What this type of data looks like
Water utility financing daily report data comes from three main sources: local public resource trading platforms, official announcements of water project financing entities, and industry information aggregation channels. Updates run daily, covering newly released water sector financing transaction and intended project information. Each document centers on a structured table, with short project background explanations added as supplementary content. Standard fields include project ID, project name, affiliated water utility sub-sector, financing amount (unit: ten thousand yuan), financing method, signing date, fund provider, and financing entity. Some entries include project location and fund purpose descriptions.
What constraints do these characteristics impose on model access and configuration
The structured characteristics and high-frequency update nature of water utility financing daily reports create multiple constraints for model access and configuration. First, extraction of multiple standardized fields requires precise thresholds for field mapping. This prevents non-core fields from interfering with extraction of key financing information. Second, financing amounts are uniformly measured in ten thousand yuan. Unit verification rules must be configured to stop the model from mistakenly using other units in calculations. Third, daily batch data updates require adjusting the maxContext parameter to fit the text length of a single daily report. Batch call timeouts must also be configured to match the high-frequency data processing rhythm. Fourth, some entries include secondary information such as location and purpose. Field recall priority must be configured to ensure core financing fields are extracted first.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | The text length of a single water utility financing daily report typically falls between 500 and 1000 characters. Reserving sufficient context space avoids information truncation |
llmModels | qwen2.5-14b-int4 or same-scale lightweight open-source models | Structured extraction tasks require model inference accuracy. Lightweight models can adapt to the response speed needed for batch processing |
PARSE_FILE_TIMEOUT_SECONDS | 60 seconds | The parsing and extraction process for a single daily report takes relatively little time. Setting a reasonable timeout prevents blocking of batch tasks |
| Similarity Threshold | 0.75–0.85 | Structured field matching for financing daily reports must balance accuracy and recall. This avoids mismatching non-water utility financing projects |
| Number of Recalled Entries | Top 3 | Core information of water utility financing daily reports is concentrated in the first 3 valid entries. Excessive recalled entries will introduce redundant data |
| Field Extraction Priority | Sorted by financing amount > financing method > signing date | Core analysis dimensions for water utility financing daily reports are financing scale and method. Prioritizing their extraction ensures analysis accuracy |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Each situation requires specific analysis, and it is recommended to test on your own samples before finalizing the configuration.
Three Common Configuration Mistakes
- Symptom: A model loading failure prompt appears, with a
model not founderror code in logs. Cause: The model name in thellmModelsparameter is not configured correctly, third-party call keys are invalid, or deployment version and model compatibility configuration do not match. - Symptom: An
ETIMEDOUTtimeout error occurs during batch daily report processing. Cause:PARSE_FILE_TIMEOUT_SECONDSis set too short, failing to adapt to the overall time required for batch parsing. - Symptom: Knowledge base answers are truncated, only returning part of the financing fields. Cause: The
maxContextsetting value is smaller than the text length of a single daily report, preventing the model from fully extracting all field information.
How to Verify Successful Configuration
- Access the model management page, confirm that the
llmModelsparameter matches the actual deployed model name, and verify that the call key configuration is valid. - Upload a single water utility financing daily report, check whether the extraction result includes all core fields, and confirm that the field extraction priority matches the preset configuration.
- Initiate a batch parsing task, check that the task execution log has no timeout errors, and confirm that
PARSE_FILE_TIMEOUT_SECONDSadapts to the current processing rhythm. - Test daily report texts of different lengths, confirm that the extraction result has no truncation, and verify that the
maxContextsetting adapts to the current text length requirements.
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.