Aktien-Quartalsberichte mit Gemini und Pinecone analysieren

# RAG Workflow For Stock Earnings Report Analysis ## Overview Automated workflow: RAG Workflow For Stock Earnings Report Analysis. This workflow integrates 15 different services: stickyNote, vectorStorePinecone, embeddingsGoogleGemini, textSplitterRecursiveCharacterTextSplitter, lmChatGoogleGemini. It contains 25 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 25 - Node Types: 15 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - Pinecone Vector Store: vectorStorePinecone - Embeddings Google Gemini: embeddingsGoogleGemini - Default Data Loader: documentDefaultDataLoader - Recursive Character Text Splitter: textSplitterRecursiveCharacterTextSplitter - Loop Over Items: splitInBatches - When clicking ‘Test workflow’: manualTrigger - AI Agent: agent - Vector Store Tool: toolVectorStore - Google Gemini Chat Model1: lmChatGoogleGemini - OpenAI Chat Model: lmChatOpenAi - ... and 15 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