What the Data for This Category Looks Like
Market and yield data for the optoelectronics category comes primarily from public market APIs of domestic stock exchanges and official data sources from industry index compiling organizations. Data updates follow two schedules: intraday order book data is pushed in real time during trading hours. Daily closing yield and market report data is fully updated within one hour after each trading day closes. Each data entry uses structured JSON format, with fields including asset code, asset name, daily opening price, closing price, highest price, lowest price, trading volume, trading amount, daily relative benchmark change value, and more. Price fields use renminbi yuan as the unit. Trading volume uses shares as the unit. Trading amount uses renminbi yuan as the unit. The change value field is a dimensionless relative change value.
Constraints Imposed on Dialogue Logs and Auditing
Optoelectronics category market data has strong real-time performance, multiple field dimensions, and high update frequency. These characteristics impose multiple constraints on the dialogue logs and auditing link. First, intraday order book data has a large volume. It is necessary to limit the retention length of the conversation context window to avoid overloading log storage. Second, multi-field structured data requires audit logs to accurately bind field names and query intent. This avoids confusion between generic yield values and category-specific change values. Third, daily closing report data generated per trading day requires logs to be archived by trading period and date. This enables cross-cycle audit traceability. Finally, financial scenarios require retaining request and response timestamps accurate to the millisecond. This ensures chronological traceability.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | The optoelectronics market data has many fields. This range balances context retention completeness and log storage efficiency |
LOG_STORAGE_PARTITION | Partition by trading day | Daily closing market reports are generated per trading day. Partitioning by date simplifies cross-cycle audit queries |
USER_ID_LOG_SEPARATE | Enabled | The business system has multiple user scenarios. Isolating logs by user ID allows quick location of a single user's asset query records |
LOG_TIMESTAMP_PRECISION | Millisecond level | The timestamp precision of intraday order book data is millisecond. Matching log time precision ensures audit chronological traceability |
PARSE_FIELD_WHITELIST | Optoelectronics-specific market field set | Filter non-category-related fields to avoid mixing irrelevant data in audit logs and improve audit accuracy |
REGEX_MATCH_TIMEOUT | 3 seconds | The category-specific field regular expression has moderate complexity. This duration avoids regex matching timeout errors |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: In multi-user scenarios, it is impossible to distinguish chat records of different users. Logs only show generic session IDs. Cause: The
USER_ID_LOG_SEPARATEconfiguration is not enabled, and the business system user ID is not bound to session logs. - Symptom: An error is triggered when calling regex matching to extract chat records, prompting field matching failure. Cause: The
PARSE_FIELD_WHITELISTconfiguration is not set. Logs contain fields from non-optoelectronics categories, causing the regex matching scope to be too large and triggering an exception. - Symptom: Calls actually occur, but no records appear in FastGPT call logs. Cause: The correct partition rule for
LOG_STORAGE_PARTITIONis not set. When logs are partitioned by non-trading days, cross-date queries cannot locate logs generated on the current day.
How to Verify Successful Configuration
- Access the FastGPT backend log management page, select the partition for the corresponding trading day, and check whether session logs classified by user ID exist.
- Trigger an optoelectronics market query, and check whether the logs contain millisecond-level request and response timestamps.
- Submit a regex matching test that includes category-specific fields, confirm no matching errors, and verify only target field content is extracted.
- Switch between different business user IDs, verify that logs only show session records for the corresponding user, and confirm no cross-user log confusion occurs.
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.