Automated Return Ticket Assignment with Webhook and RAG Agent
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This workflow automates return ticket assignments using a Webhook trigger. It leverages LangChain nodes for text splitting, embeddings, vector storage with Supabase, and a RAG agent to process incoming data, logging the status to Google Sheets and alerting on errors via Slack.
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About This Workflow
Overview
This n8n workflow is designed to automate the assignment or processing of return tickets. It utilizes a Webhook trigger to receive incoming ticket data, which is then processed through a series of LangChain nodes. The core logic involves splitting the incoming text, generating embeddings, storing these embeddings in a Supabase vector database for retrieval, and then using a RAG (Retrieval Augmented Generation) agent powered by an OpenAI Chat Model to interpret and act upon the data. The outcome is logged to a Google Sheet, and any errors are immediately reported via Slack.
This workflow is particularly useful for scenarios where external systems need to trigger ticket processing automatically, and the system needs to intelligently understand and categorize or assign these tickets based on their content.
Key Features
- Webhook Trigger: Accepts incoming data via a configurable webhook path.
- Text Processing with LangChain: Utilizes
Text Splitterfor chunking data andEmbeddings(Cohere) for vector representation. - Vector Database Integration: Stores and retrieves data using
Supabaseas a vector store. - RAG Agent: Implements a Retrieval Augmented Generation agent for intelligent data interpretation.
- AI Chat Model: Leverages OpenAI's chat models for conversational AI capabilities.
- Google Sheets Logging: Appends the processing result to a specified Google Sheet.
- Slack Error Alerting: Notifies a designated Slack channel in case of workflow errors.
How To Use
- Configure Webhook Trigger: Set up the
Webhook Triggernode with the desired HTTP method (POST) and path (e.g.,return-ticket-assignment). - Set up Text Processing: Configure the
Text Splitternode to segment incoming text data and theEmbeddingsnode to generate vector representations using a chosen model (e.g., Cohere). - Integrate Supabase: Connect your Supabase account and configure
Supabase Insertto store embeddings andSupabase Queryto retrieve relevant information. Ensure theindexNamematches across these nodes. - Configure RAG Agent: Set up the
Chat Model(e.g., OpenAI) andWindow Memory. Then, configure theRAG Agentwith the appropriate system message and input text, ensuring it can utilize theVector ToolandWindow Memory. - Set up Google Sheets: Configure the
Append Sheetnode with your Google Sheets credentials, the target Sheet ID, and the sheet name. Map the output from theRAG Agentto the desired column (e.g., 'Status'). - Configure Slack Alerts: Connect your Slack account and set up the
Slack Alertnode to receive error notifications.
Apps Used
Workflow JSON
{
"id": "7a11b249-944d-45b0-8062-ab5ee82b47f8",
"name": "Automated Return Ticket Assignment with Webhook and RAG Agent",
"nodes": 0,
"category": "Misc",
"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: 7a11b249-944d...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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