Quick question about a persistent 504 Gateway Timeout occurring during the model training phase for our custom predictive routing integration. We are deploying a Premium App via the AppFoundry platform that leverages the POST /api/v2/predictiverouting/models endpoint to initiate training cycles based on historical interaction data pulled from our external CRM. The integration uses a dedicated service principal with full administrative scope, so permissions are not the bottleneck here. The issue manifests specifically when the dataset exceeds 50,000 records, causing the Genesys Cloud platform to drop the connection before returning a job ID.
The environment is configured for the US East region, and we are using the latest version of the Python SDK (v2.14.3) for the initial trigger. Interestingly, manual testing via Postman with smaller datasets succeeds without issue, returning a 202 Accepted status immediately. However, when the automated job queue pushes larger batches through our middleware, the timeout occurs consistently at the 30-second mark. We have verified that the underlying API calls for data ingestion (/api/v2/predictiverouting/interactions) complete successfully and the data is visible in the Genesys Cloud analytics dashboard before the training trigger fires.
We suspect this might be related to the asynchronous processing limits imposed on AppFoundry-hosted applications or a specific rate-limiting behavior tied to the predictiverouting scope. The logs from our side show no errors, simply a hanging request until the gateway timeout. Has anyone encountered similar latency issues when triggering predictive model retraining via API at scale? We are looking to avoid falling back to manual UI triggers for our enterprise clients.
Any insights into whether there is a recommended pagination strategy for the training payload or a specific header we might be missing to optimize the handshake would be greatly appreciated. We are currently evaluating if moving the aggregation logic entirely server-side within Genesys Cloud via Data Actions might bypass this bottleneck, but we want to confirm if the API endpoint itself has a hard ceiling for initial request complexity.