Automate Crop Yield Prediction with n8n and LangChain
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This n8n workflow leverages LangChain to predict crop yields by processing input data, embedding it, and querying a vector store. It uses Webhook for input and Google Sheets for logging predictions.
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About This Workflow
Overview
This n8n workflow is designed to automate the prediction of crop yields. It acts as a powerful tool for agricultural professionals and researchers by integrating with LangChain's natural language processing capabilities and a vector store (Supabase) for efficient data retrieval. The workflow begins by receiving data via a Webhook, which is then processed and embedded. This embedding is stored in Supabase for future querying. When a prediction is requested, the system queries the vector store, utilizes a language model with memory, and an agent to formulate a response, ultimately logging the prediction to a Google Sheet. This automation streamlines the process of analyzing agricultural data for yield forecasting.
Key Features
- Webhook Integration: Accepts data inputs for crop yield prediction requests.
- Text Processing: Utilizes LangChain's
textSplitterCharacterTextSplitterto segment input data for effective embedding. - Vector Embeddings: Employs HuggingFace embeddings to convert text data into numerical vectors for semantic understanding.
- Vector Store Management: Integrates with Supabase to store and retrieve embedded data, enabling efficient querying.
- AI Agent and Memory: Leverages LangChain's
agentandmemoryBufferWindowto provide contextual understanding and generate intelligent responses. - Logging: Automatically logs prediction results to a Google Sheet for record-keeping and analysis.
How To Use
- Set up Webhook: Configure the
Webhooknode to receive crop data. Ensure thepathis set tocrop_yield_predictoror as desired. - Configure LangChain Nodes:
- Splitter: Adjust
chunkSizeandchunkOverlapin theSplitternode to optimize text segmentation. - Embeddings: Select your desired embedding model and ensure your HuggingFace API credentials are set up.
- Vector Store (Insert): Configure the
Insertnode with your Supabase project details (indexNameshould match your Supabase table/index). - Vector Store (Query): Configure the
Querynode with the sameindexNameas theInsertnode. - Tool: The
Toolnode connects the vector store for retrieval. - Memory: Configure the
Memorynode (e.g.,memoryBufferWindow) to maintain conversation context. - Chat: Select your preferred language model (e.g., OpenAI) and set up its API credentials.
- Agent: The
Agentnode orchestrates the AI's decision-making process, using the tools and memory. Thetextparameter should be set to={{ $json }}to pass the incoming request data.
- Splitter: Adjust
- Google Sheets Integration: Set up the
Sheetnode with your Google Sheets API credentials and specify thedocumentIdandsheetNamewhere you want to log the predictions. - Connect Nodes: Ensure all nodes are correctly connected according to the workflow's logic, especially the output of one node feeding into the input of the next.
- Trigger the Workflow: Send data to the configured Webhook URL to initiate the crop yield prediction process.
Apps Used
Workflow JSON
{
"id": "9cd53b3b-71f3-4180-bf8c-7b41c28d6385",
"name": "Automate Crop Yield Prediction with n8n and LangChain",
"nodes": 0,
"category": "Agriculture Automation",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 9cd53b3b-71f3...
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