This n8n workflow automates the process of generating and storing daily motivational messages for students, ensuring consistent engagement. When triggered via a webhook, it processes input data through several steps involving text splitting, embedding, and vector storage to analyze and retrieve relevant motivational content. The workflow leverages AI models for text understanding, uses Weaviate as a vector database for storing and querying embeddings, and employs a language model to generate tailored motivational messages. It logs the results into a Google Sheet for record-keeping and has an alert system via Slack to notify of any errors.
Key Steps:
1. The process starts with an HTTP webhook trigger, receiving daily input.
2. The input text is split into manageable chunks by the Text Splitter node.
3. Each chunk is converted into vector embeddings using OpenAI’s embedding model.
4. Embeddings are stored in Weaviate for efficient retrieval.
5. The system queries Weaviate to find relevant past motivational content.
6. The retrieved vectors are processed through a Vector Tool and Buffer Memory to maintain context.
7. A conversational AI model synthesizes a personalized motivational message based on the retrieved data.
8. The generated message is then logged into a Google Sheet, maintaining a daily record.
9. If an error occurs at any step, a Slack notification alerts the team.
Practical Use Cases:
This workflow is ideal for educational institutions or motivational services wanting to automate daily morale boosts for students, ensuring consistent support and engagement without manual effort.
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