Overview
This workflow automates the end-to-end processing of documents stored in Google Drive by leveraging advanced AI and vector storage technologies. It is designed to efficiently handle large volumes of documents, breaking them into manageable chunks for enhanced analysis and retrieval.
Key Features
- Automated Trigger: Initiates processing when new documents are added to Google Drive.
- Recursive Chunking: Splits documents into smaller, logical sections for granular analysis.
- AI Summarization & Embedding: Utilizes OpenAI's Gemini RAG and LLM models for summarization and embedding generation.
- Vector Storage: Stores document embeddings in Supabase for fast semantic search and retrieval.
- Flexible Processing: Switch and batch nodes enable dynamic routing and scalable handling of multiple files.
Benefits
- Time Savings: Eliminates manual document processing, enabling rapid ingestion and analysis.
- Enhanced Searchability: Vector embeddings allow for advanced semantic search and knowledge retrieval.
- Scalability: Handles large document sets efficiently with batch processing and recursive chunking.
- Seamless Integration: Connects Google Drive, OpenAI, and Supabase for a unified automation pipeline.
Use Cases
- Knowledge base creation from company documents
- Automated content summarization for research or compliance
- Semantic search and retrieval for support or analytics teams
