This workflow automates the gathering, processing, and logging of server health data using n8n. It starts with a webhook trigger that receives incoming server health reports. The data is then split into manageable chunks, embedded using Cohere’s language model, and stored in Weaviate for vector-based similarity search. The workflow further processes the data with a language model from Anthropic, which facilitates intelligent analysis and decision making. Results are logged into Google Sheets for record-keeping and sharing. Additionally, the system sends Slack alerts if any server issues are detected, ensuring quick response times.

This automation is ideal for IT teams or operations monitoring where real-time server health status, anomaly detection, and timely alerts are critical for maintaining system uptime and performance.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.embeddingsCohere, @n8n/n8n-nodes-langchain.lmChatAnthropic, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStoreWeaviate, googleSheets, slack, stickyNote, webhook