What the data for this category looks like
Metallurgical coal financial report data primarily comes from annual, semi-annual, and quarterly financial reports publicly disclosed by domestic metallurgical coal producers, industry operation data released by the China Coal Industry Association, and real-time transaction data from major port spot price platforms. Data update cycles follow statutory disclosure periods for corporate financial reports. Monthly industry data is released in the middle and late days of each month, and spot prices are updated daily. The document structure includes fields such as core operating indicators, production costs, upstream and downstream linkage data, and capacity planning. Unique fields include coking coal recovery rate, total cost per ton of coal, port delivery price (unit: yuan/ton), and long-term contract fulfillment rate. The core measurement benchmark is mostly per ton.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The varying update frequencies of multi-source data for metallurgical coal financial reports require dynamic adaptation to the timeliness requirements of different data during multi-turn dialogue. Avoid pairing outdated spot data with quarterly financial reports. The calculation logic for unique fields such as coking coal recovery rate requires clear guidance in prompt engineering to prevent large language models from confusing parameters with other coal types. Long financial report content contains significant text volume. During multi-turn dialogue, limit the proportion of valid information in the context window to prevent redundant data from interfering with core indicator derivation. Cross-data source indicator alignment, such as differences between corporate financial report output and industry statistical data, requires a verification step in multi-turn dialogue. Ensure that the scope of data sources is clearly specified when asking questions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The core text of a single metallurgical coal financial report is mostly 5000-8000 characters. This range retains complete core financial report segments while avoiding context window overflow |
recallTopK | Top 6–8 entries | Associated data sources for metallurgical coal financial reports mostly include corporate financial reports, industry data, and spot prices. Too many recalled entries will introduce distracting indicators from unrelated coal types |
similarityThreshold | 0.72–0.78 | This range distinguishes the matching accuracy of metallurgical coal-specific indicators and general coal indicators, preventing confusion between coking coal and thermal coal parameter definitions |
toolCallTimeout | 120 seconds | Cross-data source verification requires additional time to align and calculate multiple financial reports and industry data |
promptTemplate | Please prioritize using metallurgical coal financial report data from the user-specified data source. If no data source is specified, default to using publicly available corporate financial reports and monthly industry data from the past three months. Clearly label the data source and update time | Adapts to the multi-source, multi-update cycle characteristics of metallurgical coal financial reports, and clarifies data source constraints and timeliness requirements in the prompt |
conversationLogRetention | Set to 7–30 days based on business requirements | Conversation logs for metallurgical coal financial report analysis must be retained for a sufficient period for review. This prevents loss of multi-turn dialogue context due to missing logs |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Application conversation logs created via API appear empty or partially missing. Cause: The
conversationLogRetentionparameter is not configured, or the log storage volume is not correctly mounted in the Docker configuration file, resulting in non-persistent logs. - Phenomenon: Context is automatically lost during multi-turn dialogue, and subsequent questions cannot associate previously mentioned metallurgical coal financial report indicators. Cause: A reasonable range for
maxContextis not set, or the automatic context continuation function is not enabled, causing historical dialogue that exceeds the window to be truncated. - Phenomenon: Using the same prompt to ask questions leads to deviations between metallurgical coal financial report analysis results and expectations. Cause: The calculation logic for metallurgical coal-specific fields is not clearly defined in the prompt, and the
similarityThresholdis not configured to filter irrelevant matches, causing the large language model to confuse indicators from other coal types.
How to Confirm Proper Configuration
- Call the
/v1/chat/completionsAPI to initiate a test dialogue. Ask "What is the coking coal recovery rate of a certain metallurgical coal enterprise in 2024". Check whether the returned results include the specified data source's indicators and label the update time to verify that thepromptTemplateis effective. - View the Docker container logs or the conversation log module in the platform backend. Confirm that after configuring
conversationLogRetention, conversation logs are normally persistently stored. - Adjust the value of
similarityThreshold. Test asking "Metallurgical coal port delivery price" to check whether results related to thermal coal are filtered out, verifying that the matching accuracy meets expectations. - Initiate two consecutive questions: First ask "Quarterly output of a certain metallurgical coal enterprise in 2024", then ask "The proportion of long-term contracts corresponding to this output". Verify that the context correctly associates the indicators from the previous question.
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.