This n8n workflow automates the process of providing personalized water drinking reminders. It is triggered via a webhook, likely connected to a scheduling system or a user interaction, and leverages AI language models and vector databases to generate context-aware reminders. The workflow involves capturing input, processing it through AI models, storing and retrieving context via a vector database, and ultimately logging the reminder status while sending alerts on errors.
The steps:
1. Triggered by an external HTTP POST request via webhook.
2. The input text is split into manageable chunks with a text splitter.
3. Embeddings are generated using OpenAI’s small text-embedding model for semantic understanding.
4. Data is stored in a Supabase vector database, enabling similarity searches.
5. The relevant context is retrieved from the database for personalized call-to-action.
6. The context and task are processed through an OpenAI chat model, with a custom prompt to define the reminder task.
7. The resulting response is appended to a Google Sheet log.
8. If an error occurs at any step, a Slack alert is sent for troubleshooting.
This workflow can be useful for health or productivity applications where users receive timely, context-aware reminders to hydrate, improving wellness routines and engagement.
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