What data does this category include
This category of data mainly comes from online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. The update frequency is daily. Daily work generates documents including on-site inspection records, sampling records, monitoring reports, administrative orders for correction, and administrative penalty decisions. The document structure is fixed, including standardized fields such as law enforcement subject, inspection/sampling time, location information, working condition records, pollutant detection values, and rectification requirements. Some law enforcement documents require association with real-time synchronized monitoring value data.
What constraints do these characteristics impose on the model access and configuration link
Because data sources are scattered and updated daily, the configuration link must support synchronized access to multi-source data and real-time verification, to avoid introducing outdated or irrelevant monitoring data. Due to diverse document types and fixed fields, context extraction rules adapted to different documents must be preset to prevent redundant content from interfering with law enforcement document generation logic. Law enforcement and monitoring data must be strictly matched, so the configuration must limit the model to only call associated fields of specified data sources, to prevent data misalignment from causing errors in law enforcement documents. Document generation must comply with fixed formats, so output format constraints must be configured to ensure generated content aligns with law enforcement document templates.
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 | Daily work requires associating multiple on-site inspection records, monitoring reports and other documents, so sufficient context must be accommodated to ensure coherent law enforcement logic |
quoteMaxToken | 2000–3000 token | Key fragments such as core monitoring values and law enforcement personnel signatures must be extracted, to avoid redundant content interfering with document generation |
temperature | 0.1–0.3 | Law enforcement documents must remain rigorous and accurate, and subjective deviations or format inconsistencies should be avoided |
toolChoice | auto | The model must automatically select between monitoring data comparison tools or document generation tools based on tasks, to adapt to different scenarios |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the platform | Ensure correct routing for model requests, to avoid call failures caused by cross-domain or address errors |
AIPROXY_API_TOKEN | Fill in the exclusive token assigned by the platform | Complete identity verification to ensure the legality and security of model calls |
Three common misconfigurations
- Issue: Generated on-site inspection records do not include specified law enforcement personnel information or monitoring location data, and core fields are empty. Cause: Context extraction rules are not properly configured, and documents from specified data sources are not included in the model's call scope.
- Issue: The model call returns a 504 timeout status code. Cause: The
maxContextconfiguration value is too large, exceeding the single request limit of the model interface. - Issue: The custom reranking model cannot be called normally, returning a connection error. Cause: The custom address of
AIPROXY_API_ENDPOINTis not filled in correctly, or matching token verification information is not configured.
How to confirm correct configuration
- Submit a simulated request to generate an on-site inspection record, and check whether the returned content includes preset law enforcement fields to confirm that the matching degree meets preset standards.
- Import a specified third-party test report, submit a request to generate a monitoring report associated with the report, and check whether the model only references the content of this file.
- View model call logs to confirm that the request address and token configuration are correct, and there are no connection failure records.
- Submit multiple identical task requests, and check the consistency of the format and fields of the generated content to confirm that it meets the preset rigor requirements.
The provided values are common starting points, and should be assessed against relevant 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.