Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data is sourced from industrial control systems for coal mining, power generation, and coal transportation and storage, as well as enterprise ERP ledger systems. Update rhythms include real-time operating condition data and daily or shift summary ledger records. A single ledger document contains structured fields such as equipment number, operating duration, coal consumption, power generation, and maintenance records, supplemented by unstructured maintenance report attachments. Field units include hours, tons, megawatt-hours, grams per kilowatt-hour, and others. Ledger fields differ across business links, and must be sorted and categorized by business module.
Constraints on model access and configuration
The high-frequency updates of real-time operating condition data require that model call response latency be controlled within a reasonable range. The maximum length of a single context must be limited to avoid timeouts. The mixed multi-field ledger structure requires the model to accurately identify field units and meanings for different business modules. Appropriate tool call rules must be configured to adapt to structured data parsing. The presence of unstructured maintenance reports requires the model to handle both text and structured information. The temperature parameter must be adjusted to balance accuracy and flexibility. Differences in ledger fields across business links require configurable switchable context templates to prevent cross-module data confusion.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed multi-field context length of coal-fired power integration ledgers, avoiding exceeding the model's supported limits |
maxTokens | 2000–3000 token | Meets the content length requirement for single ledger report generation, while controlling single-call costs |
toolChoice | auto | Adapts to mixed processing of structured ledgers and unstructured maintenance reports, automatically triggering tool calls to parse data |
temperature | 0.3–0.5 | Balances the accuracy of ledger data parsing and the standardization of report generation, avoiding excessive divergence |
AIPROXY_API_ENDPOINT | Locally deployed large model service address | Adapts to the enterprise internal network environment, preventing data exfiltration |
AIPROXY_API_TOKEN | Access key generated within the enterprise | Ensures the security of model calls, restricting unauthorized access |
Three common configuration errors
- Model calls return error messages indicating context length exceeded. The cause is failure to adjust the
maxContextparameter based on the multi-field characteristics of coal-fired power integration ledgers, resulting in the passed context exceeding the model's supported upper limit. - Field units are mixed in generated reports, for example, mixing kilograms and grams per kilowatt-hour as the unit for coal consumption. The cause is failure to set
toolChoiceto auto mode, so the model cannot automatically identify field unit rules for different business modules. - Connection timeout occurs when calling the model after deployment. The cause is failure to correctly configure
AIPROXY_API_ENDPOINTas the local large model service address, leading to network connectivity issues.
How to confirm successful configuration
- Upload a single coal-fired power integration ledger document, check whether the model can accurately extract core fields such as equipment number and operating duration, and verify that field units meet business requirements.
- Trigger a single ledger summary report generation, check whether the generated content includes all specified business module data with no obvious format confusion.
- View model call logs, confirm that response times meet business requirements with no frequent timeouts.
- Configure context templates for different business modules, after switching, check whether the model can correctly adapt to the field rules of the corresponding module.
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.