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Automated Slack IT helpdesk with GPT, Supabase vector search, and JIRA ticketing

inderjeet Bhambrainderjeet Bhambra
915 views
2/3/2026
n8n
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This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

Who is this for?

IT teams and support organizations looking to automate Level 1 support with AI-powered assistance while maintaining proper ticket management workflows.

What problem does this solve?

Eliminates repetitive manual support tasks by providing instant, context-aware assistance that references organizational knowledge and creates structured tickets when needed.

What this workflow does

  • RAG Pipeline: Processes PDF/CSV documents into searchable vector database
  • Intelligent Slack Bot: This AI-helpdesk assistant handles support requests with thread-aware conversations
  • Vector Knowledge Search: Searches embedded knowledge base articles and historical case data
  • JIRA Integration: Creates, searches, and manages support tickets automatically
  • Emoji Reactions: Users can trigger actions (create tickets, escalate) via emoji reactions

Requirements

Required Accounts:

  • n8n Cloud or self-hosted instance
  • Slack workspace with admin access
  • Supabase account (vector database)
  • JIRA Cloud instance
  • OpenAI API key

Technical Prerequisites:

  • Basic n8n workflow knowledge
  • Slack app creation experience
  • Understanding of vector databases

Setup Steps

1. Slack App Configuration

  • Create new Slack app with Bot Token Scopes: app_mentions:read, channels:history, channels:read, groups:history, groups:read, im:history, im:read, mpim:history, mpim:read, users:read
  • Configure Event Subscriptions: app_mention, message.channels, message.groups, reaction_added
  • Set Request URL to your n8n Slack Trigger webhook

2. Supabase Vector Database Setup

  • Create new Supabase project
  • Enable pgvector extension
  • Create documents table with vector column (1536 dimensions for OpenAI embeddings)
  • Configure RLS policies for secure access

3. JIRA Configuration

  • Generate API token from JIRA Cloud
  • Create helpdesk project with appropriate issue types
  • Note project ID and issue type IDs for workflow configuration

4. n8n Workflow Configuration

  • Import workflow and configure credentials
  • Update Slack channel IDs in trigger nodes
  • Set OpenAI API key in all OpenAI nodes
  • Configure Supabase connection in vector store nodes
  • Update JIRA project settings in MCP server nodes

5. Knowledge Base Data Format

Supported file formats: PDF, CSV CSV Structure: Structure your data with columns, but not limited to, Ticket#, Issue Description, Issue Summary, Resolution Provided, Case Status, Contact User PDF Content: Technical documentation, troubleshooting guides, policy documents

Upload documents via the form trigger to automatically embed in vector database.

Customization Options

AI Agent Behavior

  • Modify system prompt in AIHelpdesk Agent node
  • Adjust conversation memory window (default: 20 messages)
  • Change AI model (GPT-4o, GPT-3.5-turbo, etc.)

Reaction Mappings

  • Customize emoji-to-action mappings in Reaction Handler code
  • Add new reaction types for department-specific workflows
  • Configure escalation rules and priority levels

JIRA Integration

  • Customize ticket templates and fields
  • Add auto-assignment rules based on issue type
  • Configure SLA and priority mappings

Vector Search

  • Adjust similarity thresholds for knowledge retrieval
  • Modify search result limits and relevance scoring
  • Add metadata filtering for departmental knowledge bases

Advanced Features

  • Thread-aware conversation memory
  • Automatic bot loop prevention
  • Context-preserving ticket creation
  • Multi-modal file processing (PDF + CSV)
  • Scalable MCP architecture for tool integration

Use Cases

  • Level 1 IT Support: Automate common troubleshooting workflows
  • Employee Onboarding: Answer policy and procedure questions
  • Internal Help Desk: Route and track internal service requests
  • Knowledge Management: Make organizational knowledge searchable and actionable

Template includes

  • Complete Slack integration with thread support
  • RAG pipeline for document processing
  • Vector similarity search implementation
  • JIRA ticket lifecycle management
  • Emoji reaction-based user interactions
  • Comprehensive error handling and validation

n8n AI Agent Workflow for Slack and MCP

This n8n workflow demonstrates a basic AI agent setup using Langchain components, triggered by either a Slack message or an n8n form submission, or a Model Context Protocol (MCP) server trigger. It processes incoming data, potentially applies transformations, and then leverages an AI agent.

What it does

This workflow showcases the following capabilities:

  1. Listens for Events: It can be triggered by:
    • A new message in a configured Slack channel (using the "Slack Trigger" node).
    • A submission to an n8n form (using the "n8n Form Trigger" node).
    • An incoming request to an MCP Server (using the "MCP Server Trigger" node).
  2. Conditional Logic: An "If" node allows for branching logic, though its specific condition is not defined in the provided JSON.
  3. Data Transformation: An "Edit Fields (Set)" node is available for modifying or setting data fields within the workflow.
  4. AI Agent Processing: An "AI Agent" node (Langchain Agent) is the core of the AI processing, which can utilize various tools and models.
  5. AI Components: It includes sub-nodes for:
    • OpenAI Chat Model: For conversational AI capabilities.
    • Embeddings OpenAI: To generate vector embeddings for text.
    • Supabase Vector Store: To store and retrieve vector embeddings, likely for RAG (Retrieval Augmented Generation) or knowledge base lookups.
    • Default Data Loader: A generic document loader for AI processing.
    • Simple Memory: To maintain conversational context for the AI agent.
  6. MCP Client Tool: An "MCP Client" node suggests interaction with another Model Context Protocol server.
  7. Slack Notification: A "Slack" node can be used to send messages or notifications to Slack channels.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n instance: A running n8n instance.
  • Slack Account: With a Slack App configured for the "Slack Trigger" and "Slack" nodes (API token/credential).
  • OpenAI API Key: For the "OpenAI Chat Model" and "Embeddings OpenAI" nodes.
  • Supabase Account: Configured for the "Supabase Vector Store" node (API URL, API Key, and potentially a table name).
  • Langchain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up your Slack API credentials for both the "Slack Trigger" and "Slack" nodes.
    • Configure your OpenAI API Key credentials for the "OpenAI Chat Model" and "Embeddings OpenAI" nodes.
    • Provide your Supabase credentials (Project URL and anon key) for the "Supabase Vector Store" node.
  3. Configure Trigger Nodes:
    • Slack Trigger: Specify the Slack channel(s) or events you want to listen to.
    • n8n Form Trigger: Activate the form and note its URL if you intend to use it.
    • MCP Server Trigger: This node acts as an endpoint for other MCP clients.
  4. Configure AI Agent and Tools:
    • Review the "AI Agent" node and its connected sub-nodes ("OpenAI Chat Model", "Embeddings OpenAI", "Supabase Vector Store", "Default Data Loader", "Simple Memory", "MCP Client") to ensure they are configured according to your specific use case. This includes setting models, prompts, memory parameters, and vector store details.
  5. Activate the Workflow: Once all configurations are complete, activate the workflow.

This workflow provides a flexible foundation for building AI-powered automations, especially for integrating with Slack and leveraging vector search capabilities.

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