What the Data for This Use Case Looks Like
Data for administrative penalty case handling comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. The update frequency is daily. Daily documents include on-site inspection records, interrogation records, evidence lists, prior administrative penalty notification letters, penalty decisions, and more. Each individual case includes fields such as law enforcement subject, illegal facts, penalty basis, document number, and law enforcement date. Image data uses binary format. Most structured fields are string or date types.
Constraints Imposed by These Characteristics on Model Access and Configuration
Data sources include multimodal images and structured database content. Model access must support both text and multimodal parsing. Daily updated law enforcement data requires a real-time synchronization trigger mechanism. Diverse case document structures require the model to automatically recognize field rules for different documents. Error consequences in penalty cases directly affect administrative legality. A result verification link must be configured to ensure generated ledgers and reports comply with law enforcement norms.
Configuration Specifications
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total content of a single batch of administrative penalty cases is large, and needs to accommodate multiple documents and image metadata |
maxTokens | 2000–3000 token | The output length of ledger summary and report generation needs to cover core law enforcement information to avoid truncation |
vision | Enabled | Need to parse multimodal data such as law enforcement recorder footage to extract fixed evidence content |
temperature | 0.1–0.3 | Need to maintain stability of penalty basis matching and reduce random deviations |
AIPROXY_API_ENDPOINT | Fill in the exclusive access address of the corresponding vendor | Support cross-vendor model calls and adapt to the selection needs of different law enforcement agencies |
AIPROXY_API_TOKEN | Fill in the valid key obtained from the model vendor | Complete security verification for model calls and prevent access from being blocked |
Three Common Misconfigurations
- Garbled or invalid content is returned after calling the model. The cause is that the
visionparameter is not enabled, making it impossible to parse multimodal data such as law enforcement recorder footage. - The dedicated model vendor option is not present in the channel configuration list. The cause is that
AIPROXY_API_ENDPOINTis not correctly filled in as the vendor's exclusive access address. - No image analysis result is returned after calling the multimodal model. The cause is that the
maxContextparameter is set too small to accommodate the encoded context content of the image.
How to Verify Successful Configuration
- Upload a single law enforcement recorder footage, trigger a model call, and check whether the returned result includes factual descriptions related to the footage.
- View the model call log to confirm that the
AIPROXY_API_TOKENverification status is passed. - Enter a query containing multiple cases, and check whether the returned ledger summary result covers the core fields of all input documents.
- Adjust the
temperatureparameter to different ranges, compare the consistency of the two output results, and confirm that the stability meets the requirements.
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.