What Data for This Category Looks Like
Data for IT services financing daily reports is aggregated from the National SME Share Transfer System, local equity trading centers, vertical industry information databases, and enterprise self-reporting channels. The update cadence is daily T+1, syncing new financing records from the previous day. Data is stored as structured tables. Core fields include financing entity name, financing amount (unit: RMB ten thousand yuan), financing round, disclosure date, investor type, and announcement link. All fields use basic text or numeric types, with no nested complex structures.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The daily T+1 update cadence requires fixed scheduled trigger frequencies for tool calls. This avoids compute resource waste from full historical data pulls. Differences in field naming across multiple data sources require unified field mapping rules in tool configurations. This adapts to field format differences across platforms. Inconsistent financing amount units require unit conversion logic during tool calls. This ensures all data is unified to the RMB ten thousand yuan format. The structured document format requires parsed results returned by tools to conform to a preset JSON Schema. This facilitates subsequent report generation and data analysis.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
plugin_cron_expression | 0 1 * * * | Adapts to the T+1 update cadence of the financing daily report, triggers the pull task at 1 AM daily |
tool_response_schema | {"type": "array", "items": {"type": "object", "properties": {"企业名称": {"type": "string"}, "融资金额": {"type": "number"}, "融资轮次": {"type": "string"}, "发布日期": {"type": "string"}}}} | Unifies the return format of structured data, adapts to subsequent report generation requirements |
unit_conversion_enabled | Enabled | Adapts to differences in financing amount units across data sources, unifies conversion to RMB ten thousand yuan |
incremental_sync_enabled | Enabled | Only pulls new daily financing records, avoids compute resource usage from full data pulls |
tool_call_timeout | 300 seconds | Structured data parsing takes relatively little time. Mark abnormal records and terminate tasks if timeout occurs |
error_alert_threshold | ≥3 abnormal records | A small number of abnormalities may be temporary data source fluctuations. Batch abnormalities should trigger operational alerts |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When using a database connection plugin to connect to PostgreSQL, the interface displays "Workflow verification failed. Please check for missing or empty values, and whether connections are normal". Cause: Failed to fill in a compliant database connection string in plugin configuration, or failed to configure an allowed IP whitelist, leading to failed connection verification.
- Phenomenon: In FastGPT 4.8.23, some models cannot trigger preset tool calling flows. Cause: Failed to configure a correct tool calling prompt template, leading to the large language model failing to recognize the trigger format, or the context window being insufficient to carry prompt content related to tool calling.
- Phenomenon: Reply generation takes more than 10 seconds after calling the financing daily report plugin. Cause: Too frequent tool calling triggers rate limiting from third-party data sources, or an unreasonable tool calling timeout threshold is set, leading to excessive waiting time.
How to Confirm Proper Configuration
- Enter the FastGPT plugin management page, check the scheduling logs for the corresponding financing daily report plugin, and confirm that daily scheduled trigger tasks have execution records.
- Manually trigger a plugin call, and check if the returned structured data conforms to the preset response Schema, with no missing core fields and correct formats.
- Simulate an abnormal test scenario, input test data with a missing financing amount, and confirm that the plugin marks abnormal records and triggers preset alert rules.
- Check the large language model call logs, confirm that the tool calling prompt is correctly recognized, and that the model triggers the tool calling flow and returns expected data source 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.