What the Data for This Category Looks Like
Data for this category comes from compliant public securities market APIs and industry-specific quote platforms. It is updated 1 to 2 hours after daily market close. Each entry is stored in a structured format, with one row per listed optoelectronics enterprise or industry segment for that day’s market performance. Fields include:
- 6-digit security code
- Chinese security name
- Daily closing price (unit: yuan per share)
- Daily price change (unit: yuan per share)
- Daily trading volume (unit: shares)
- Daily trading amount (unit: yuan)
- Industry classification code
- Data release date (format: YYYY-MM-DD)
Data is grouped and archived by security code and release date. The number of records in a single daily report varies based on coverage scope.
Constraints Imposed on Model Access and Configuration by These Characteristics
The fixed daily update schedule requires model invocation timing to align with the post-market update window. This prevents pulling outdated data that has not completed updating.
Multiple numerical fields with units require clear field mapping rules during access. This avoids calculation errors caused by unit confusion.
The variable number of records per document requires configuring batch processing parameters that adapt to variable-length inputs. This prevents data truncation or memory overflow.
Data sources relying on public APIs require configuring compliant authentication parameters. This ensures request legitimacy.
The fixed-format release date field requires adding format validation logic to the model’s context cleaning step. This filters invalid data entries.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
modelInvokeTimeout | 300 seconds | Batch processing requests for optoelectronics market data typically complete within 300 seconds, avoiding timeout interruptions |
batchProcessMaxRecords | 500 records | Adapts to the typical record count range of a single daily report, preventing data truncation or memory overflow |
fieldMappingRule | Precise matching by field name | Data field names are fixed, precise matching avoids unit confusion |
apiAuthType | API_KEY authentication | Most public market APIs use this authentication method, meeting compliance requirements |
contextCleanupFormat | YYYY-MM-DD | Matches the fixed format of the data release date, quickly filtering invalid entries |
maxContextTokens | 8192 tokens | Adapts to token consumption of structured market daily report data, aligning with context limits of most general large models |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and testing on relevant samples is recommended prior to final configuration.
Three Common Configuration Errors
- Phenomenon: Model invocation returns a
403 Forbiddenstatus code when calling the market data API, or the interface displays an "Authentication Failed" prompt. Cause: Authentication parameters and keys for the public market API are not configured correctly, resulting in the API rejecting the request. - Phenomenon: When multiple models with the same name are configured, calls always use the key from the first configured model. Cause: No independent identification parameter is configured for each model, so the system cannot distinguish between the keys of models with the same name.
- Phenomenon: Market data returned by workflow nodes includes non-current-day historical records. Cause: No format validation rule is configured, and no release date entries that do not match the
YYYY-MM-DDformat are filtered out.
How to Verify Successful Configuration
- Manually trigger a model invocation, and check whether the returned market data includes current-day optoelectronics category records, with field units matching the data source.
- Send an interface authentication test request, and confirm the returned status code is
200 OK. - Import a test data set with a record count exceeding the typical range, and confirm the system can normally process variable-length input data sets.
- Configure identification parameters for multiple models with the same name, switch between different identifiers to initiate calls, and confirm that the corresponding key parameters are used during invocation.
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.