This n8n workflow automates the process of estimating rental prices based on input data, utilizing AI models, embedding, and vector similarity search, then logs the results into Google Sheets. It is ideal for property managers or real estate platforms that want to streamline rental price analysis and record-keeping.
The workflow begins with a webhook trigger, which receives rental property data. The data is then split into manageable chunks using a text splitter node. These chunks are embedded into vector representations via the Cohere API for semantic understanding. The embeddings are stored in a Supabase vector database, enabling efficient similarity searches.
When a new rental property needs a price estimate, the workflow queries the vector database to find similar rental listings. The related data is processed through a language model (ChatGPT via Anthropic) to generate a rental price estimate or related insights based on the input and similar records.
The conversation context is maintained in memory, ensuring coherent interactions. Finally, the estimated rental price and other details are logged into a Google Sheet for record-keeping and further analysis.
This automation is useful for real estate website owners, property managers, or analytics teams seeking to automate rental price estimation and keep detailed logs effortlessly.
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