What the data for this category looks like
This category of data typically comes from the Dual Random, One Public regulatory platform, enterprise credit information disclosure system, industry regulatory systems, and law enforcement officer directory. The update cycle is monthly. Routine documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Core fields include the unified social credit code of regulatory targets, law enforcement officer ID numbers, inspection item entries, rectification completion status, and more. The size of individual documents ranges from tens to thousands of characters.
Constraints imposed on the model access and configuration link
Monthly updated data requires the model access link to support scheduled full data synchronization and incremental updates, to avoid regulatory task deviations caused by delayed data. Mixed input of multiple document types requires configuring parsing rules adapted to structured lists and unstructured records, to ensure accuracy of field extraction. Precise matching of regulatory targets and law enforcement officers requires retaining field-level association logic during model calls, to prevent inspection non-compliance caused by extraction deviations. Compliance requirements for inspection result announcements require model output content to strictly correspond to original document fields, without arbitrary tampering or omission of key information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Accommodates multiple monthly updated regulatory documents and law enforcement records, avoids loss of field associations due to context truncation |
maxTokens | 4000–6000 token | Covers complete inspection item and target information when generating inspection tasks or announcement content in a single round |
quoteMaxToken | 2000–3000 token | Retains complete references to key document fragments, avoids missing regulatory information due to truncation |
toolChoice | auto | Supports automatic tool calls to complete regulatory processes such as law enforcement officer matching and data announcement verification |
temperature | 0.1–0.3 | Adapts to the rigor requirements of regulatory scenarios, reduces compliance risks caused by randomized output |
Reference only; verify against the target FastGPT release and deployment documentation.
| AIPROXY_API_TOKEN | Fill in the exclusive token generated during deployment | Ensures identity verification security for model calls, prevents unauthorized access |
Three common configuration mistakes
- Symptom: After importing the inspection target list, the model cannot accurately match law enforcement officers and inspection items. Cause: The input document does not use a structured format, leading to chaotic field parsing and inability to extract precise association information.
- Symptom: When using the Qwen2.5 model deployed via Ollama, tool calls cannot be triggered, and there is no response when requesting a specified regulatory task. Cause: The
toolChoiceparameter is not correctly set toauto, or the proxy link does not have tool call permissions enabled. - Symptom: After only upgrading the FastGPT image, the original monthly data synchronization task fails. Cause: Associated components are not updated synchronously, leading to interruption of the model call chain and inability to normally pull regulatory data.
How to verify a successful configuration
- Execute a single inspection plan generation test, check whether the output content includes complete inspection targets, law enforcement officers, and item lists, with no missing fields.
- Trigger the monthly data synchronization task, check whether the vector database update log shows that all monthly documents have been successfully loaded, with no error messages.
- Initiate a tool call test, verify whether the model can automatically match law enforcement officers and inspection targets, with output results conforming to regulatory business logic.
- View the model call monitoring panel, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have no abnormalities, and the request status is normal.
The values provided 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.