Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports, with a daily update frequency. Daily documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. On-site inspection records contain fields such as law enforcement officer information, inspection time, and point operating conditions. Monitoring reports include fields such as sampling time, pollutant concentration, and detection methods. Law enforcement documents include information such as process nodes and responsible entities. Some monitoring data includes concentration units.
What constraints these characteristics impose on the "model access and configuration" link
High-frequency data updated daily requires a stable model call mechanism to prevent process interruptions caused by interface timeouts. The differentiated field structures of multiple document types require the model to distinguish between different document types such as on-site inspection records, monitoring reports, and law enforcement documents to avoid content confusion. Mixed input of real-time online monitoring data and offline third-party test reports requires the model to accurately associate data sources with corresponding business links. The rigor of law enforcement and penalties requires that model outputs must cite original text fragments to ensure compliance with regulatory requirements.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the dedicated endpoint assigned by the platform | Adapt to the intranet or public network call path of the ecological environment system |
AIPROXY_API_TOKEN | Bind a token with exclusive permissions | Ensure only authorized entities can call the model interface, ensuring the security of law enforcement data |
quoteMaxToken | 800–1200 token | Cover the core fragment length of a single monitoring report or law enforcement document to meet the requirement of citing original text |
maxContext | 15000–20000 token | Accommodate the context of multiple law enforcement documents and real-time monitoring data to avoid truncation of critical information |
toolChoice | auto | Allow the model to independently choose whether to call database tools, adapting to process guidance requirements for different business scenarios |
temperature | 0.1–0.3 | Reduce output randomness to ensure the rigor and consistency of law enforcement-related content |
Three common mistakes
- Phenomenon: The model output does not include original text fragments from the database, only returning summarized content. Cause: The
quoteMaxTokenparameter is not configured, or its value is too small to accommodate the reference fragment of a single document. - Phenomenon: The selectable models in the model selection interface keep jumping, and the target model cannot be fixed. Cause: The
toolChoiceparameter is not fixed, or there is a repeated update issue in the model list returned by the interface. - Phenomenon: Logical confusion occurs when calling database tools, and data fields cannot be matched correctly. Cause: The dedicated field naming rules of the ecological environment law enforcement system are not adapted, and the model cannot accurately parse and associate data.
How to confirm the configuration is complete
- Initiate a test simulating an on-site law enforcement process, check whether the model output includes original text fragments of the specified document, and verify whether the
quoteMaxTokenconfiguration covers the fragment length. - View the model call logs to confirm that there are no format errors in the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and that the interface returns a normal status code. - Adjust the
temperatureparameter and compare the output content to confirm that the rigor of the output meets the requirements of law enforcement scenarios. - Simulate a scenario with mixed multiple documents, and check whether the model can correctly distinguish between different types of law enforcement and monitoring documents.
The values provided here are common starting points and should be evaluated against the user'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.