Slack AI Chatbot with RAG for Company Staff
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An AI chatbot integrated with a Retrieval Augmented Generation (RAG) system to answer company-related queries on Slack.
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About This Workflow
Overview
This workflow sets up an AI chatbot within Slack that leverages Retrieval Augmented Generation (RAG) to access and retrieve information from your company's internal knowledge base. It allows staff members to ask questions directly in Slack and receive accurate, contextually relevant answers drawn from company documents.
Key Features
- Answers questions using company documents via RAG.
- Integrates seamlessly with Slack for easy access.
- Provides contextually rich and accurate responses.
- Cites document sources for verification.
- Customizable system message for AI persona.
- Supports threaded replies in Slack.
How To Use
- Configure Slack Trigger: Set up the
Get messagenode to listen for app mentions (app_mention) in a specific Slack channel. - Configure AI Agent: Customize the
AI Agentnode's system message to define the chatbot's persona and responsibilities. - Configure RAG: Set up the
RAGnode (vectorStoreQdrant) to connect to your Qdrant collection and define the tool description. - Configure Embeddings: Ensure the
Embeddings OpenAI1node is correctly set up with your OpenAI credentials. - Configure Memory (Optional but recommended): Set up the
Simple Memorynode to manage conversation context. - Configure Slack Send Message: Ensure the
Send messagenode is configured to reply in the correct Slack channel and thread.
Apps Used
Workflow JSON
{
"id": "1cf10812-722a-45b8-afea-b1fe42a0b17e",
"name": "Slack AI Chatbot with RAG for Company Staff",
"nodes": 0,
"category": "Communication",
"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: 1cf10812-722a...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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Verification Info
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