Unlock Instant Knowledge with AI-Powered Retrieval Augmented Generation
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Supercharge your knowledge base by integrating your live data with advanced AI. This workflow leverages Retrieval Augmented Generation (RAG) to enable intelligent Q&A directly from your information sources, ensuring you always have the most up-to-date answers.
About This Workflow
This n8n workflow automates the process of building an intelligent Q&A system powered by Retrieval Augmented Generation (RAG). It dynamically ingests 'living data' from sources like Notion, converts it into searchable embeddings using OpenAI, and then uses these embeddings to answer questions with unprecedented accuracy.
The workflow begins by triggering on changes to your Notion pages or on a schedule, fetching the latest content. This content is then processed and split into manageable chunks, enriched with relevant metadata, and converted into vector embeddings. These embeddings are stored for efficient retrieval. When a question is posed, the system retrieves the most relevant information chunks from your stored embeddings and uses a powerful language model (like GPT-4o) to generate a concise and accurate answer, referencing your live data. This ensures your AI always has access to the most current information, eliminating outdated responses and boosting productivity.
Key Features
- Dynamic Data Ingestion: Automatically pulls the latest information from your Notion knowledge base.
- AI-Powered Q&A: Leverages Retrieval Augmented Generation (RAG) for highly accurate answers based on your live data.
- Intelligent Embeddings: Utilizes OpenAI's embedding models to create a searchable vector database.
- Real-time Updates: Ensures your AI responses are always based on the most current information.
- Customizable Chunking: Allows adjustment of text chunk size for optimal embedding and retrieval.
How To Use
- Trigger Setup: Configure either the
Schedule Triggerfor periodic updates or theNotion Trigger(ensure it's enabled) to react to changes in your Notion database. - Data Fetching: Connect your Notion credentials and specify the database or page you want to use as your knowledge source. The
Get page blocksnode will fetch the content. - Data Preparation: Use the
Default Data Loaderto prepare your content and add metadata (likeidandname) that links back to the original source. - Chunking and Embedding: The
Token Splitternode breaks down large text into smaller, manageable chunks. TheEmbeddings OpenAInode then converts these chunks into vector embeddings using your OpenAI API key. - Vector Store Integration: The
Delete old embeddings if existnode (using Supabase in this example) cleans up previous entries, and theEmbeddings OpenAInode conceptually stores these new embeddings for retrieval. - Q&A Chain: The
OpenAI Chat ModelandVector Store Retrievernodes work together to find the most relevant information based on a query. - Answer Generation: The
Question and Answer Chainutilizes the retrieved information and the chat model to generate a comprehensive answer to the user's question.
Apps Used
Workflow JSON
{
"id": "99dca2f5-551e-4d85-8aea-714bb110ffb6",
"name": "Unlock Instant Knowledge with AI-Powered Retrieval Augmented Generation",
"nodes": 22,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 99dca2f5-551e...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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