What the data for this category looks like
Heating category financial report data mainly comes from three sources: public annual reports of listed public utility companies, publicly disclosed data from local heating regulatory platforms, and monthly operation reports from regional energy operating entities. Update schedules follow these rules: quarterly financial reports are updated every 3 months. Annual financial reports are released before March of the following year. Monthly operation data is updated before the 10th day of the following month. Document structure includes core fields: heating supply revenue, operation and maintenance costs, total heating area, number of heating users, and fuel consumption proportion. Their respective units are ten thousand yuan, ten thousand yuan, ten thousand square meters, ten thousand households, and percentage.
What constraints these characteristics impose on tool calling and plugin workflows
Multi-time granularity data requires that the query period must be clearly specified during tool calling, to avoid confusion between monthly operation data and quarterly financial reports. Multi-source data sources bring format differences. Field mapping and unit unification must be completed via plugins, otherwise unit mismatches will appear in AI output. High-frequency updated monthly data requires the tool to be configured with scheduled pull trigger rules, to ensure that the latest publicly disclosed data is called. Some non-standard fields such as fuel consumption proportion must be normalized before tool calling, otherwise they cannot be directly used for quantitative comparison in financial report analysis.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
plugin_data_source | ["public_annual_report", "monthly_heating_data"] | Covers the core data source types for heating category financial reports, ensuring data compliance |
tool_query_time_range | ["last_quarter", "last_12_months"] | Matches the regular update schedule of heating financial reports (quarterly and annual), to avoid calling expired or irrelevant data |
field_unit_conversion | Calibrated based on actual measurements | Heating data has multi-source unit differences, so custom unit conversion rules must be configured for different data sources |
plugin_exec_timeout | 240 seconds | Pulling and format verification of multi-source data takes a long time, to avoid call timeout interruptions |
rag_retrieve_top_k | Top 8 entries | Heating financial reports have few core fields, recalling too many entries will increase AI processing load |
image_output_switch | false | Heating financial report analysis focuses on structured data, no image output is required |
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, and it is recommended to conduct tests on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Images returned after tool calling fail to display normally, and the interface shows loading failure. Cause:
image_output_switchis not configured astrue, and correct image MIME type declarations are not added in the tool response. - Phenomenon: Fields returned by tool calling are empty, or numerical units do not match expectations. Cause:
field_unit_conversionrules are not configured, and unit differences across multi-source data are not unified. - Phenomenon: Tool calling frequently triggers timeout errors, and the log shows status code
504 Gateway Timeout. Cause:plugin_exec_timeoutis not set to a sufficiently long duration, and multi-source data pulling time exceeds the default limit.
How to Confirm Proper Configuration
- Execute a tool calling test, specify the query period as
last_quarter, and verify that the time range of the returned data matches the configuration. - Check the field units in the tool response, confirm that they match the preset
field_unit_conversionrules, with no unit confusion issues. - Review the tool calling logs, confirm that no timeout errors related to
plugin_exec_timeoutor abnormal status codes appear. - Test triggering the scheduled pull task, confirm that the data update frequency matches the cycle specified in
tool_query_time_range.
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.