What the Data for This Category Looks Like
Data sources for thermal coal financial report analysis include periodic reports of domestic and overseas publicly traded companies involved in thermal coal, supply and demand monitoring data published by industry associations, and publicly available port transaction settlement data. Two update schedules are used: periodic financial reports of listed companies are disclosed on a fixed quarterly and annual basis, while real-time industry monitoring data is updated weekly. Document structures include two core modules: the main business breakdown section of financial report texts, and the statistical table module for industry data. Covered fields include thermal coal production volume, sales volume, average sales price, inventory level, and other relevant metrics. Corresponding units include ten thousand tons, yuan per ton, and similar standard units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The classified disclosure and update cycles of thermal coal data require multi-turn dialogue to clearly separate periodic financial report queries from real-time industry data queries, to avoid mixing information across different time dimensions. The specificity of data fields and units requires prompts to mandate that the model annotate corresponding units when returning results, to prevent confusion over data dimensions. The scattered structure of source documents requires multi-turn dialogue to retain contextual time anchors and business dimensions, to eliminate the need for users to repeatedly explain query scope.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 4000–6000 characters | Individual thermal coal financial report documents often exceed 2000 characters. Context for multi-turn dialogue (such as report periods and data dimensions) must be retained to avoid truncation of critical information |
RECALL_TOP_K | Top 6–8 entries | Thermal coal data fields are scattered. A sufficient number of associated data fragments must be retrieved to cover different dimensions including sales volume, price, and inventory |
SIMILARITY_THRESHOLD | 0.72–0.78 | This range distinguishes associated data related to thermal coal from fragments of other coal types (such as coking coal), to avoid inclusion of irrelevant data |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Individual thermal coal financial report documents contain substantial content. Sufficient time is required to complete structured parsing |
HTTP_REQUEST_TIMEOUT | 30 seconds | When calling industry data interfaces, this range adapts to the response duration of weekly data interfaces |
systemPrompt | Must include "Only extract business data related to thermal coal, and annotate corresponding units" | Forces the model to focus on the target category, to avoid confusion with data from other coal types |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific scenarios require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: HTTP network requests configured in the workflow are not triggered, and responses are generated directly by the model. Cause: The HTTP request node is not set as a required preceding execution node, or matching rules for triggering the request are not configured.
- Symptom: Final output includes reply content from a prior AI conversation. Cause: Context filtering rules do not exclude non-target output from previous conversations, or parameters for retaining only the output of the final node are not set.
- Symptom: Dependent parsing tools cannot be called within the container. Cause: Required dependencies are not pre-installed during the container build phase, or necessary permissions are not granted to the container.
How to Confirm Proper Configuration
- Upload a thermal coal financial report document from a publicly traded company, and verify that extracted fields after parsing only include business data related to thermal coal.
- Initiate two rounds of dialogue: first ask for thermal coal revenue for a specific quarter, then ask for the average price during the same period. Verify that the second round of dialogue retains the time anchor from the first round.
- Trigger the HTTP request node in the workflow, and check run logs to confirm the request was sent and corresponding data was obtained.
- Test context retention for multi-turn dialogue, confirm that subsequent questions do not require repeated explanation of data time ranges.
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.