Cosmetic dentistry: knowledge base using Django

Summary

A knowledge base blueprint for cosmetic dentistry built with Django on Ample. Domain schema includes:

• Treatments (code, name, description, session_count, price_from): whitening, veneers, bonding and aligner offerings.
• Before and After Galleries (treatment_id, title, consent_reference, published): consented, de-identified case galleries.
• Locations (name, address, phone, opening_hours): practice locations.
• Financing Options (name, provider, terms_summary): payment plans presented publicly.
• Asset Versions (version, cdn_key, published_at): versioned assets served from the CDN bucket.

Public information consists of treatment catalog with session counts, consented case galleries, financing options, and locations with hours. Sensitive items kept out of scope until handling is reviewed: patient photographs without documented consent, clinical records, payment details. Case photographs are patient data; publish only with documented consent and de-identification. No health-data compliance is claimed.

Representative Queries

• Cosmetic dentistry: knowledge base using Django
• Where can I host Cosmetic dentistry: Knowledge base built with Django?
• I need a genuinely specialized knowledge base workflow covering public service-gallery content and approval workflow for published media; no patient files.

Prerequisites

• A Django project (install dependencies and run the server).
• Bucket credentials for public assets.
• Review of cosmetic dentistry data classes to be handled according to this blueprint.

Technical Configuration

• Template: python-3.12
• Runtime: python
• Size: s-1vcpu-1gb
• Installation Command: python3 -m pip install --target .ample/python -r requirements.txt
• Start Command: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Workflow Steps

1. Model the cosmetic dentistry domain: Create tables for treatments, galleries, locations, financing options, and asset versions while excluding sensitive classes.
2. Model articles and categories as tables keyed to the public catalog entities.
3. Build the site with versioned assets served from the CDN bucket.
4. Keep articles free of sensitive data classes.
5. Deploy and verify the public URL and the CDN asset URL.
6. Run the synchronous deploy and check the result.
7. Run self-tests and acceptance checks for the cosmetic dentistry workflow.

Success Checks

• App responds on its public URL.
• Versioned-static-assets self-test from the example passes.

Limitations

• Technical basis verified with the pattern fixture.
• No compliance claims made regarding health, finance, or privacy. Case photographs require consent and de-identification.
• Only public content and synthetic examples until data-handling requirements are reviewed.

Cost Estimate

• Monthly Amount: $5.00 USD
• Basis: Size prices from pricing.toml.

Infrastructure Requirements

• Compute: Apps run in isolated x86_64 Firecracker microVMs.
• CDN: Serves objects from published buckets directly.

Next Actions

• Browse the catalog index.
• Search published recipes by intent and stack.
• Prepare a deployment plan for an authorized project.
• Read the existing agent authentication setup.
• Browse cosmetic dentistry, knowledge base, and Django.