What data for this category consists of
Thermal coal investment research data mainly comes from National Energy Administration official announcements, coastal port spot price platforms, futures exchange delivery data, and industry association supply and demand weekly reports. Update frequencies include daily spot prices, weekly industry updates, monthly supply and demand balance sheets, and annual industrial planning reports. Document formats are mostly structured XLSX tables and PDF analysis reports. Core fields include calorific value, sulfur content, ash content, spot price, port inventory, transportation mileage, and others. Common units are kcal/kg, percentage, yuan/ton, and ten thousand tons. Some historical data is exported in bulk as CSV files, containing monthly statistical entries spanning multiple consecutive years.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Timing differences across multiple data sources require associating the publication time of specified data in multi-turn dialogue to avoid mixing statistical results from different cycles. There are many structured fields with specialized units; prompts must clearly define field identification and unit standardization to prevent errors such as mixing calorific value and volume units. Statistical standards vary across different ports and grades of thermal coal. The user-specified category dimension must be retained in the dialogue context to avoid mixing cross-category parameters. Parsing results for large-volume structured documents must be linked to the dialogue context to ensure the model can accurately match specific data entries mentioned by the user.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 8–12 turns of dialogue context | Thermal coal investment research dialogues often involve comparisons of multi-dimensional parameters, requiring sufficient context to avoid parameter confusion, while preventing excessive length from causing model context overflow |
UPLOAD_FILE_MAX_SIZE | 500 MB | Monthly supply and demand reports and historical price databases for thermal coal are mostly large-volume XLSX or CSV files, requiring support for large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large-volume structured tables requires a longer time, to avoid parsing failure due to timeout |
Recall count | Top 6–10 entries | Thermal coal investment research data has multiple dimensions, requiring sufficient recalled historical data to support comparative analysis, while avoiding interference from redundant information |
Similarity threshold | 0.75–0.85 | Precise matching of specific thermal coal parameters (such as calorific value grades) is required to avoid confusion with documents of other coal categories |
Chunk size | 800–1200 characters | Structured tables and analysis reports for thermal coal have long paragraphs; segmentation must retain complete field descriptions and data logic |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When calling the file upload recognition interface, the thermal coal database in XLSX format cannot be accessed by AI dialogue, or error code 413 is returned. Cause: The uploaded file size exceeds the threshold configured by
UPLOAD_FILE_MAX_SIZE, or the structured document parsing function is not enabled. - Phenomenon: During multi-turn dialogue, the model cannot correctly associate variables in user questions, such as failing to bind the "5500 kcal thermal coal" parameter specified by the user. Cause: The prompt does not clearly bind the variable name in the user's question, or the parameter information entered by the user is not retained in the context configuration.
- Phenomenon: The dialogue history generated in the debug preview cannot be cleared, or the historical data of a specified application cannot be deleted via the interface. Cause: The history clearing button in the workspace is not used, or the correct application ID parameter is not specified when calling the clearing interface.
How to Confirm Configuration is Correct
- Upload a test thermal coal XLSX table, check whether core fields such as calorific value, price, and inventory are correctly extracted in the parsing result, and whether the field units match the document.
- Initiate more than two rounds of multi-turn dialogue, for example, first ask for the spot price of 5500 kcal thermal coal, then ask for the port inventory in the same period, check whether the model retains the "5500 kcal" category restriction and does not mix data of other grades.
- Adjust the
Similarity thresholdconfiguration, then test queries for documents unrelated to thermal coal, check whether the model correctly filters irrelevant results. - Upload a test document larger than 100 MB, check whether parsing completes within the time configured by
PARSE_FILE_TIMEOUT_SECONDSwithout timeout errors.
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.