RAG on Living Data
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Integrates RAG (Retrieval-Augmented Generation) with dynamic, living data sources to provide context-aware answers.
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About This Workflow
Overview
This workflow implements a Retrieval-Augmented Generation (RAG) system that continuously processes and indexes living data. It leverages OpenAI embeddings and a vector store retriever to provide up-to-date, contextually relevant answers to user queries.
Key Features
- Real-time data ingestion and indexing from Notion.
- Dynamic updates of embeddings based on Notion page modifications.
- Token splitting for efficient processing of large text chunks.
- OpenAI's GPT models for generating responses.
- Vector store for efficient semantic search and retrieval.
How To Use
- Configure the Notion credentials and database ID.
- Ensure OpenAI API credentials are set up.
- Connect to your vector store (e.g., Supabase).
- Adjust chunk size and overlap in the 'Token Splitter' node for optimal performance.
- The workflow will automatically process updates from the specified Notion database.
Apps Used
Workflow JSON
{
"id": "6b7c28d8-3b53-4108-a379-3ef84c2c5ffb",
"name": "RAG on Living Data",
"nodes": 0,
"category": "Community Contributed",
"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: 6b7c28d8-3b53...
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N8N_Community_Pick
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