This n8n workflow facilitates efficient management of chat messages in a web-based chat system, integrating Redis for buffering messages, tracking user activity, and automating response triggers. It ensures messages are collected, processed, and responded to based on user inactivity or message threshold criteria.
The workflow starts with a manual trigger or an external trigger, such as a chat message received through a webhook. Incoming messages are stored in Redis buffers to manage batching. The system records timestamps and message counts to monitor user activity.
It evaluates whether sufficient inactivity has occurred or if message count thresholds are met, using Redis-stored data like last seen timestamps and message counters. When these conditions are met, the workflow consolidates messages using an AI language model (GPT-4) for information extraction or response formulation.
The process includes waiting mechanisms to handle inactivity periods and employs Redis to coordinate state, ensuring no duplicate responses are sent. Once the response is generated, the workflow cleans up buffer data, resets flags, and prepares for new messages.
This setup is particularly useful for chatbots, customer support automation, or any system that benefits from batching messages for contextual AI processing, reducing API calls, and maintaining conversation flow with minimal manual intervention.
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