This n8n workflow is designed to facilitate soil nutrient analysis through an automated process that captures, analyzes, and logs soil-related data. It begins with a webhook trigger that captures soil nutrient information submitted via HTTP POST request. The data, structured as text, is then split into smaller fragments using a character-based text splitter to prepare for embedding generation.
The workflow employs Hugging Face embeddings to convert text fragments into numerical vectors, which are then stored into a Weaviate vector database. This setup allows for efficient similarity searches when querying specific soil nutrient information. The stored vectors are queried to retrieve relevant data based on user input.
A language model, connected through Hugging Face, facilitates natural language interactions with the stored data. An agent manages the conversation flow and prompt processing, helping users understand or analyze soil nutrient details. The interaction context is maintained in memory, ensuring coherent exchanges.
After processing, the workflow logs the interaction into a Google Sheet for record-keeping. This comprehensive automation is ideal for agronomists, soil scientists, or agricultural businesses that need to log, analyze, and query soil nutrient data automatically, enhancing decision-making and research efforts.
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