This workflow automates the detection and logging of pest outbreak alerts using an AI-powered system integrated with webhook triggers, natural language processing, and vector similarity search. It streamlines the process of capturing pest-related information, analyzing and storing it efficiently, and logging the results into a Google Sheet for future reference.
The process starts with a webhook that receives pest outbreak data. This data is then split into manageable text chunks using a text splitter node. Each chunk is processed to generate embeddings via Cohere, which are stored in a Pinecone vector database. Before inserting new data, the workflow queries Pinecone to check for existing similar records, ensuring that duplicate alerts are minimized.
An AI-powered agent leverages these embeddings to interpret the context and define the alert details. These insights are then processed through a chat model (OpenAI), which further refines the information. The final output is appended as a log entry into a Google Sheet, allowing easy tracking and management of pest outbreak alerts.
This workflow is particularly useful for pest control companies, agricultural agencies, or environmental monitoring programs that need timely alerts and detailed records of pest outbreaks, enabling quicker responses and data-driven decision making.
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