This n8n workflow automates the process of capturing, analyzing, and recording harvest log entries with an AI-powered system. It starts with an HTTP POST webhook, allowing users or systems to submit harvest data in text format. The submitted data is then split into manageable chunks using a text splitter node, preparing it for embedding and semantic analysis.
The workflow enhances the data with OpenAI embeddings, which are stored in Pinecone, a vector database optimized for similarity searches. These embeddings enable efficient retrieval of relevant log entries based on user queries. The system also includes an AI language model, integrated via Hugging Face, which can generate insights or summaries and respond to queries about the log data.
Further, the workflow incorporates a memory buffer to maintain context during interactions, ensuring coherent AI responses. Users can define and refine their queries or commands through an agent node, which interprets the input and interacts with the language model. The final step features appending the processed log data into a Google Sheets document, providing a structured, accessible record.
This workflow is ideal for organizations or individuals managing extensive harvest activities, offering real-time logging, intelligent analysis, and organized record-keeping without manual intervention, thus streamlining operational oversight and decision-making.
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