What this category of data looks like
Optical module yield rate and market data is sourced from public trading data for the optical module sector on securities markets, industry supply chain monitoring data, and public manufacturer shipment information. Data updates are synchronized daily after market close, with a full refresh completed the following morning. Data documents are output in structured JSON or CSV formats. Core fields include security code, security name, daily trading price, daily price change rate, manufacturer model shipment volume, unit production cost, and business gross margin. Trading price is measured in CNY per share, shipment volume in ten thousand units, unit production cost in CNY per unit, and price change rate and gross margin in percentage units.
What constraints these characteristics impose on model access and configuration
The multi-source heterogeneous nature, T+1 update schedule, and segmented field characteristics of optical module data create multiple constraints for model access configuration. The daily post-market update requirement means configuring a daily scheduled pull task instead of real-time calling interfaces, to reduce invalid requests and call costs. Multi-source fields cover different data types such as securities trading, supply chain shipments, and production costs, so standardized field mapping rules must be configured to align field names and formats across sources. The presence of percentage-based fields requires clear statistical scope and unit conversion logic in the model prompt, to avoid the model mixing up proportional calculation scenarios across different use cases. The optional nature of niche manufacturer data requires configuring empty value skipping or default filling rules, to prevent the model from generating unsubstantiated inference results. The need for cross-source data fusion also requires additional configuration of a data consistency check step, to ensure accurate and unified information input to the model.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
dataset_sync_cron | 0 10 * * * | Matches the T+1 update schedule of optical module data, completes synchronization of the previous day’s data at 10 AM daily |
field_align_rule | Map the external data source’s "daily closing price" to close_price, "daily price change rate" to price_change, and "manufacturer shipment volume" to shipment_qty | Aligns standardized naming for multi-source heterogeneous fields, ensuring the model can uniformly recognize all types of data |
percentage_unit_spec | Clearly mark the statistical scope of trading price change rate and business gross margin, uniformly use percentage as the output unit | Avoids the model mixing up proportional calculation logic across different scenarios |
empty_field_policy | skip_empty_field | Handles missing shipment data for niche manufacturers, prevents the model from generating unsubstantiated inference results |
model_api_timeout | 300 seconds | Adapts to the response duration of cross-source data pulling and fusion, avoids premature request timeouts |
max_model_input_tokens | 8000–12000 characters | Adapts to the long context requirement of single-batch input containing multiple security and manufacturer information |
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 targeted analysis. It is recommended to conduct tests on self-hosted samples before finalizing settings.
Three common configuration errors
- Symptom: The model call returns a
403 Forbiddenerror, indicating the token does not have permission to use the specified model. Cause: The permission scope of the current application is not correctly bound in the model access configuration, or the configured model permissions do not cover the models related to optical module market data. - Symptom: The yield rate broadcast generated by the model does not follow the preset word count limit, and the output content exceeds the expected length. Cause: The trigger conditions for word count constraints are not clearly specified in the system prompt, or the configured model output token upper limit parameter value is too large, failing to match the word count limit requirements.
- Symptom: Shipment data for some optical module manufacturers is not included in the model’s calculations, resulting in missing generated results. Cause: No empty value processing rules are configured, causing data entries containing empty values to be incorrectly included in the model input, or the field mapping rules are configured incorrectly, causing the shipment volume field to not be correctly identified.
How to confirm the configuration is complete
- Manually trigger a dataset synchronization task, check whether the synchronization log shows that all configured data source fields have been correctly mapped, with no field alignment failure prompts.
- Call the model test interface, input simulated optical module market data, verify whether the percentage-based data output by the model conforms to the preset unit and scope specifications.
- Review the model call logs, confirm that each call’s timeout duration matches the configured parameter value, with no request timeout errors.
- Generate a test data set containing empty values, verify whether the model processes the data according to the preset rules, with no abnormal inference results.
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.