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

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. An n8n automation workflow template by Mohsin Ali.

Community Metrics

Categories

• Automation
• AI

Tags

• n8n
• workflow
• google-drive
• code
• ai-agent
• basic-llm-chain
• embeddings-openai
• openai-chat-model
• character-text-splitter
• supabase-vector-store

Capabilities

• 11 nodes
• 262 views

Information