Unlock Your Inbox with AI-Powered Email Search
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Transform your email experience with this RAG-powered workflow. It intelligently ingests your Gmail into a searchable vector database, enabling lightning-fast, conversational AI queries about your past communications.
About This Workflow
This n8n workflow is designed to revolutionize how you interact with your email. By leveraging Retrieval-Augmented Generation (RAG), it automatically imports your Gmail messages into a sophisticated vector database. This means you can now go beyond simple keyword searches and engage in natural language conversations with your inbox. Ask complex questions, retrieve specific information based on context, and unlock the full potential of your email history. It's an intelligent assistant for your inbox, ensuring no important detail is ever lost again.
Key Features
- Intelligent Email Ingestion: Automatically pulls new emails from Gmail.
- Conversational AI Search: Query your email history using natural language.
- Vector Database Integration: Stores email data for efficient and context-aware retrieval.
- RAG Architecture: Combines the power of information retrieval with advanced language models.
- Customizable Data Extraction: Extracts key details like sender, recipient, subject, and body.
How To Use
- Trigger New Emails: Configure the 'Gmail Trigger' to monitor your inbox for new messages.
- Fetch Email Content: Use the 'Get Mail Data' node to retrieve the full content of each new email.
- Prepare for Indexing: Employ the 'Enhanced Default Data Loader' to format email content and extract key metadata (like sender, date, subject).
- Split and Embed: Utilize the 'Recursive Character Text Splitter' to break down email content into manageable chunks, and then use 'Embeddings OpenAI' to generate vector embeddings for each chunk.
- Store in Vector DB: Insert the processed data and embeddings into your 'Postgres PGVector Store' for efficient searching.
- Build AI Agent: Configure the 'When chat message received' trigger to listen for user queries.
- Create RAG Agent: Set up the 'New RAG Agent' to process user questions, using the 'Postgres PGVector Store' (configured as a tool) to retrieve relevant email data based on the query and the 'OpenAI Chat Model' to generate a coherent response.
Apps Used
Workflow JSON
{
"id": "eef4fdbd-66f5-4aee-aa3c-298075d603ef",
"name": "Unlock Your Inbox with AI-Powered Email Search",
"nodes": 24,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: eef4fdbd-66f5...
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