What this category of data looks like
Data for this category comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases of market supervision, ecological environment, transportation, and other law enforcement agencies. The update frequency is daily. Daily documents include on-site inspection records, interrogation records, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and service receipts. Data fields cover illegal subjects, illegal facts, discretion benchmarks, legal provisions, penalty decisions, and other items. No specially customized unit fields are included.
What constraints these characteristics impose on model access and configuration
Dispersed data from multiple sources requires model access to support parsing of multiple document formats, to avoid loss of records and decision content due to format incompatibility. Daily updated business data requires configuration adapted to high-frequency synchronization requirements, to ensure real-time performance of model calls. A single case composed of multiple documents requires sufficient context carrying capacity, to avoid truncation of key information. The standardization of law enforcement documents requires limiting the randomness of model output, while ensuring the accuracy of structured extraction, to reduce the risk of penalty revocation and unqualified case review.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple law enforcement documents contained in a single case, to avoid loss of key information caused by context overflow |
maxTokens | 2000–3000 token | Covers structured extraction and content generation requirements for long documents such as penalty decisions and interrogation records |
temperature | 0.2–0.4 | Reduces the randomness of model output, to ensure the standardization and content accuracy of law enforcement document generation |
toolChoice | auto | Enables tool calls as needed, to complete structured field extraction for on-site inspection records and evidence lists |
AIPROXY_API_ENDPOINT | Fill in the compliant address provided by the deployment service provider | Adapts to model call link requirements for domestic law enforcement scenarios |
AIPROXY_API_TOKEN | Exclusive key bound to the deployment service provider | Ensures identity legitimacy and link security for model calls |
Three common misconfigurations
- Symptom: The model return result does not include relevant information from the uploaded document, but complete document content exists within the
FilesContenttag. Cause: ThemaxContextparameter is not configured correctly, causing the uploaded document to be truncated or not included in the model context scope. - Symptom: Only the model name can be set, and parameters such as
maxTokensandtemperaturecannot be adjusted. Cause: Only the model identifier was filled in during the model access link, and the corresponding configuration items were not bound, resulting in parameters not taking effect. - Symptom: After tool call is enabled, the model does not output the pre-thinking process, and only returns the tool call result. Cause: The tool call configuration and the thinking process output parameter are not enabled simultaneously, causing the reasoning link to omit intermediate steps.
How to confirm the configuration is correct
- Upload a single law enforcement record document, trigger the content extraction task, and check whether the model return result includes the core information in the document.
- Adjust the
temperatureparameter value, observe the change trend of the model generated content, and verify whether the parameter takes effect. - After configuring tool calls, trigger the structured extraction task for the evidence list, and check whether the tool is correctly called to complete field extraction.
- Test the model call link, verify connectivity by checking the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN.
The values provided here are common starting points, and should be evaluated 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.