What the Data for This Category Looks Like
Data sources for metallurgical coal financial reports include publicly filed periodic reports of domestic and overseas listed metallurgical coal producers, monthly supply and demand data released by industry associations, and spot transaction quotes for metallurgical coal at major ports. Update frequencies are split into three intervals: quarterly (corporate financial reports), monthly (industry supply and demand reports), and weekly (port inventory and prices). Each financial report document includes fields such as metallurgical coal business revenue proportion, production capacity and output, coal tonnage production cost, average sales price, and inventory turnover. Most field units are ten thousand tons, yuan per ton, yuan per gigajoule, and similar units. Some documents include multi-page tabular data.
What Constraints Do These Characteristics Impose on Model Onboarding and Configuration
The multi-time-granularity update rhythm of metallurgical coal financial reports requires the model onboarding link to support dynamic adjustment of context window length, to adapt to the long text of quarterly financial reports and the short-frequency updates of weekly data. Professional field units and segmented category attributes require configuring classification thresholds for entity extraction, to distinguish metallurgical coal business data from other coal categories. The access requirement for multi-source heterogeneous data requires configuring data source priority and conflict resolution rules, to avoid confusion of metallurgical coal price data from different sources. Additionally, the high proportion of tabular data in financial reports requires adjusting document parsing table extraction parameters to ensure complete structured data extraction.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The text length of a single quarterly metallurgical coal financial report mostly falls within the 6000-10000 character range, adapting to the long-context financial report analysis requirement |
PARSE_TABLE_ENABLE | Enabled | Metallurgical coal financial reports include structured tabular data such as production capacity, cost, and price. Enabling this parameter extracts structured fields for precise analysis |
SIMILARITY_THRESHOLD | 0.75–0.85 | Terminology related to metallurgical coal has high similarity with terminology for other coal categories. Raising the threshold avoids accidental recall of non-metallurgical coal business data |
RECALL_TOP_N | Top 8–12 entries | Core fields of metallurgical coal financial reports are scattered across multiple reports of different cycles. Recalling an appropriate amount of data ensures comprehensive analysis |
DATA_SOURCE_PRIORITY | Corporate financial reports > Industry association data > Port quotes | Metallurgical coal business data from corporate financial reports is the most accurate. Prioritizing access ensures accuracy of analysis results |
PARSE_TIMEOUT_SECONDS | 300 seconds | Metallurgical coal financial reports include multiple tables and long text passages. Extending the timeout period ensures complete parsing |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Model onboarding returns a
connection refusederror. Cause: Incorrect configuration of model onboarding port and intranet mapping rules. Using a generic model deployment address directly for dedicated requests for metallurgical coal financial report analysis causes port conflicts and fails to establish a connection. - Phenomenon: Generated financial report analysis results lack the metallurgical coal tonnage production cost field. Cause: The
PARSE_TABLE_ENABLEparameter is not enabled, and cost tabular data in the financial report is not extracted, resulting in missing core fields. - Phenomenon: External vector model experiences operational lag. Cause: The
maxContextparameter is not adjusted based on the text length of metallurgical coal financial reports. Using an excessively small context window causes the model to repeatedly load redundant data, increasing hardware load.
How to Confirm Successful Configuration
- Upload a publicly available quarterly financial report of a listed metallurgical coal enterprise, and check whether the parsed structured fields include core content such as metallurgical coal production capacity and average sales price.
- Initiate a financial report analysis request, and check whether the returned results only include metallurgical coal-related business data, with no confusing content from other coal categories.
- Check model onboarding logs to confirm that all requests complete responses within the configured timeout period, with no frequent timeout errors.
- Test multi-source data access to confirm that enterprise financial report data is prioritized for generating analysis results, in line with configured priority rules.
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.