AI-Powered Rental Price Estimator
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This n8n workflow creates an AI-powered rental price estimator that intelligently analyzes property data to provide accurate valuations. It leverages a vector database for dynamic knowledge management and an LLM agent for conversational estimates, logging all interactions for review.
About This Workflow
This n8n workflow, "Rental Price Estimator," offers a powerful solution for dynamically estimating rental prices and managing property data. It starts with a webhook endpoint that can both ingest new property information (splitting and embedding it into a Supabase vector store) and receive user queries for estimates. An intelligent AI agent, powered by Anthropic's LLM, leverages this Supabase knowledge base and maintains conversational context to deliver accurate and relevant rental price predictions. All interactions, including the agent's responses, are meticulously logged to a designated Google Sheet, providing a valuable audit trail and data for future analysis.
Key Features
- AI-Driven Estimations: Utilize an Anthropic LLM and a Langchain agent to provide intelligent, context-aware rental price estimates.
- Dynamic Knowledge Base: Store and retrieve property data efficiently using Supabase as a vector database, enabling continuous learning and updated insights.
- Conversational Context: The integrated memory buffer allows the AI to maintain context throughout a conversation, leading to more natural and accurate interactions.
- Automated Data Ingestion: Easily feed new property information into your knowledge base via a simple webhook endpoint.
- Comprehensive Logging: Automatically log all estimation requests and agent responses to Google Sheets for auditing, analysis, and record-keeping.
How To Use
- Webhook Endpoint: Configure the 'Webhook' node with a POST method and a specific path (e.g.,
/rental_price_estimator). This URL will serve as the entry point for both providing new data for your knowledge base and submitting queries for rental price estimations. - AI Service Credentials: Ensure your API keys for Cohere (used by the 'Embeddings' node) and Anthropic (for the 'Chat' LLM) are securely set up in their respective credential fields.
- Supabase Vector Store: Connect your Supabase account via credentials. Crucially, specify
rental_price_estimatoras theindexNamein both the 'Insert' and 'Query' Supabase nodes. This is where your property data will be stored and retrieved. - Google Sheets Logger: Provide your Google Sheets credentials and configure the 'Sheet' node. Specify the
documentId(your Google Sheet ID) andsheetName(e.g., 'Log') where every agent interaction and its output will be recorded. - Populate & Query: You can send raw property data to the webhook to populate your Supabase knowledge base. Alternatively, send a query (e.g., "What's the estimated rental price for a 2-bed apartment in downtown with 900 sq ft?") to receive an AI-powered estimate, which will also be logged.
Apps Used
Workflow JSON
{
"id": "3f203abb-f36f-48a8-b9aa-64f089c41325",
"name": "AI-Powered Rental Price Estimator",
"nodes": 22,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 3f203abb-f36f...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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