Automate Exam Question Generation with n8n and Qdrant
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Streamline the creation of exam questions by leveraging AI-powered text processing and vector storage. This workflow automatically generates questions from your educational content, saving educators valuable time.
About This Workflow
This n8n workflow automates the complex process of generating exam questions from educational materials. It integrates with OpenAI for advanced text embedding and Qdrant for efficient vector storage and retrieval. The process begins by creating or preparing your Qdrant collection. Then, it fetches content from Google Docs, converts it into Markdown format, and splits it into manageable chunks. These chunks are then vectorized using OpenAI embeddings and stored in Qdrant. Finally, a retriever is set up to efficiently query these vectorized documents, forming the foundation for generating contextually relevant exam questions. This robust system significantly reduces manual effort in question creation and ensures consistency.
Key Features
- Automated Content Ingestion: Seamlessly pulls content from Google Docs.
- AI-Powered Embeddings: Utilizes OpenAI for sophisticated text vectorization.
- Efficient Vector Storage: Leverages Qdrant for scalable and fast vector database operations.
- Markdown Conversion: Converts raw document content into a structured Markdown format.
- Configurable Chunking: Allows for customizable text splitting to optimize embedding quality.
How To Use
- Set up Qdrant: Ensure your Qdrant instance is accessible and create a collection (e.g., 'ai_article_test') with appropriate vector size (1536) and distance metric (Cosine).
- Configure Google Docs Node: Replace 'XXXXXXXXXXXXXXXX' in the 'Get Doc' node with the actual URL of your Google Document.
- Set up OpenAI Credentials: Authenticate with your OpenAI API key in the 'Embeddings OpenAI' node.
- Configure Qdrant Credentials: Authenticate with your Qdrant API key/details in the 'Qdrant Vector Store' nodes.
- Adjust Parameters: Fine-tune
chunkSizeandchunkOverlapin the 'Token Splitter' node to optimize content processing. - Run Workflow: Trigger the workflow by clicking 'Test workflow'. The nodes will execute sequentially, fetching, processing, embedding, and storing your document content in Qdrant.
Apps Used
Workflow JSON
{
"id": "4f23bf5d-6c53-4436-b4b2-7c9926a03ed8",
"name": "Automate Exam Question Generation with n8n and Qdrant",
"nodes": 7,
"category": "Operations",
"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: 4f23bf5d-6c53...
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