RAG on live data

How it works

1. 1.Trigger workflow via Notion event or scheduled interval to fetch live data
2. 2.Load documents and split them into semantically meaningful chunks using tokenization
3. 3.Convert document chunks to vector embeddings via OpenAI and persist in Supabase vector store
4. 4.Process user queries through chat interface with semantic similarity search across stored vectors
5. 5.Generate precise, contextual answers using LLM Chain and optional summarization

Use cases

• Enable employees to ask natural language questions about company documents and policies updated in real-time
• Extract relevant information from large document databases and automatically answer complex queries
• Build AI-powered knowledge assistants that combine multiple data sources for comprehensive responses

Requirements

• OpenAI API keys for embeddings and language model access
• Active Supabase database with vector extension enabled
• Notion workspace with database access and API integration
• Documents in supported formats (PDF, Markdown, plain text)

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.