RAG Chatbot for Movie Recommendations with Qdrant and OpenAI

# Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI ## Overview Automated workflow: Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI. This workflow integrates 20 different services: stickyNote, vectorStoreQdrant, merge, lmChatOpenAi, github. It contains 38 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 38 - Node Types: 20 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - When clicking ‘Test workflow’: manualTrigger - GitHub: github - Extract from File: extractFromFile - Embeddings OpenAI: embeddingsOpenAi - Default Data Loader: documentDefaultDataLoader - Token Splitter: textSplitterTokenSplitter - Qdrant Vector Store: vectorStoreQdrant - When chat message received: chatTrigger - OpenAI Chat Model: lmChatOpenAi - Call n8n Workflow Tool: toolWorkflow - ... and 28 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