📖 Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG
⚡ 3,048 views · 📖 Internal Wiki & Knowledge Base
Description
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 saves embeddings with metadata → Embeddings OpenAI creates vector representations → Default Data Loader handles storage operations
4. Query Processing & Retrieval
When Clicking Execute triggers user queries → OpenAI processes and understands the question → AI Agent orchestrates hybrid search (combining vector similarity + keyword matching) → Google Gemini Chat Model generates final responses using retrieved context → HTTP Request handles additional external data sources
🔗 Nodes Used
Google Drive, Google Drive Trigger, AI Agent, Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File