Chat-based email search with RAG: Gmail, Qdrant, OpenAI

This automation workflow solves the problem of slow information retrieval from email archives. It transforms email history into a semantically searchable knowledge base, accessible via chat with natural language responses. Built on n8n, LangChain, and Qdrant vector database.

## Who it´s for
- Professionals who rely heavily on business email and need fast access to past information.
- Project managers tracking decisions and agreements from emails.
- Analysts restoring discussion context quickly.
- Entrepreneurs managing high volumes of incoming messages.

## What the automation does
- Connects to Gmail API and retrieves new and existing emails.
- Extracts text and metadata, converts them into embeddings via OpenAI.
- Stores vector representations in Qdrant for semantic search.
- On receiving a chat message, triggers a LangChain RAG agent.
- Searches relevant email fragments and generates coherent answers.
- Automatically indexes new emails without manual intervention.

## What´s included
- Ready-to-use n8n workflow.
- Trigger logic: new email, incoming chat message, manual test trigger.
- Integrations with Gmail, Qdrant, and OpenAI.
- Basic setup and adaptation guide.

## Requirements for setup
- Gmail account with API enabled.
- Running Qdrant instance (local or cloud).
- OpenAI API key.
- n8n platform with node editing access.

## Benefits and outcomes
- Saves time searching for dates, details, agreements in old emails.
- Enables rapid context recovery within minutes.
- Accelerates onboarding by providing instant access to communication history.
- Reduces reliance on personal notes and manual archiving.
- Improves response accuracy through direct references to source emails.
- Continuously enriches knowledge base with every new message.

## Important: template only
Important: you are purchasing a ready-made automation workflow template only. Rollout into your infrastructure, connecting specific accounts and services, 1:1 setup help, custom adjustments for non-standard stacks and any consulting support are provided as a separate paid service at an individual rate. To discuss custom work or 1:1 help, contact via Telegram: @gleb923.