Process Documents with Recursive Chunking using Google Drive, OpenAI & Gemini RAG

1. Document Ingestion & Processing Google Drive Trigger monitors for new files → Loop Over Items processes each file → File Info extracts metadata → Google Drive downloads the actual content → Switch routes to appropriate extractors (PDF or TEXT) based on file type 2. Content Transformation & Chunking Document Data node processes extracted text → Recursive Splitter breaks content into contextual chunks → Chunk Splitting applies intelligent segmentation while preserving document context and relationships between chunks 3. Embedding & Storage Basic LLM Chain processes chunks → OpenAI Chat Model generates contextual understanding → Summarize creates document summaries → Supabase Vector Store sa

Help me set up the n8n workflow "Process Documents with Recursive Chunking using Google Drive, OpenAI & Gemini RAG" (https://n8n.io/workflows/5521). It uses: Google Drive, Code, AI Agent, Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model, Character Text Splitter, Supabase Vector Store, Default Data Loader, OpenAI, Google Gemini Chat Model. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.