What data for this category includes
This category of data originates from work safety supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems. It routinely produces documents including hazard inspection lists, hazard management ledgers, rectification notices, review records, and major hazard suspension and supervision orders. Data updates occur daily. Core fields include hazard level, rectification responsible entity, rectification deadline, review result, and other related fields. The data focuses on closed-loop risk and hazard management. It tracks recording and circulation across the full inspection, rectification, and review workflow. It does not include additional processes related to administrative penalties.
Constraints imposed on model access and configuration
Decentralized data sources and daily updates require model access to support format compatibility for data pulling from multiple systems, to prevent field mapping errors. Diverse document types with both structured and unstructured content require configuring processing logic that supports mixed documents. Fields such as rectification deadline and review result are directly tied to compliance requirements. Model output must strictly align with field definitions, and cannot be modified arbitrarily. Hazard level determination relies on industry standard texts. The knowledge base used for model calls must include the corresponding standard content, and retrieval results must accurately link to original document sources.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context splicing requirements for multiple hazard ledgers and rectification notices, to avoid context overflow |
quoteMaxToken | 2000–3000 token | Ensures complete reference of key documents such as hazard inspection lists and law enforcement records, to support content traceability during work consultation |
temperature | 0.1–0.3 | Ensures rigorous output for hazard determination and rectification suggestions, to meet compliance requirements |
toolChoice | auto | Automatically selects whether to call knowledge base retrieval, to adapt to dynamically changing hazard data scenarios |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the platform | Ensures stability and permission compliance for model calls |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Implements identity verification for model calls, to prevent unauthorized access |
Three common configuration errors
- Context token count exceeds the limit error occurs. This happens when the value range of the
maxContextparameter is not restricted, and multiple long documents are directly spliced, exceeding the token limit supported by the model. - Environment variables cannot be called during the problem optimization stage. This occurs when environment variable permissions are not enabled in the configuration, or variable names do not follow platform specifications.
- The number of knowledge base reference results does not meet expectations. This is caused by incorrectly configuring non-specified retrieval logic, leading to abnormal vector model calls.
How to confirm successful configuration
- Use the document testing tool in FastGPT v4.17.0 to upload a single hazard management ledger, trigger model question answering, and check whether returned results include accurate references to the document source.
- Adjust the
maxContextparameter value, test question answering stability after long context splicing, and confirm no token overflow errors occur. - Verify environment variable configuration by calling the problem optimization function through the test interface, and confirm variables can be read normally and take effect.
- Check model call logs, and confirm that the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches platform requirements.
The values provided here are common starting points, and should be evaluated against deployment-specific 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.