Automated Ticket Urgency Classification with RAG and AI
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This workflow leverages a RAG Agent, Pinecone, and AI models to automatically classify ticket urgency. It processes incoming tickets via a Webhook Trigger, splits text, generates embeddings, and stores them in Pinecone for intelligent classification and logging.
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About This Workflow
Overview
This n8n workflow automates the critical task of classifying ticket urgency using a Retrieval-Augmented Generation (RAG) approach powered by AI. It's designed to streamline support operations by quickly identifying the priority of incoming support requests. The workflow begins with a Webhook Trigger to receive new ticket data. The Text Splitter node breaks down the ticket content into manageable chunks. These chunks are then converted into numerical representations using the Embeddings node (Cohere's embed-english-v3.0). The generated embeddings are stored and indexed in a Pinecone Insert vector database for efficient retrieval. When a new ticket arrives, a Pinecone Query searches for relevant historical ticket information within the vector store. This retrieved context is combined with the new ticket data and fed into a RAG Agent (leveraging an Anthropic chat model and Window Memory) which determines the ticket's urgency. The final classification is then logged to a Google Sheet using the Append Sheet node, with errors being reported to Slack via the Slack Alert node.
Key Features
- Real-time ticket urgency classification via Webhook.
- Utilizes Retrieval-Augmented Generation (RAG) for context-aware classification.
- Embeddings generation using Cohere's English model.
- Vector storage and retrieval with Pinecone.
- Error handling with Slack alerts.
- Automatic logging of classifications to Google Sheets.
How To Use
- Set up your Pinecone index named
ticket_urgency_classification. - Configure the
Webhook Triggerto receive ticket data. - Set up credentials for Cohere, Anthropic, Pinecone, and Google Sheets.
- Update the
SHEET_IDandLogsheet name in theAppend Sheetnode. - Adjust
Text Splitterparameters (chunkSize,chunkOverlap) for optimal performance. - The
RAG Agentwill use the provided system message for classification.
Apps Used
Workflow JSON
{
"id": "93c5ff92-2660-4011-984c-25f04a9a7016",
"name": "Automated Ticket Urgency Classification with RAG and AI",
"nodes": 0,
"category": "AI/ML Automation",
"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: 93c5ff92-2660...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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