Automate Semantic Vectorization of Medical Procedures for Enhanced Search
This workflow transforms medical procedures from the USS table into vector embeddings using Google Gemini, enabling efficient semantic search in a PostgreSQL database with pgVector.
The workflow automates the process of importing medical procedures from the USS table, preprocessing the text, and generating semantic vector embeddings using Google Gemini. These vectors are stored in a PostgreSQL database with the pgVector extension, facilitating advanced semantic search capabilities. This approach improves the precision of search results by focusing on the meaning of queries rather than exact keyword matches.
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How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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