Summarize Customer Emails Using AI and RAG
detail.loadingPreview
This workflow automates the summarization of customer emails using an AI RAG (Retrieval Augmented Generation) agent. It leverages Weaviate as a vector store to retrieve relevant context for accurate summaries.
🚀Ready to Deploy This Workflow?
About This Workflow
Overview
This n8n workflow is designed to automatically summarize incoming customer emails. It utilizes a Retrieval Augmented Generation (RAG) approach, which combines the power of a language model with a knowledge retrieval system.
- Webhook Trigger: Receives incoming customer emails.
- Text Splitter: Breaks down the email content into manageable chunks.
- Embeddings: Converts these text chunks into vector embeddings using Cohere.
- Weaviate Insert: Stores these embeddings in a Weaviate vector database for efficient retrieval.
- Weaviate Query: When a new email arrives, it queries Weaviate to find similar past email embeddings, providing context.
- Vector Tool: Processes the retrieved vector information.
- Window Memory: Maintains a history of interactions for context.
- Chat Model: An Anthropic chat model processes the query and retrieved context.
- RAG Agent: The core AI component that orchestrates the retrieval and generation of the summary.
- Append Sheet: Logs the summarization status to a Google Sheet.
- Slack Alert: Sends an alert to Slack in case of any errors.
This workflow is ideal for quickly digesting large volumes of customer communication, identifying key issues, and improving response times.
Key Features
- Automated email summarization
- Retrieval Augmented Generation (RAG) for context-aware summaries
- Integration with Weaviate for efficient vector storage and retrieval
- Logging of summarization status to Google Sheets
- Error alerting via Slack
How To Use
- Set up Webhook Trigger: Configure the
Webhook Triggernode with your desired path (e.g.,/summarize-customer-emails). - Configure Text Splitter: Adjust
chunkSizeandchunkOverlapin theText Splitternode based on your email content length. - Set up Cohere Embeddings: Connect your Cohere API credentials to the
Embeddingsnode. - Configure Weaviate: Set up your Weaviate instance and connect your credentials to
Weaviate InsertandWeaviate Querynodes. Ensure theindexNamematches. - Configure Chat Model: Connect your Anthropic API credentials to the
Chat Modelnode. - Define RAG Agent Prompt: Customize the system message and text in the
RAG Agentnode for optimal summarization. - Configure Google Sheets Logging: Connect your Google Sheets credentials and specify the
SHEET_IDandLogsheet name in theAppend Sheetnode. - Configure Slack Alerts: Connect your Slack API credentials to the
Slack Alertnode to receive error notifications.
Apps Used
Workflow JSON
{
"id": "f5840d41-2401-49cb-9085-98b6215049ba",
"name": "Summarize Customer Emails Using AI and RAG",
"nodes": 0,
"category": "AI/ML",
"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: f5840d41-2401...
About the Author
Free n8n Workflows Official
System Admin
The official repository for verified enterprise-grade workflows.
Statistics
Verification Info
Related Workflows
Discover more workflows you might like
Customer Sentiment Analysis Using RAG and Webhook Trigger
Automate customer sentiment analysis by integrating a webhook trigger with a RAG agent. This workflow processes incoming data, leverages Pinecone for vector storage, and logs results to a Google Sheet.
RAG Document Update and Management with Qdrant and Google Drive
Automates RAG system updates using Qdrant vector store and Google Drive documents.
OpenAI Assistant for File Retrieval with Citation Formatting
Automates generating structured metadata from OpenAI assistant responses, ensuring citations and file sources are correctly identified and formatted.