Automated Large Document Upsert to Supabase Vector Store via Notion and OpenAI

Overview

This workflow automates the ingestion and upsert of large documents from Notion into a Supabase vector store, leveraging OpenAI for embeddings and advanced retrieval-based question answering.

Key Features

• Document Splitting: Uses token-based splitting to handle large Notion documents efficiently.
• OpenAI Embeddings: Generates vector embeddings for each document chunk via OpenAI's API.
• Batch Processing: Processes documents in manageable batches for scalability.
• Vector Store Integration: Upserts processed embeddings into Supabase, enabling semantic search and retrieval.
• Retrieval QA: Integrates a retrieval-based QA chain for intelligent querying of stored content.
• Automated Triggers: Supports both scheduled and chat-based triggers for flexible automation.

Benefits

• Scalable Knowledge Management: Handles massive documents without manual intervention.
• Enhanced Searchability: Enables semantic search and contextual Q&A over company knowledge bases.
• Time Savings: Automates repetitive data processing, reducing manual workload.
• Seamless Integrations: Connects Notion, OpenAI, and Supabase for a unified workflow.

Use Cases

• Building internal knowledge bases with advanced search.
• Automating ingestion of meeting notes, wikis, or documentation for instant Q&A.
• Enabling AI-powered support bots with up-to-date company information.