Real-Time MQTT Monitoring and Intelligent Analysis
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This workflow enables real-time monitoring of MQTT topics, intelligently analyzing incoming data using AI and storing insights for further action. Seamlessly integrate with your IoT devices and gain actionable intelligence.
About This Workflow
The MQTT Topic Monitor workflow leverages n8n to create a powerful real-time data ingestion and analysis pipeline for IoT devices. It begins by receiving data via a webhook, then intelligently splits and embeds this data using AI. The embedded data is stored in a Redis vector store, allowing for efficient querying and retrieval of relevant information. An AI agent, powered by Hugging Face models and equipped with memory, analyzes the queried data and identifies patterns or anomalies. Finally, critical insights and logs are automatically appended to a Google Sheet for persistent storage and review. This workflow transforms raw MQTT data into structured, analyzable information, empowering smarter operational decisions.
Key Features
- Real-time MQTT data ingestion via webhook.
- AI-powered text splitting and embedding for intelligent data processing.
- Efficient data storage and retrieval using Redis Vector Store.
- Advanced AI agent with memory for contextual data analysis.
- Automated logging of insights and events to Google Sheets.
How To Use
- Webhook Setup: Configure the
Webhooknode with your desiredpath(e.g.,mqtt_topic_monitor). This will provide a URL to send your MQTT data to. - Data Splitting: Adjust the
chunkSizeandchunkOverlapparameters in theSplitternode to optimize text segmentation based on your data characteristics. - AI Embeddings: Ensure your OpenAI API credentials are set up correctly in the
Embeddingsnode. - Redis Vector Store (Insert): Configure the
Insertnode with your Redis connection details and specify your desiredindexName(e.g.,mqtt_topic_monitor). This node will store the embedded data. - Redis Vector Store (Query): The
Querynode uses the same Redis index to retrieve relevant information based on AI-driven queries. - AI Agent Configuration: Set up your Hugging Face API credentials in the
Chatnode. Configure theAgentnode's prompt to define how it should process and respond to the data. - Memory: The
Memorynode helps the AI agent maintain context across interactions. - Google Sheets Logging: In the
Sheetnode, provide your Google Sheets API credentials, thedocumentId, and thesheetNamewhere you want to log the processed insights.
Apps Used
Workflow JSON
{
"id": "e86b7694-9f4f-43ee-8723-8cb2e0fb6b47",
"name": "Real-Time MQTT Monitoring and Intelligent Analysis",
"nodes": 28,
"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.
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ID: e86b7694-9f4f...
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