HTTP Interfaces and External Systems for the Model Allocation Unified AI Platform

Model allocation data is sourced from the platform’s connected large model metadata, user-created application configurations, and real-time call

What the Model Allocation Data Looks Like

Model allocation data is sourced from the platform’s connected large model metadata, user-created application configurations, and real-time call traffic statistics. Update schedule: Full updates are triggered when new models are added or application allocation rules are adjusted; real-time traffic data refreshes every several seconds. The data format is structured JSON, including fields such as model_id (string, unique model identifier), weight (integer, representing call weight), call_limit (integer, per-minute call limit), app_ids (array, associated bound application IDs), and others. Field units: weight has no unit, call_limit uses calls per minute as the unit, and model_id is a string identifier.

Constraints on HTTP Interfaces and External Systems

Because the data includes real-time call traffic and application binding rules, HTTP interfaces must support matching model allocation rules by application ID to prevent model misuse across business scenarios. Legitimacy validation for model_id requires the interface to verify that the incoming model identifier exists in the platform’s connected model list when receiving a request; otherwise, a parameter error is triggered. The high-frequency refresh characteristic of real-time traffic data requires the interface to support streaming returns or short-interval pulls to synchronize the latest call load status. External systems must carry the app_ids associated parameter when connecting to ensure model allocation matches specific business scenarios, and must adapt to call timeout differences across models to avoid request interruptions caused by excessive model loading or inference latency.

How to Configure Settings

Configuration ItemRecommended ApproachRationale
default_model_idPlatform built-in general model IDEnsures a verified stable model is used for default calls, preventing errors from no matching models
model_call_weight10-100, layered by model performanceModels with higher weights receive more call requests, adapting to computing power costs and performance characteristics of different models
app_model_bind_modeConfigured per individual applicationDifferent business scenarios for different applications require matching different model combinations, preventing misuse of models across applications
stream_response_interval500-2000 millisecondsBalances front-end display smoothness and interface call frequency, adapting to actual front-end rendering requirements
request_timeout30-60 secondsCovers the normal response duration of large model calls, preventing request failures caused by excessive model loading or inference latency

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and testing on independent samples prior to finalization is recommended.

Three Common Misconfigurations

  • Phenomenon: Fixed 4-second return interval when calling streaming interfaces, with no option for custom adjustment. Cause: The stream_response_interval parameter was not configured, and the platform’s default interval value was used.
  • Phenomenon: do_request_failed error returned when calling the interface. Cause: The incoming model_id is not in the platform’s connected model list, or call permissions for that model were not configured.
  • Phenomenon: Unexpected model allocation results when calling across applications. Cause: Models were not bound by application dimension, and global default configurations were used, leading to model misuse.

How to Confirm Proper Configuration

  • Call the model allocation query interface, pass a test application ID, and verify that the returned model_id matches the bound model configured.
  • Initiate a streaming call request, check the return data interval via the front-end console, and adjust stream_response_interval until it meets business requirements.
  • Simulate high-concurrency calls, and verify that the error types returned by the interface match the preset current-limiting rules.
  • Pass a non-existent model_id to initiate a call, and confirm that the interface returns a clear parameter error prompt.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.