Smart Home Energy Saver with n8n & LangChain-Style Components

Efficient energy management in a smart home can lower electricity costs, reduce wear on appliances, and cut your carbon footprint. This technical guide explains how to implement a Smart Home Energy Saver workflow in n8n using LangChain-style building blocks such as text splitting, embeddings, and a vector store, combined with a lightweight agent and Google Sheets logging.

The workflow accepts JSON payloads from sensors or APIs, transforms free-text fields into semantic embeddings for context-aware retrieval, and uses an agent to generate recommendations or log events based on historical data.

1. Solution Overview

This n8n workflow template is designed for users who want to automate smart home energy optimization using:

• n8n as the orchestration and automation platform
• LangChain-style components for text splitting, embeddings, and vector storage
• A lightweight agent powered by a language model
• Google Sheets for persistent logging and auditing

At a high level, the workflow:

1. Receives sensor or event data via an HTTP Webhook.
2. Splits long text fields into manageable chunks.
3. Generates embeddings for each chunk using Cohere (or another provider).
4. Stores embeddings and metadata in a Supabase vector store.
5. Retrieves relevant past events using semantic search.
6. Maintains short-term conversational memory for the agent.
7. Uses a language model to generate human-readable recommendations or actions.
8. Writes outputs to Google Sheets for analytics and auditability.

2. Architecture & Data Flow

2.1 High-level architecture

The template is composed of the following logical components:

• Input & ingestion
  • Webhook node – accepts POST requests from smart home sensors or gateways.
• Preprocessing & embedding
  • Splitter node – segments long text into chunks.
  • Embeddings node (Cohere) – converts chunks into high-dimensional vectors.
  • Insert node (Supabase vector store) – persists embeddings and associated metadata.
• Retrieval & reasoning
  • Query node – performs similarity search over the Supabase vector index.
  • Tool node – exposes retrieval results as a tool for the agent.
  • Memory node (Buffer) – stores recent interactions for conversational continuity.
  • Chat node (Hugging Face LM) + Agent – generates recommendations and reasoning.
• Logging & analytics
  • Google Sheets node – appends a row with the agent’s output and key metadata.

2.2 End-to-end data flow

1. Event ingestion A sensor, smart hub, or intermediate gateway sends a JSON payload to the n8n Webhook via HTTP POST. Typical fields include:
   • device_id
   • timestamp
   • power_watts
   • notes or other descriptive text
2. Text splitting The Splitter node processes long text fields (for example, logs, notes, or event descriptions) and divides them into overlapping chunks. In the template:
   • chunkSize = 400 characters
   • chunkOverlap = 40 characters

   This improves embedding quality by preserving local context and preventing oversized inputs for the embeddings model.

3. Embedding generation For each chunk, the Embeddings node calls the Cohere embeddings API. The result is a vector representation that captures semantic similarity between events. While the template is configured for Cohere, you can substitute providers such as OpenAI or Hugging Face as long as you keep the vector dimensions and API usage consistent with your Supabase configuration.
4. Vector storage The Insert node writes each embedding to a Supabase-backed vector store along with metadata such as:
   • device_id
   • timestamp
   • original_text or notes
   • Any additional contextual fields you pass through from the Webhook

   Supabase then supports fast similarity search so the workflow can retrieve past events similar to new anomalies or queries.

5. Contextual retrieval When the system needs to answer a question or explain an event (for example, “Why did the kitchen fridge spike?”), the Query node executes a semantic search over the Supabase index. The Tool node wraps this retrieval capability so the agent can call it as needed and reason over the returned documents.
6. Conversation memory The Memory (Buffer) node maintains a short history of recent messages or interactions. This enables the agent to:
   • Handle follow-up questions
   • Refer back to recent recommendations
   • Maintain context across a short multi-step conversation

   The memory window is intentionally small to control token usage and latency.

7. Agent reasoning & response The Chat node, configured with a Hugging Face language model, is used by the agent to produce natural language responses. The agent uses:
   • Current event data from the Webhook
   • Relevant historical context from the vector store
   • Short-term context from the Memory node

   The output can include recommended actions such as:

   • “Lower thermostat by 2°C between 14:00 and 16:00.”
   • “Schedule an appliance inspection for the kitchen fridge.”
8. Logging & audit trail Finally, the Google Sheets node appends a new row that records:
   • Key input fields (for example, device, timestamp, power level)
   • The agent’s recommendation or explanation
   • Optional additional metadata for analytics or compliance

   This provides an easily accessible audit trail and a dataset for further analysis in BI tools.

3. Input Specification: Webhook Payload

The following example illustrates a typical JSON payload that the Webhook node receives:

{  "device_id": "fridge_kitchen_01",  "timestamp": "2025-08-01T14:22:00Z",  "power_watts": 220,  "notes": "sudden spike while door opened"
}

In practice, you can extend this schema with additional fields such as room, circuit, or status codes, as long as the workflow nodes are configured to handle them.

4. Node-by-Node Breakdown

4.1 Webhook node

Purpose: Entry point for sensor or gateway events.

