What Data for This Category Looks Like
Thermal coal-related data comes primarily from public disclosures by the China Coal Industry Association, major port exchanges, and railway transport departments. This data is adapted for financial marketing content and customer acquisition scenarios across the thermal coal industry chain. Spot prices and transport capacity data are updated daily. Monthly supply and demand reports are released in the mid-to-late portion of each month. Production capacity adjustment information is updated quarterly.
Public documents include structured tables with fields such as origin, calorific value, and tax-included price, semi-structured industry analysis documents, and unstructured supply and demand meeting minutes. Single long documents can reach tens of thousands of characters. Field units include yuan/ton, megajoules/kilogram, ten thousand tons, and others.
Constraints Imposed on Model Integration and Configuration
The multiple update frequencies, mixed document structures, and specialized field units of thermal coal data create multiple constraints for model integration and configuration, to support content generation needs for financial marketing and customer acquisition scenarios.
Daily updated spot data requires real-time incremental index configuration to prevent static knowledge base lag from reducing marketing content timeliness. Long documents and structured fields need appropriate segmentation and parsing parameters to avoid context fragmentation or field recognition errors that undermine the rigor of professional content. Multi-dimensional business data needs support for multiple data source calls, to ensure marketing content can integrate core information such as spot prices, transport capacity, and supply and demand. At the same time, unit differences across different sources require unified mapping rules to avoid unit confusion in generated marketing content.
How to Configure Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Used for thermal coal industry chain financial marketing content generation. Single monthly reports can reach tens of thousands of characters. The standard 600-second parsing duration often causes interruptions. Extending to 900 seconds ensures complete parsing of long documents |
chunk_size | 800–1200 characters | Thermal coal documents contain specialized terminology and long sentences. Too-short segmentation will break the logical connection of supply and demand. Too-long segmentation will introduce irrelevant context, affecting the professional coherence of marketing content |
VECTOR_STORE_BATCH_SIZE | 50 items/batch | Thermal coal structured spot data has many entries. Too large a batch will trigger index node overload. Too small a batch will extend full index time. This value adapts to the processing needs of batch marketing materials |
RECALL_TOP_K | Top 8–12 items | Thermal coal marketing content needs to cover three core dimensions: spot price, transport capacity, and supply and demand. Too many recalled items will dilute the weight of core information, affecting the accuracy of customer acquisition content |
MODEL_TOOL_ENABLE | Enabled | Thermal coal marketing content requires real-time updated supply and demand data. Calling tools can pull the latest public market data to compensate for the lag of static knowledge bases |
UPLOAD_FILE_MAX_SIZE | 200 MB | Single monthly supply and demand report PDFs can reach 150 MB. This setting reserves sufficient space for batch uploads of industry documents, adapting to the needs of batch import of marketing materials |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: Vector indexing tasks stay in "pending" or "executing" state for a long time, or return the
ETIMEDOUTerror code. Cause: TheVECTOR_STORE_BATCH_SIZEparameter was not adjusted for thermal coal's structured data. An overly large batch parameter causes index node load overload. - Symptom: After enabling model tool calls, the generated marketing content does not include the latest thermal coal market data. Cause: The public data source permissions of the model tool were not correctly bound, or the trigger conditions for tool calls were not set, causing the model to not actively call tools to pull real-time information.
- Symptom: Field recognition errors occur in uploaded thermal coal industry documents, such as recognizing calorific value units in non-standard formats. Cause: The structured data parsing switch of the text understanding model was not enabled, causing the model to fail to automatically adapt to thermal coal's specialized fields and unit rules.
How to Verify Successful Configuration
- Upload a thermal coal industry document longer than 10,000 characters, check whether the parsing progress is completed within the preset timeout period. Adjust the timeout parameter to meet the document parsing requirements.
- Submit a marketing content generation request that requires multi-dimensional data, check whether the generated content covers the specified thermal coal business dimensions. Adjust the number of recalled items and the tool call switch to match the content requirements.
- Submit batch structured spot data, check the completion time of the indexing task. Adjust the batch processing parameters to balance indexing efficiency and stability.
- Verify the model tool call function, submit a generation request that requires real-time market data, check whether the returned content includes the latest public thermal coal-related information. Confirm that the tool permissions and trigger logic are working correctly.
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.