Generate Product LLMs.txt

{
  "status": "success",
  "products_processed": 5,
  "files_generated": 5,
  "files_deployed": 5,
  "products": ["Product A", "Product B", "Product C", "Product D", "Product E"]
}

1. Fetch business entity and AI site project_name
2. Fetch products (all or filtered by product_ids)
3. For each product:
   - Get source_urls from entities.product_source_urls
   - Generate /llms/{slug}.txt content using Gemini
4. Deploy all generated files to Vercel (single deployment)
5. Return summary

src/app/shared/products/generate_llms_txt.py

# Generate for all products
curl -X POST https://searchcompany-main.up.railway.app/api/cron/generate-product-llms-txt \
  -H "Content-Type: application/json" \
  -H "X-API-Key: search-company" \
  -d '{"business_id": "my-business-123"}'

# Generate for specific products
curl -X POST https://searchcompany-main.up.railway.app/api/cron/generate-product-llms-txt \
  -H "Content-Type: application/json" \
  -H "X-API-Key: search-company" \
  -d '{"business_id": "my-business-123", "product_ids": ["uuid-1", "uuid-2"]}'

src/app/apis/cron/generate_product_llms_txt/
├── __init__.py
└── routes.py