Агент планирования поездок с векторным поиском Couchbase и Gemini

# Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI ## Overview Automated workflow: Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI. This workflow integrates 11 different services: webhook, stickyNote, textSplitterRecursiveCharacterTextSplitter, vectorStoreCouchbaseSearch, lmChatGoogleGemini. It contains 18 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 18 - Node Types: 11 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - When chat message received: chatTrigger - Google Gemini Chat Model: lmChatGoogleGemini - Sticky Note: stickyNote - Webhook: webhook - Default Data Loader: documentDefaultDataLoader - Recursive Character Text Splitter: textSplitterRecursiveCharacterTextSplitter - Sticky Note1: stickyNote - Simple Memory: memoryBufferWindow - AI Travel Agent: agent - Retrieve docs with Couchbase Search Vector: vectorStoreCouchbaseSearch - ... 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