Building a GPT Backed by a Heroku-Deployed API

Summary

The walkthrough shows how to connect an OpenAI custom GPT to a Node.js backend that provides natural-language access to an employee directory. It builds an Express API with a POST /search endpoint, where OpenAI’s Chat Completions API converts a user’s question into a single SQL query, the database returns rows, and a second OpenAI call turns those rows into a natural-language response. The project uses Heroku Postgres, environment configuration, bearer-key authentication, Swagger UI, and an OpenAPI specification imported into the GPT action. The deployment flow covers seeding dummy records, deploying from a GitHub-connected repository, and configuring the action to call the hosted API, while noting that data leaves Heroku’s trust boundary for OpenAI services.

Takeaways

1. 1

   The /search flow uses one OpenAI call to generate SQL from the user’s question and another to convert database rows into a natural-language answer.

2. 2

   The GPT action uses Bearer API-key authentication and imports the API contract from the Heroku app’s /api-docs/openapi.yaml route.

3. 3

   The architecture sends data beyond Heroku’s trust boundary to OpenAI services, which matters for data-sensitive applications.