What data looks like for this use case
Data for this use case comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information public disclosure system, and departmental supervision databases. The update frequency is daily. Commonly processed documents include on-site inspection records, interrogation records, evidence lists, administrative penalty prior notice letters, hearing notice letters, penalty decisions, and delivery receipts. All are standardized law enforcement documents, with fields including illegal facts, law enforcement subjects, penalty basis, discretion standards, delivery information, and more. The data structure is tightly bound to the law enforcement workflow.
Constraints during model access and configuration
Fixed format requirements for law enforcement documents mean model output must match preset fields and structures. Non-compliant output will compromise case file compliance. Multi-source data access requires compatibility with multiple input formats, including structured case handling system data and unstructured transcribed text from law enforcement footage. Daily updated data requires a synchronization mechanism to ensure the latest supervision and enterprise credit information is available during model calls. Errors in law enforcement case files can lead to serious consequences such as penalty revocation and administrative reconsideration losses, so the model must produce highly accurate output.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the full context of a single complete law enforcement case file, avoiding loss of illegal facts and penalty basis due to context truncation |
maxTokens | 2000–3000 token | Matches the generation length of a single law enforcement document, ensuring all required fields are fully output |
temperature | 0.3–0.5 | Reduces generation randomness to ensure accuracy and format consistency of law enforcement documents |
AIPROXY_API_ENDPOINT | Configure according to the official documentation of the accessed model | Matches the standard API call path of the model to ensure requests are delivered normally |
AIPROXY_API_TOKEN | Enter a valid key assigned by the platform | Completes identity verification to avoid call failures due to authentication errors |
vision | Enabled | Supports transcription and content extraction from law enforcement recorder footage, assisting in generating documents such as on-site inspection records that rely on footage information |
Three common configuration mistakes
- An
invalid configuration parameter name "hnsw.max_scan_tuple"error occurs when calling the knowledge base. This is caused by configuring non-standard vector database parameters without using the parameter naming rules compatible with FastGPT. - No thought process is returned after enabling output thinking for the qwen-plus model. This is caused by failing to correctly configure the model's thought output trigger parameters, or the preset output format not matching the model's requirements.
- The model list cannot be obtained after accessing oneapi. This is caused by using
/v1/model/listas the API endpoint; the correct interface path is/v1/models.
How to verify a correct configuration
- Initiate a single law enforcement document drafting test, and check whether the output includes the required document structure and mandatory fields such as on-site inspection records and penalty decisions.
- Review the FastGPT v4.17.0 API call log to confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have no format errors, and that no 401 or 404 level errors occur. - Test the knowledge base recall function to confirm that the match between the recalled national enterprise credit information public disclosure system data and the illegal facts in the law enforcement case handling system meets requirements.
- Adjust the
temperatureparameter to the 0.3–0.5 range, and check whether the generated law enforcement documents maintain consistent formatting and contain no redundant content.
The values provided here 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.