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RAG & Knowledge Bases

Business WhatsApp RAG Chatbot with OpenAI and Qdrant Copy

When a WhatsApp message arrives by webhook, an OpenAI agent answers from business documents from Google Drive indexed in Qdrant.

What it does

# Business WhatsApp AI RAG Chatbot ## Overview Automated workflow: Business WhatsApp AI RAG Chatbot. This workflow integrates 17 different services: webhook, stickyNote, httpRequest, textSplitterTokenSplitter, vectorStoreQdrant. It contains 44 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 44 - Node Types: 17 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - Respond to Webhook: respondToWebhook - AI Agent: agent - Sticky Note: stickyNote - OpenAI Chat Model: lmChatOpenAi - When clicking ‘Test workflow’: manualTrigger - Qdrant Vector Store: vectorStoreQdrant - Create collection: httpRequest - Refresh collection: httpRequest - Get folder: googleDrive - Download Files: googleDrive - ... and 34 more nodes ## Usage Instructions 1. Configure Credentials: Set up all required API keys and credentials 2. Update Variables: Replace any placeholder values with actual data 3. Test Workflow: Run in test mode to verify functionality 4. Deploy: Activate the workflow for production use ## Security Notes - All sensitive data has been removed or replaced with placeholders - Error handling is implemented for reliability - Follow security best practices when configuring credentials ## Troubleshooting - Check error logs if workflow fails - Verify all credentials are properly configured - Ensure all required services are accessible

The steps

  1. Respond to Webhook
  2. AI Agent
  3. OpenAI Chat Model
  4. When clicking ‘Test workflow’
  5. Qdrant Vector Store
  6. Create collection
  7. Refresh collection
  8. Get folder
  9. Download Files
  10. Embeddings OpenAI
  11. Default Data Loader
  12. Token Splitter
  13. Verify
  14. Respond

74 steps in total.

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