Model Access and Configuration for Multi-Holding Yield Data

Data sources are daily financial accounting systems and third-party market data interfaces from affiliated business segments and invested entities.

What this type of data looks like

Data sources are daily financial accounting systems and third-party market data interfaces from affiliated business segments and invested entities. Full synchronization is completed within 1 hour after market close each trading day. The document structure uses business segments as the primary grouping. Each group contains seven fields: business entity name, asset category, initial position market value, ending position market value, interval yield, position proportion, and transaction flow identifier. For field units: market value values use ten thousand yuan as the unit, yield values use percentage as the unit, and time fields use ISO 8601 format timestamps.

What constraints do these characteristics impose on the model access and configuration process

Because data is grouped by business segment and includes a large number of fields, model access must support multi-field mapping configuration to prevent data parsing failures caused by inconsistent field naming across subsidiaries. The fixed daily update schedule requires that scheduled task trigger times align with the data source synchronization completion node, to avoid calling datasets that have not finished updating. Batch data from multiple entities will consume more context space, so the model input segmentation threshold must be adjusted to prevent truncation or timeout errors caused by excessive content. Additionally, the special format of the yield field requires custom parsing rules to be configured, ensuring the model can recognize percentage-based numerical calculation logic.

How to set the configurations

Configuration ItemRecommended ValueRationale
maxContext8000–12000 charactersAdapts to batch input of multi-segment data, prevents model truncation caused by excessive content
batchInvokeSize5 per batchMatches single-batch data volume, balances call rate and interface load
fieldMappingModeMap by standard field namesUnifies differing field naming across subsidiaries, simplifies parsing logic
triggerScheduleTrigger daily at 17:30Aligns with the synchronization completion time of most data sources, ensures access to the latest data
parseTimeout300 secondsAdapts to time requirements for batch data parsing, prevents mid-process timeout interruptions
customParseRulesEnable automatic percentage field conversionAdapts to the special format of yield fields, ensures the model can directly calculate aggregated values

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.

Three common configuration errors

  • Phenomenon: When calling a third-party large language model, the response returns {"object":"error","message":"Only allowed now"}. Cause: The third-party model whitelist allowed by the platform has not been configured, and only direct calls to officially preset models are supported.
  • Phenomenon: When adding a locally deployed vLLM service via OneAPI, the prompt connection refused is displayed. Cause: The external port of the vLLM service has not been opened, or network policies restrict cross-node access permissions.
  • Phenomenon: Empty result fields appear when batch parsing multi-segment data. Cause: No field mapping rules have been configured, and custom fields from some subsidiaries are not correctly identified, resulting in lost data after parsing.

How to confirm the configuration is complete

  • Manually trigger a scheduled task, check whether there is a data synchronization success identifier in the task log, and verify that the number of returned fields matches expectations.
  • Call the configured model interface, pass in test segment data, and check whether the returned results include correct yield calculation and aggregation logic.
  • Verify the connection status of the third-party model, check whether the platform's model management page shows normal connection with no error prompts.
  • Adjust the volume of test data, check whether the batch call processing logic works normally, with no timeout or truncation phenomena.

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.