The ‘Maintenance Ticket Router’ n8n workflow is designed to automatically process, classify, and log maintenance tickets submitted via a webhook. It streamlines issue management by integrating AI-driven natural language processing, vector search, and data logging to enhance maintenance operations.
The workflow begins with an HTTP webhook triggered by new ticket submissions. The raw ticket content is split into manageable chunks using a character-based text splitter. These chunks are converted into embeddings via the Cohere API, which are then stored in a Supabase vector database. The stored embeddings facilitate similarity searches when a user submits a new query, enabling quick retrieval of relevant past tickets or knowledge.
When a new ticket or query is received, the system searches the vector store for related issues to assist in diagnosing or categorizing the problem. This information is processed through a language model (Hugging Face API) to generate a meaningful response or classification.
The workflow also employs an AI agent that interprets the ticket content and context, assisted by stored memory buffers, to further refine responses or actions. Finally, key details and logs of each processed ticket are appended to a Google Sheet for record-keeping and auditing.
This automation is ideal for large maintenance teams looking to optimize ticket handling, reduce response times, and maintain thorough records for continuous improvement and accountability.
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