What the data for this category looks like
Coking coal-related marketing content data is primarily sourced from public reports released by domestic coal industry associations, domestic major coastal port spot price systems, trading floor data from coking coal futures exchanges, and operation monitoring data from downstream coking plants and steel plants. Data update rhythms fall into three categories: spot prices are updated daily, futures data is updated in real time during trading days, and industry operation and supply and demand data is updated weekly or every ten days. Document structures are mostly structured tables or standardized industry reports, including core fields such as delivery grade, ash content, sulfur content, volatile matter, bonding index G, colloidal layer thickness Y, origin, quotation unit (yuan/ton), price change range, and update time. Some documents include historical price comparison data for the past 30 trading days.
What constraints do these characteristics impose on model integration and configuration
The multi-professional indicator attributes of coking coal data require retaining sufficient contextual association during model calls, to avoid separating logical connections between indicators. Frequently updated data sources require the knowledge base refresh cycle to match the data update rhythm. Otherwise, generated marketing content will contain outdated data. Structured document formats require specific parsing rules, otherwise field extraction errors will occur. Quotation data with the unit yuan/ton must strictly match input and output unit specifications, to avoid unit confusion in marketing content. Marketing content for financial scenarios requires accurate data. Data sources recalled by the model must first screen public data from authoritative channels, and exclude non-standard private quotation content.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Coking coal marketing content requires multi-dimensional professional indicators, requiring sufficient context to support logical coherence |
chunkSize | 600–800 characters | Coking coal data documents contain multiple related professional indicator fields. Too small a chunk size will separate indicator associations, while too large a chunk size will prevent accurate recall of detailed content |
RECALL_TOP_N | Top 6–8 entries | Coking coal marketing content needs to balance data across multiple dimensions including spot, futures, and downstream demand. Too many recalled entries will lead to content redundancy |
SIMILARITY_THRESHOLD | 0.75–0.85 | Coking coal professional terms have high recognizability. A threshold that is too low will mix in irrelevant coal category data, while a threshold that is too high will fail to recall relevant detailed indicators |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Coking coal historical quotation documents are mostly batch CSV/Excel files with large single-file data volume, requiring extended parsing timeout |
ONE_API_MODEL_TOKEN | Calibrated based on actual testing | Token consumption for coking coal-related marketing content is higher than general content, requiring adjustment of token configuration based on actual call logs |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the configuration.
Three common configuration mistakes
- Issue: After upgrading FastGPT from version 4.8.14 to 4.8.15, the configured
qwenplusmodel automatically switches togpt-4oduring chat. Cause: The model binding field in the application configuration was not properly migrated after the version upgrade, or the global default model overrode the application-level configuration. - Issue: When using the
markertool to process batch coking coal quotation documents, the processing speed is much lower than expected. Cause: Parsing parameters were not adjusted for coking coal's structured CSV/Excel files. The default parsing mode is adapted for unstructured documents, leading to excessive redundant processing steps. - Issue: An error
Error 1406 (22001): Data too long for column 'models'is returned when adding a token in OneAPI. Cause: The configured coking coal-related model list is too long, exceeding the character limit of the database field, resulting in field overflow.
How to confirm successful configuration
- Trigger a coking coal marketing content generation request, and verify that the professional coking coal indicators included in the returned content match the configured knowledge base data.
- Check the application's model binding log to confirm that the called model is the specified configured model, and has not been overwritten by the global default model.
- Upload a coking coal historical quotation CSV file, and verify that the parsing time is within
300 seconds, and the parsed fields are complete and without missing values. - Call the OneAPI token test interface to confirm that the token can normally call the configured coking coal-related model, with no field length error reported.
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.