What this category of data looks like
Coal transportation and sales ledger data comes primarily from weighing systems, coal quality test reports, transportation settlement documents, and customer delivery receipts. Individual waybill data includes fields such as coal type identifier, shipment volume, net calorific value as received basis, total moisture, shipment origin, receiving entity, settlement unit price, and transportation cost. Common units are tons, kilocalories per kilogram, and yuan per ton. Ledgers generate detailed entries from daily waybills. They aggregate full-cycle data on a monthly basis. Report documents integrate multi-dimensional statistical content over weekly, monthly, or quarterly cycles. Each report covers the three core indicators of volume, price, and quality.
Constraints on model access and configuration
The multi-field structured data and specialized terminology of coal transportation and sales require model access configuration to support domain semantic understanding. Daily updated waybill details and monthly summary report cycles require configuration to enable incremental data access and periodic batch processing. Fields use multiple units including tons, kilocalories per kilogram, and yuan per ton. Models must accurately pair numerical values with their corresponding units to avoid format errors. The need to integrate multi-dimensional data into a single report requires context configuration to cover the full set of fields. This prevents information truncation from compromising the completeness of summary results.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the complete set of ledger fields and historical summary data, preventing key information from being truncated |
maxTokens | 2000–3000 token | Adapts to the generation length of a single monthly report, balancing output completeness and model processing speed |
quoteMaxToken | 1500 token | Retains reference content for core waybill data, avoiding redundant information from occupying model context resources |
temperature | 0.1–0.3 | Adapts to the accuracy requirements of ledger summary and report generation, reducing random output deviations |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address provided by the platform | Ensures correct routing of model requests, avoiding cross-region or cross-platform call delays |
toolChoice | auto | Supports the model to automatically select whether to call tools for structured data processing, adapting to multi-source data integration scenarios |
Three common configuration errors
- Symptom: After upgrading to FastGPT v4.17.0, the specified configured model switches to another model when re-entering the chat interface after saving. Cause: Cache configurations for model access were not updated after the version upgrade, or configuration items were not properly written to the system database.
- Symptom: Model processing speed drops noticeably after uploading documents such as coal quality test reports. Cause: The
maxContextparameter was not adjusted appropriately, causing the model to load excessive redundant historical ledger data and consume too many processing resources. - Symptom: A data length limit exceeded error appears when submitting
AIPROXY_API_TOKEN. Cause: The configured token value exceeds the storage limit of the platform database field, and the valid length was not truncated as required.
How to verify proper configuration
- Enter the model access configuration interface, and check that the filled values for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the official information provided by the platform. - Run a small-scale ledger summary test, and confirm that the matching between fields and units in the output conforms to the format requirements of the original data.
- Review the model call log, and confirm that the actual usage values of
maxContextandmaxTokensstay within the configured range, with no abnormal truncation. - Test a single incremental data access workflow, and confirm that newly generated waybill data can be successfully integrated into the ledger to generate corresponding statistical content.
The values provided here are common starting points, and should be evaluated against the user's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.