Einen RAG-Agenten mit Milvus und Cohere aufbauen

# RAG AI Agent with Milvus and Cohere ## Overview Automated workflow: RAG AI Agent with Milvus and Cohere. This workflow integrates 13 different services: stickyNote, googleDriveTrigger, textSplitterRecursiveCharacterTextSplitter, agent, googleDrive. It contains 18 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 18 - Node Types: 13 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - Default Data Loader: documentDefaultDataLoader - Embeddings Cohere: embeddingsCohere - When chat message received: chatTrigger - Watch New Files: googleDriveTrigger - Download New: googleDrive - Insert into Milvus: vectorStoreMilvus - RAG Agent: agent - Retrieve from Milvus: vectorStoreMilvus - OpenAI 4o: lmChatOpenAi - Memory: memoryBufferWindow - ... and 8 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