Smart Home Energy Saver Workflow
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Automates energy saving strategies for smart homes.
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About This Workflow
This workflow leverages n8n's Langchain integration to create an intelligent system for optimizing energy consumption in smart homes. It processes incoming data, generates embeddings, stores them in a vector database, and uses a language model agent to formulate and log energy-saving recommendations.
Key Features
- Webhook Integration: Receives energy data or commands via a webhook.
- Text Splitting: Prepares incoming data for embedding by splitting it into manageable chunks.
- Embedding Generation: Uses Cohere embeddings to create vector representations of the data.
- Vector Database Storage: Stores embeddings in a Supabase vector store for efficient retrieval.
- Knowledge Retrieval: Queries the vector store to find relevant information.
- AI Agent: Utilizes a Hugging Face language model to process information and generate responses.
- Memory Management: Maintains conversation history for context-aware interactions.
- Logging: Appends execution logs and actions to a Google Sheet for record-keeping.
How To Use
- Webhook Trigger: Configure the
Webhooknode to receive incoming data. This could be sensor readings, user commands, or scheduled events. - Data Processing: The
Splitternode breaks down the incoming text data into smaller pieces. TheEmbeddingsnode then generates vector embeddings for these chunks using Cohere. TheInsertnode stores these embeddings in the Supabase vector store, indexed bysmart_home_energy_saver. - Querying and Interaction: The
Querynode retrieves relevant information from the Supabase vector store based on embeddings. This information is passed to theToolnode, which acts as a tool for the AI agent. - AI Agent Logic: The
Memorynode maintains conversation history. TheChatnode (Hugging Face LM) andAgentnode work together. TheAgentnode, configured withpromptType: define, uses the provided tools and memory to process queries and generate actions or responses. Thetextparameter={{ $json }}in the Agent node should be dynamically populated with the user's query or relevant context. - Logging: The
Sheetnode appends the results or actions taken by the agent to a specified Google Sheet for logging purposes.
Apps Used
Workflow JSON
{
"id": "6be07c4b-509e-4b31-8794-5491ff830a5b",
"name": "Smart Home Energy Saver Workflow",
"nodes": 0,
"category": "Smart Home",
"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: 6be07c4b-509e...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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