What the data for this category looks like
Coal transportation and sales business data mainly comes from internal transportation and sales scheduling systems, contract management modules, and weighing systems. Data updates trigger per individual business node. The system syncs immediately after a single waybill is weighed. Bulk business data updates daily. Each business document includes fields such as waybill number, coal type, shipping location, receiving location, approved tonnage, approval status, and completion deadline. Most field units are tons, train trips, and calendar days. Document structure is fixed, and fields have strong correlation.
What constraints these characteristics impose on model integration and configuration
The real-time update nature of coal transportation and sales business data requires limiting context cache duration during model integration, to prevent calls to outdated business information. The large number of highly correlated fields requires the model to accurately match fields with business logic, to avoid generating content that deviates from actual business rules. The relatively high token count per individual business document requires setting a reasonable maximum token limit for single input, to prevent request overrun. Additionally, transportation and sales data involves internal business processes. Model integration must prioritize support for intranet API endpoint configuration, to ensure data transmission compliance.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 72 hours | Matches the standard approval cycle of coal transportation and sales business, avoids caching old data beyond the business handling deadline |
maxTokens | 8000–12000 token | Adapts to the average token scale of single transportation and sales business documents, prevents requests from exceeding the model's processing limit |
quoteMaxToken | 2000 token | Limits the token proportion of referenced business data, ensures sufficient token space for core guidance logic |
temperature | 0.2–0.4 | Ensures accuracy of business process guidance, avoids generating content that deviates from actual business rules |
AIPROXY_API_ENDPOINT | intranet-compliant API address | Adapts to intranet data docking requirements of coal transportation and sales business, ensures data transmission security |
AIPROXY_API_TOKEN | exclusive encrypted access key | Distinguishes exclusive business access permissions, complies with internal data security management specifications |
Three common mistakes
- A 422 status code is returned during calls, with a prompt indicating parameter format error. The cause is that request parameters are not spliced according to the fixed format of transportation and sales business fields, so the model cannot recognize the business context.
- The model test succeeds on the interface, but actual business guidance has no response. The cause is that
AIPROXY_API_ENDPOINTis configured as a public network address, which does not adapt to intranet data access permissions, so real-time transportation and sales business data cannot be pulled. - Timeout occurs when the workflow calls an external model. The cause is that the cache duration of
maxContextis not limited, so the request carries too much historical data and exceeds the model's processing limit.
How to confirm the configuration is complete
- Log in to the FastGPT v4.17.0 backend, enter the model configuration page, and verify that
AIPROXY_API_TOKENexactly matches the applied exclusive key. - Initiate a simulated call for a single transportation and sales business, and check that the returned result contains accurate fields and process steps for the current business.
- View the model call logs, and confirm that the cache duration of
maxContextmeets the preset 72-hour threshold. - Test process guidance across business nodes, and verify that the model can correctly correlate transportation and sales data fields from different stages.
The values provided are common starting points and should be measured against the reader'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.