Automated Quality Defect Classification System

This n8n workflow automates the process of classifying quality defects through a sophisticated AI-driven system integrated with various services. It begins with a webhook trigger that captures defect reports or descriptions submitted via HTTP POST requests. The data is then split into manageable text chunks to facilitate efficient processing. These chunks are transformed into embeddings using OpenAI’s models, enabling semantic analysis and similarity searches. The workflow stores these embeddings in a Redis vector database for quick retrieval.

Subsequently, the system queries the Redis database to find related defect records, assisting in consistency checks or pattern recognition. An advanced language model, such as Anthropic’s GPT, interacts with the retrieved data to generate a detailed defect classification or explanation. The entire interaction is kept in memory to maintain context across steps.

Finally, the workflow logs the resulting classification or insights into a Google Sheet for record-keeping and analysis. This setup is perfect for quality control teams aiming to automate defect analysis, enhance troubleshooting consistency, and maintain detailed logs for ongoing quality improvements.