RAG on Living Data
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This workflow enables Retrieval Augmented Generation (RAG) on dynamically updated data sources.
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About This Workflow
Overview
This workflow implements a Retrieval Augmented Generation (RAG) system that can process and query information from 'living' or continuously updated data sources. It leverages OpenAI's embedding and chat models to enable intelligent question answering over relevant documents.
Key Features
- Ingests and processes data from various sources (e.g., Notion).
- Utilizes OpenAI for text embedding and chat completion.
- Supports dynamic data updates and retrieval.
- Splits text into manageable chunks for efficient processing.
- Stores and retrieves information from a vector store (e.g., Supabase).
How To Use
- Configure your data source (e.g., Notion) and OpenAI API credentials.
- Set up the vector store (e.g., Supabase) to store embeddings.
- Define the trigger mechanism (e.g., schedule, webhook, or Notion trigger) to initiate data processing.
- Adjust chunking and embedding parameters as needed for optimal performance.
Apps Used
Workflow JSON
{
"id": "6c8c5783-bfb3-4b81-ae00-3d3ab17904ef",
"name": "RAG on Living Data",
"nodes": 0,
"category": "AI & Machine Learning",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 6c8c5783-bfb3...
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