Key configuration aspects:

• HTTP Method: Typically POST for JSON payloads.
• Authentication:
  • Use a secure URL and, where possible, apply authentication or IP allowlists.
  • If devices cannot connect directly to the internet, route events through a secure gateway that buffers and forwards them to the Webhook.
• Response handling: You can return a simple acknowledgment (for example, HTTP 200 with a minimal JSON body) to confirm receipt to the sensor or gateway.

4.2 Splitter node

Purpose: Segment long text fields to improve embedding performance and retrieval quality.

Typical parameters:

• chunkSize: 300 to 500 characters is a practical range for log or notes text.
• chunkOverlap: 20 to 50 characters helps maintain context between adjacent chunks.

Behavior:

• Operates on one or more text fields, such as notes or aggregated log text.
• Outputs multiple items, one per chunk, each preserving the original metadata fields where needed.

Edge considerations:

• If the text is shorter than chunkSize, only a single chunk is produced.
• Very large payloads may generate many chunks, which increases embedding cost. Consider truncation or filtering upstream if necessary.

4.3 Embeddings node (Cohere)

Purpose: Convert text chunks into semantic embeddings.

Provider: Cohere (as configured in the template). Compatible alternatives include OpenAI and certain Hugging Face models, but those require corresponding credential and node configuration changes.

Configuration notes:

• Set the model name according to your Cohere plan and performance targets.
• Ensure that the embeddings dimensionality is compatible with your Supabase vector column definition.
• Test with representative payloads to verify that semantically similar events yield high similarity scores.

Cost and rate limits:

• Each chunk results in a separate embeddings request. High-frequency sensors or verbose logs can increase costs quickly.
• Use batching, throttling, or event sampling if you expect large bursts of events.

4.4 Supabase vector store – Insert node

Purpose: Persist embeddings and associated metadata for later semantic retrieval.

Stored fields typically include:

• Embedding vector
• device_id
• timestamp
• original_text or notes
• Any additional fields useful for filtering (for example, room, circuit, event type)

Configuration notes:

• Use Supabase credentials with the minimum required privileges to insert and query vectors.
• Define indexes that support both vector similarity search and common filters such as device ID or time range.
• Ensure that the vector column type and dimension match the embeddings model output.

4.5 Supabase vector store – Query node

Purpose: Retrieve relevant historical events via similarity search.

Usage pattern:

• Given a query embedding or query text, the node searches the vector index and returns the most similar documents.
• Used when the agent needs context, for example to answer “Why did the kitchen fridge spike?”

Key parameters:

• Number of results (top-k) to return.
• Optional filters, such as:
  • Specific device_id
  • Date or time range

Integration with the Tool node:

• The Tool node exposes the query capability to the agent so it can call the vector store when needed.
• The agent then receives the retrieved documents as part of its reasoning context.

4.6 Memory node (Buffer)

Purpose: Maintain a bounded history of recent interactions to support short conversational flows.

Typical configuration:

• Store the most recent 5 to 10 messages, or
• Limit to interactions from the last 24 hours of events, depending on your usage pattern.

Trade-offs:

• A larger memory window provides richer context but increases token usage and latency.
• A smaller window is cheaper and faster but may lose context across longer conversations.

4.7 Chat node (Hugging Face LM) & Agent

Purpose: Generate natural language recommendations, explanations, or action suggestions based on current and historical data.

Components:

• Chat node: Calls a Hugging Face-hosted language model.
• Agent: Orchestrates tool usage (for example, the vector store Tool) and memory to answer user or system queries.

Typical outputs:

• Actionable recommendations such as:
  • “Lower thermostat by 2°C between 2 and 4 pm to reduce peak load.”
  • “Schedule a maintenance check for the kitchen fridge due to repeated spikes.”
• Explanations referencing past events retrieved from Supabase.

Configuration considerations:

• Prompt design is important to ensure the agent focuses on energy optimization and safety.
• For production systems that control physical devices, consider adding a rule layer or human approval step before executing actions.

4.8 Google Sheets node

Purpose: Append a log entry for each agent interaction or recommendation.

Commonly logged fields:

• Timestamp of the event or recommendation
• Device identifier
• Power reading or other key metrics
• Agent summary or recommended action
• Any additional flags for severity or follow-up

Benefits:

• Provides a simple, accessible audit trail.
• Supports ad-hoc analysis in spreadsheets or export to BI tools.
• Acts as a fallback record if vector or model logs are unavailable.

5. Configuration Guidelines & Best Practices

5.1 Webhook security

• Use HTTPS for all inbound requests.
• Apply authentication or signed requests when supported by your devices or gateway.
• If direct device access is not possible, deploy a gateway that:
  • Receives raw sensor data on the local network.
  • Validates and batches events.
  • Forwards them securely to the n8n Webhook.

5.2 Splitter parameters

• Start with:
  • chunkSize: 300 to 500 characters
  • chunkOverlap: 20 to 50 characters
• Increase chunkSize if you see fragmented context in retrieval results.
• Adjust chunkOverlap to balance redundancy vs. cost.

5.3 Embeddings model selection

• Choose a model that fits your:
  • Budget and API rate limits
  • Desired semantic accuracy for your domain
• Validate performance by:
  • Indexing a small set of known similar and dissimilar events.
  • Running similarity queries and confirming that related events rank higher.

5.4 Supabase indexing & metadata

• Store at least:
  • device_id
  • timestamp
  • original_text or