tags
  • In Enhanced Default Data Loader, tweak the metadataValues to tag by folder, label, or sender domain.
• Batch Size
  • In SplitInBatches, change batchSize to suit your inbox volume.
• RAG Agent Prompt
  • Customize the systemMessage in the RAG Agent node to set the assistant’s tone, instruct on date handling, or add additional tools.
• Additional Tools
  • Chain other n8n nodes (e.g., Slack, Discord) after the RAG Agent to broadcast AI answers to team channels.