What the data for this use case looks like
Publicly disclosed trading data from domestic stock exchanges and classified military electronics sub-sector market data from professional financial market service providers supply military electronics yield and market daily report data. Providers generate data daily after market close on trading days, with no updates on non-trading days. The data follows structured JSON or CSV formatting, with each record corresponding to the daily market information of a single military electronics security. Fields include trade_date (trading date, YYYY-MM-DD format), security_code (security code, 6-digit numeric format), security_name (security name, string), closing_price (closing price, unit: yuan), opening_price (opening price, unit: yuan), daily_turnover (daily turnover, unit: ten thousand yuan), industry_subclass (sub-sector identifier), and other fields.
Constraints imposed by data characteristics on model access and configuration
The structured nature of military electronics market data requires model access links to adapt to precise matching of structured fields, using structured field matching logic. The exclusive update schedule for trading days requires scheduled synchronization tasks to match trading hours for trigger frequency, to avoid triggering invalid synchronization on non-trading days. The filtering requirement for sub-sectors requires precise configuration of screening rules to retain only military electronics classified security data, excluding interference information from other industries. Fields include clear unit identifiers, requiring the data parsing link to retain unit information to avoid unit confusion during subsequent use.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
sync_cron_expression | 0 0 17 * * 1-5 | Military electronics market data generates 15:00 after market close on trading days, with a 2-hour processing window reserved to ensure access to the latest daily data |
structured_data_field_mapping | trade_date→Trading Date, security_code→Security Code, security_name→Security Name, closing_price→Closing Price, opening_price→Opening Price, daily_turnover→Daily Turnover, industry_subclass→Sub-sector | Matches data source fields with knowledge base field definitions to avoid field mismatch errors during parsing |
recall_top_k | Top 10 entries | The number of securities in the military electronics sub-sector is moderate; recalling 10 entries covers core market information and avoids redundant data interfering with model inference |
model_api_timeout | 600 seconds | Parsing structured data and model inference requires a certain amount of processing time, to avoid synchronization or generation tasks being interrupted by timeouts |
filter_condition | industry_subclass == "military electronics" | Retains only military electronics classified security data, excluding market information from other industries to ensure knowledge base content accurately matches the targeted use case |
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, and require specific analysis for specific issues. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: The response latency when calling the configured model interface far exceeds the latency of a direct curl call. Cause: The
model_batch_sizeparameter was not adjusted, resulting in an excessively large data volume processed per request, which increases model inference delay. - Symptom: Testing fails after adding a locally deployed model, with the interface displaying
Connection timed out. Cause: Network rules for the intranet environment or corresponding port permission settings were not configured, preventing FastGPT from accessing the local model interface. - Symptom: A database connection error
1045 Access deniedappears after deployment, even if the database process starts normally. Cause: The database access account and password were not correctly configured in FastGPT's environment variables, or intranet IP access to the database was not allowed.
How to Confirm Successful Configuration
- Manually trigger a data synchronization task, and check if the synchronization log displays prompts for
Data parsing successfulandMatched military electronics securitiesto confirm that field mapping and filtering rules are working. - Call the configured model interface, and check if the returned results include correct military electronics market fields to confirm that the model access configuration is correct.
- Check the scheduled task trigger records to confirm that synchronization tasks only trigger at the specified time on trading days, to confirm that the cron expression is configured correctly.
- View the knowledge base document list to confirm that only military electronics industry market data is included, to confirm that the filtering rules are working.
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.