What the data for this category looks like
Heating financing daily report data primarily comes from public utility project filing information released by local housing and urban-rural development departments, official announcements from heating supply enterprises, and third-party industry news platforms. Data is updated daily, covering heating sector financing projects from the current day and the past three business days. Each daily report document includes a project list. Each project contains fields such as project name, heating supply type, financing amount, financing party and investor entities, signing date, project location, and designed heating supply scale. Financing amount is measured in ten thousand RMB. Designed heating supply scale is measured in megawatts. Date fields use the YYYY-MM-DD format uniformly.
Constraints imposed by these characteristics on model access and configuration
The high-frequency update attribute of heating financing daily reports requires model call frequency to align with the daily report generation cycle, to avoid delays or repeated triggers. The structured multi-field project data requires the model to support precise multi-field extraction and unit verification, to prevent confusion between financing amount and heating supply scale units. The standardized requirement for administrative division fields requires configuring administrative division matching rules for the model, to ensure classification accuracy. The medium-length batch project data in each document requires the model's context window to adapt to medium text volumes, to avoid information truncation. Additionally, exclusive terminology in the heating sector requires the model to have basic domain recognition capabilities, to reduce entity extraction bias.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Adapts to the 5000-12000 character text length of a single heating financing daily report, avoiding truncation of batch project data |
MODEL_API_TIMEOUT | 60 seconds | Meets the processing duration required for multi-field structured extraction, avoiding task failure caused by network latency |
TEXT_SPLITTER_CHUNK_SIZE | 1000–1500 characters | Splits daily report text into reasonable segments, retains project context while adapting to the model's single-processing limit |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters non-heating sector financing projects, improving the accuracy of entity extraction |
FIELD_MAPPING_RULE | Bind official administrative division codes to project location fields | Ensures extracted location information meets standardized requirements, facilitating subsequent classification and statistics |
TOOL_ENABLED | false | Structured extraction for heating financing daily reports does not require external tool calls, reducing call complexity and latency |
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
- Symptom: The model call returns an
invalid input dataerror or empty extraction result. Cause: Directly filling base64-encoded heating project announcement images into theimage_urlparameter without adapting to the input format requirements of the target model, causing the Qwen model deployed on Xinference to fail to parse. - Symptom: Model call fails, with the interface displaying
API key invalidorconnection refused. Cause: The proxy address and key of OneAPI are not configured correctly, or the request routing adaptation switch is not enabled, causing interface requests to fail to connect normally. - Symptom: Field extraction for heating financing daily reports has deviations, such as the heating supply scale unit being incorrectly converted to kilowatt-hours, when the correct unit is megawatts. Cause: Field mapping and unit verification rules are not configured, causing the general model to fail to recognize exclusive numerical units and field definitions in the heating sector.
How to confirm the configuration is complete
- Upload a single heating financing daily report sample, check the field completeness of the model's extraction results, and verify whether the units of values such as financing amount and heating supply scale meet the requirements.
- Configure a scheduled call task, observe the model call success rate over three consecutive business days, and confirm that the call frequency matches the daily report update rhythm.
- After importing the proxy address and key of OneAPI, initiate a test call, and check that the interface returns a status code of
200 OKwith no authentication errors. - Check the configuration of the text understanding model in the knowledge base, confirm that the structured parsing switch is enabled, and check whether the accuracy of field extraction meets the business preset verification standards after importing the sample.
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.