What the data for this category looks like
Thermal coal investment research data covers four source types: production, circulation, trading, and policy. Update cadences are layered to match different business requirements. Spot price data updates daily. Port inventory data updates weekly. Long-term contract pricing policies update monthly or at temporary nodes. Each data entry mostly combines structured tables and industry interpretation snippets. Fields include origin code, calorific value, sulfur content, daily closing price, and port yard stock. Some entries also include cross-region transport mileage and policy document snippets.
What constraints these characteristics impose on model access and configuration
Thermal coal data’s layered update cadence, high proportion of structured content, and diverse field units create clear constraints on model access and configuration. First, layered update data requires differentiated sync trigger logic to avoid unnecessary full pulls. Second, documents mixing structured tables and interpretation text must be split into separate recall units. This ensures the model accurately matches precise values in tables and policy interpretation in text. Third, same-type indicators with multiple units need clear mapping rules. This prevents the model from confusing same-dimension data with different units.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | A single thermal coal recall unit typically ranges from 200 to 800 characters. Sufficient context must be reserved to accommodate multiple structured fields and interpretation text |
RECALL_TOP_N | Top 8–12 entries | Thermal coal data has multiple dimensions. A sufficient number of recalled entries must cover origin, price, policy and other categories to avoid missing key dimensions |
PARSE_TABLE_MODE | Structured extraction priority | A large number of tables in thermal coal data contain precise numerical indicators. Prioritizing structured extraction reduces model confusion |
SYNC_INTERVAL | Layered configuration by data type: Spot data 1 hour, inventory data 12 hours, policy data 24 hours | Different data sources have large differences in update frequency. Layered sync reduces unnecessary calls and resource consumption |
PROMPT_TEMPLATE_FIELD_MAPPING | Enable mandatory field mapping | Thermal coal fields have diverse units. Mandatory field mapping ensures the model outputs results with unified units to meet investment research analysis requirements |
MODEL_TIMEOUT | 600 seconds | Structured data parsing and multi-dimensional recall require longer processing time to avoid timed-out interrupted calls |
The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three common configuration errors
- An Alibaba Cloud model invocation returns a 400 error. For FastGPT V4.9.7, no dedicated field mapping rules are configured for thermal coal data. The model cannot recognize custom calorific value and sulfur content field formats, triggering parameter validation failure.
- A Tongyi Qianwen 3-235B model invocation returns a 404 error. The proxy address filled in the model access configuration is not bound to the corresponding team’s resource pool, so requests cannot be routed to the correct model instance.
- Thermal coal price-related files are indexed, but the model does not reference specified content. The recall count configuration is too low, failing to cover recall entries containing specific price values. Only irrelevant policy interpretation snippets are retrieved.
How to confirm the configuration is set correctly
- Run a single model invocation test for thermal coal data. Verify if the returned results include precise values and units matching the input fields.
- Check model invocation logs to confirm sync tasks trigger according to preset layered intervals, with no duplicate or timed-out invocation records.
- Test multi-dimensional queries such as "Port closing price of 5500 kcal thermal coal in northern Shanxi". Verify if the recall results cover relevant dimensions including origin, calorific value, price and other related categories.
- Check the team’s model usage permission configuration. Confirm that the accessed models are only open to team members related to the investment research knowledge base.
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.