Automated Production KPI Data Processing Workflow

This n8n workflow automates the collection, processing, and logging of production KPI data using a combination of webhooks, language model integrations, and data storage. It streamlines the entire process from data reception to analysis, making it ideal for production environments needing real-time KPI updates and insights.

The workflow begins with a webhook trigger that receives production data submissions. This data is then split into manageable chunks by a text splitter node. Each chunk undergoes embedding through Cohere’s API to generate meaningful vector representations, which are then inserted into a Weaviate vector database for efficient retrieval and analysis.

Subsequently, the workflow performs queries on the stored vectors to fetch relevant KPI information, which is processed by a language model (OpenAI) to generate insights, summaries, or automated responses. The interaction with the language model is buffered in memory for context management. The final output, including any generated insights, is logged into a Google Sheet for record-keeping and further analysis.

This automation is particularly useful in production settings where real-time KPI monitoring and analysis are critical, enabling rapid decision-making without manual data handling. The combination of vector storage, language models, and seamless logging ensures a scalable and intelligent KPI dashboard management.