What this category of data looks like
This category of data mainly comes from the national unified construction project EIA declaration and approval system, ecological environment zoning control results, pollution discharge permission platform, and monitoring data. The update frequency is irregular. Daily processed documents include environmental impact statements or report forms, approval applications, public participation explanations, technical assessment opinions, EIA approvals, and similar materials. These documents use a multi-chapter formal official document structure, and contain fields such as project category, EIA level, pollution prevention measures, and public participation status. There are no unified fixed units, and the content varies depending on the document type.
What constraints these characteristics impose on model access and configuration
Data sources for this category are scattered. Information must be obtained by connecting multiple system interfaces. Irregular updates require models to support incremental synchronization. Long multi-chapter official documents with nested structures require models to have sufficient long-context processing capabilities. Fields change flexibly based on document type, so custom field extraction rules must be supported during configuration. The seriousness of approval documents requires model outputs to be traceable. The ability to configure original text citations must be retained. Review errors may lead to serious consequences. Failure handling mechanisms and log retention functions must be configured.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-chapter long text structure of EIA documents, covers complete approval file content |
maxTokens | 2000–3000 token | Meets the output length requirements for a single review result, avoids truncating core review opinions |
quoteMaxToken | 1500 token | Retains a reasonable length for original text citations, ensures traceable content is complete |
toolChoice | auto | Supports automatic tool calls to complete multi-data-source data verification and field extraction |
temperature | 0.2–0.4 | Reduces output randomness, ensures the rigor of review results, and meets government document requirements |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Connects to compliant model call channels, ensures data transmission security |
AIPROXY_API_TOKEN | Fill in the dedicated token generated by the platform | Verifies call permissions, prevents unauthorized access |
Three common configuration errors
- Phenomenon: Authentication failure or connection error occurs when calling the model. Detailed call logs cannot be viewed. Cause:
AIPROXY_API_TOKENis not filled correctly, orAIPROXY_API_ENDPOINTis configured incorrectly. A valid connection cannot be established. - Phenomenon: The model's review output does not match the original text citations. Content deviations occur. Cause:
temperatureis set too high. This leads to excessive output randomness that does not meet the rigor requirements of government review. - Phenomenon: Qwen3 series models cannot be called to complete EIA review tasks. Cause:
AIPROXY_API_ENDPOINTis not configured as the dedicated interface address adapted to Qwen3, or the model's supported context parameter range is not confirmed.
How to confirm the configuration is complete
- Upload a standard environmental impact report form, trigger model review, and check if the output includes core fields such as project category and EIA level. Confirm that the field extraction rules take effect.
- View model call logs. Confirm that
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations are read correctly. No authentication or connection errors appear. - Adjust the
temperatureparameter, compare the differences between two output results. Confirm that the parameter's impact on output randomness meets expectations. - Test uploading a long document. Confirm that the
maxContextconfiguration covers the complete document content, with no truncation.
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.