What the Data for This Category Looks Like
This category of data includes documents such as on-site inspection records, sampling records, monitoring reports, decisions ordering correction, and administrative penalty decisions. Data sources include online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. The data update frequency is daily. Each individual document contains fields such as on-site operating condition records, monitoring values, law enforcement process nodes, and rectification requirements. Some documents also include both structured quantitative monitoring data and unstructured on-site description content.
Constraints Imposed on Model Access and Configuration
Support multi-format data input for model access to handle diverse sources including structured monitoring values and unstructured on-site descriptions. Synchronize the latest monitoring results to model context in a timely manner for daily updated data from online monitoring and law enforcement systems. Equip the model with structured information extraction and correlation analysis capabilities to support comparison of on-site operating conditions and online data. Limit randomness in model-generated content to meet the rigor requirements of law enforcement documents, and avoid expressions that violate law enforcement specifications. Configure reasonable context and output restrictions to prevent serious regulatory loopholes caused by data errors, ensuring response accuracy and compliance.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate daily updated multi-source monitoring data and law enforcement documents, to avoid context overflow causing loss of key information |
maxTokens | 2000–3000 token | Law enforcement-related responses must include clear law enforcement basis and processing steps, control output length to avoid redundant statements |
temperature | 0.1–0.3 | Law enforcement responses require rigor and accuracy, reduce random generation probability to avoid content that does not comply with regulatory specifications |
AIPROXY_API_ENDPOINT | Fill in the internal gateway address for privately deployed FastGPT | Adapt to the access paths of internal online monitoring and law enforcement systems, ensure stability of multi-source data synchronization |
AIPROXY_API_TOKEN | Use a dedicated authorization token | Isolate access permissions for different law enforcement entities, ensure the security of regulatory data and law enforcement documents |
toolChoice | auto | Automatically select whether to call tools to process structured data comparison based on query type, improve response efficiency |
Three Common Mistakes
- Symptom: The model stream response is empty, with no output content. Cause:
AIPROXY_API_ENDPOINTis not configured correctly, or the token has insufficient permissions, causing model invocation failure. - Symptom: The comparison result output by the text understanding model does not match the actual monitoring data. Cause: The
maxContextparameter is not set reasonably, causing key monitoring values to be truncated and unable to support complete comparison analysis. - Symptom: The generated law enforcement documents have formatting confusion or missing required elements. Cause: The
temperatureparameter is set too high, causing the model to generate overly random expressions that deviate from the rigor specifications of law enforcement documents.
How to Verify Proper Configuration
- Import a single sampling record and the corresponding online monitoring data, initiate a data comparison query, and check whether the comparison result output by the model covers all input fields.
- Simulate a law enforcement document generation request, verify whether the output content includes necessary law enforcement elements and has no obvious formatting deviations.
- View the model invocation logs to confirm that the
maxContextandmaxTokensparameters of each request meet the preset configuration, with no abnormal truncation. - Test access to data sources from different sources, confirm that the model can normally read and process both structured monitoring data and unstructured on-site description content.
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.