This workflow automates the process of posting jobs to a Notion board by integrating AI-powered text analysis, embedding, and search functionalities, coupled with logging and alerting. It is triggered via a webhook, processing incoming data to generate enriched content, perform semantic searches, and log activities. Practical scenarios include automating job postings, enhancing job description relevance, and maintaining logs for metrics or review.
The workflow starts with a webhook trigger that receives job data submissions. A text splitter node breaks down the incoming text into manageable chunks for detailed analysis. These chunks are then sent to an OpenAI embedding model, which generates vector representations for semantic understanding. The processed embeddings are stored in a Supabase vector database, allowing for efficient similarity searches.
Subsequently, the workflow performs a query on the Supabase vector store to find related content or previous entries, which are then processed by an AI language model to understand or summarize the data. This intelligence is used by a Retrieval-Augmented Generation (RAG) agent, which processes the combined data and generates a response.
The final step logs the response status in a Google Sheet for record-keeping and, if any errors occur, sends an alert message via Slack. This workflow exemplifies how to leverage AI and cloud services for streamlined, intelligent content management in WordPress or Notion-based job boards.
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