Automate Your Email Intelligence with the Email History RAG Workflow
detail.loadingPreview
Unlock the power of your email history by transforming it into a searchable knowledge base. This workflow leverages Retrieval Augmented Generation (RAG) to intelligently process and store your email data, enabling quick access to information through AI.
About This Workflow
This n8n workflow revolutionizes how you interact with your email history. By integrating with OpenAI's language models and Langchain, it creates a powerful Retrieval Augmented Generation (RAG) system. The workflow triggers on new Gmails, extracts essential data like sender, subject, and body, and then processes this information using a character text splitter and embeddings. This processed data is then loaded into a vector database, effectively turning your email archive into a smart, queryable knowledge base. When combined with an OpenAI Chat Model, this setup allows for sophisticated querying and retrieval of information previously buried within your inbox, providing contextually relevant answers powered by your own email communications.
Key Features
- Intelligent Email Ingestion: Automatically captures new emails from Gmail.
- Rich Data Extraction: Extracts sender, recipient, subject, and full body content.
- AI-Powered Knowledge Base: Utilizes RAG and OpenAI embeddings to create a searchable email database.
- Contextual Information Retrieval: Enables AI-driven questioning of your email history.
- Customizable Data Processing: Offers flexibility in how email content is split and prepared for embedding.
How To Use
- Configure Gmail Trigger: Connect your Gmail account and set the polling interval (e.g., every minute).
- Fetch Email Data: Use the 'Get Mail Data' node to retrieve the content of incoming emails.
- Process Email Content: Employ the 'Code' node to clean and structure the email data, extracting key fields like sender, subject, and body.
- Split Text: Utilize the 'Character Text Splitter' to break down large email bodies into manageable chunks for embedding.
- Generate Embeddings: Use the 'Embeddings OpenAI' nodes to create vector representations of your email content.
- Load to Vector Store: Configure the 'Default Data Loader' node to add the processed email data and its embeddings to your chosen vector database, ensuring
created_atanddata_sourcemetadata are included. - Set up RAG Agent: Configure the 'RAG Agent' node with a system message that guides the AI in using the retrieved email data to answer user queries. Connect this to the 'OpenAI Chat Model' for generating responses.
Apps Used
Workflow JSON
{
"id": "b549d09e-4264-4912-b198-5b2a170f4c61",
"name": "Automate Your Email Intelligence with the Email History RAG Workflow",
"nodes": 8,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: b549d09e-4264...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
Statistics
Related Workflows
Discover more workflows you might like
Instant WooCommerce Order Notifications via Telegram
When a new order is placed on your WooCommerce store, instantly receive detailed notifications directly to your Telegram chat. Stay on top of your e-commerce operations with real-time alerts, including order specifics and a direct link to view the order.
On-Demand Microsoft SQL Query Execution
This workflow allows you to manually trigger and execute any SQL query against your Microsoft SQL Server database. Perfect for ad-hoc data lookups, administrative tasks, or quick tests, giving you direct control over your database operations.
Automate Getty Images Editorial Search & CMS Integration
This n8n workflow automates searching for editorial images on Getty Images, extracts key details and embed codes, and prepares them for seamless integration into your Content Management System (CMS), streamlining your content creation process.