What data for this category includes
Data for this category originates from work safety supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems, with a daily update frequency. Commonly processed documents include hazard investigation lists, hazard management ledgers, rectification notices, review records, and major hazard suspension and supervision orders. Core document fields include hazard number, investigation time, rectification responsible person, rectification deadline, and review results. Document formats exported from different systems have minor differences, requiring adaptation to multi-source heterogeneous files and declaration materials.
Constraints imposed by these characteristics on model access and configuration
This page covers configuration logic for FastGPT v4.17.0. Multi-source data sources require configuration items to align with access rules of different business systems, preventing call failures from mismatched addresses or keys. Mass daily updated file data requires model call context and token parameters to match document lengths, avoiding parsing timeouts or truncation. Differences in document formats require configurations with flexible settings, while maintaining result stability for core workflows such as hazard determination and rectification tracking. Errors in the review link directly impact work safety responsibilities, so parameter values must be strictly controlled to avoid determination deviations.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Setting |
|---|---|---|
AIPROXY_API_ENDPOINT | Use the API access address specified by the target business system, such as the exclusive access domain name of the supervision system | Ensure normal access to multi-source file data, avoid 404-level call errors |
AIPROXY_API_TOKEN | Use the exclusive access key assigned by the business system | Ensure data access permissions, prevent unauthorized calls |
maxContext | 8000–12000 token | Adapt to the context requirements of long documents such as hazard management ledgers and major hazard suspension and supervision orders, avoid content truncation |
maxTokens | 2000–3000 token | Control the output length of a single round of review, ensure complete presentation of file field extraction and determination results |
temperature | 0.2–0.4 | Ensure result stability for links such as hazard determination and rectification deadline verification, avoid random deviations |
quoteMaxToken | 1500–2000 token | Limit the maximum length of referenced documents, ensure reasonable allocation of context windows |
Three common configuration errors
- Symptom: Model calls return 404 page not found. Cause:
AIPROXY_API_ENDPOINTis not correctly set to the target business system’s access address, orAIPROXY_API_TOKENis not bound to valid access permissions. - Symptom: Model results for hazard determination standards are overly variable. Cause:
temperaturevalue is too high, leading to random deviations in determination logic that do not match the stability requirements of the review scenario. - Symptom: Parsing requests fail after uploading a file. Cause:
maxContextis not reasonably adjusted to match the content length of long documents, exceeding the model’s context limit, or system resources do not match the processing load of daily updated data.
How to verify successful configuration
- Submit a single standard rectification notice sample, and confirm that the rectification requirements in the model output match the document content.
- View API call logs to confirm that request parameters for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave no format errors. - Run a batch test to verify that the call success rate for daily updated file data meets business expectations.
- Adjust the
temperaturevalue, and confirm that result stability meets work safety review requirements.
The values provided here are common starting points, and should be evaluated 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.