Chat with GitHub OpenAPI Spec Using Pinecone and OpenAI

# Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI) ## Overview Automated workflow: Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI). This workflow integrates 13 different services: stickyNote, httpRequest, vectorStorePinecone, textSplitterRecursiveCharacterTextSplitter, agent. It contains 23 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 23 - Node Types: 13 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - When clicking ‘Test workflow’: manualTrigger - HTTP Request: httpRequest - Pinecone Vector Store: vectorStorePinecone - Default Data Loader: documentDefaultDataLoader - Recursive Character Text Splitter: textSplitterRecursiveCharacterTextSplitter - When chat message received: chatTrigger - AI Agent: agent - OpenAI Chat Model: lmChatOpenAi - Window Buffer Memory: memoryBufferWindow - Vector Store Tool: toolVectorStore - ... and 13 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