What the data for this use case looks like
Core data for this use case comes from regulatory and rule databases, higher-level law databases, departmental policy document repositories, and administrative regulatory document filing systems. Daily processed documents include regulatory document drafts, drafting explanations, legality review opinions, summaries of solicited comments, and filing reports. Data updates have no fixed cycle, and are added gradually as policy development and filing processes progress. Data processing entities include judicial administrative departments, departmental legal affairs divisions, and policy and legal positions in government offices. Documents are primarily structured policy texts, with some accompanied by attachment explanations.
Constraints on Model Integration and Configuration
Decentralized data sources and non-fixed update cycles require the model integration process to support batch pulling and incremental synchronization of multi-source data. Diverse document types and large differences in length require adaptation to text processing needs for different lengths. The core review step requires comparison with higher-level laws and authority basis, which requires the model to have a sufficiently large context window to fully load associated regulatory documents. Review results directly affect policy release and implementation, which requires high rigor in model output to avoid clause matching errors.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Regulatory documents and associated reference documents are typically lengthy, requiring full loading of all content relevant to the review |
temperature | 0.1–0.3 | Review work requires strict adherence to standards; a low temperature parameter reduces randomness in generated content to avoid deviations |
toolChoice | Force invocation of regulatory comparison tool | The core requirement of this use case is legality review; forced tool invocation ensures coverage of comparison processes for formulation authority, release procedures, and clause legality |
AIPROXY_API_TOKEN | Exclusive token assigned by the platform | Ensures compliance of request authentication and prevents access permission errors |
AIPROXY_API_ENDPOINT | Regional node address specified by the platform | Optimizes request latency and improves review processing speed |
maxTokens | 2000–3000 token | Controls the output length of review opinions to ensure content focuses on core review points |
Three Common Configuration Errors
- Returned review results do not cover the higher-level law comparison step, or omit authority review content. This occurs when the
toolChoiceparameter is not configured correctly, and the model is not forced to invoke the regulatory comparison tool. As a result, the model generates content solely based on its internal knowledge base, without linking to actual regulatory references. - Obvious clause matching errors appear in review results, or generated content fails to meet rigor requirements. The cause is that the
temperatureparameter is set too high, which increases randomness in model-generated content and prevents accurate review conclusions. - Authentication errors are triggered after sending a request, or the API node cannot be connected normally. The cause is incorrect configuration of
AIPROXY_API_TOKEN, using an invalid or expired token, or failing to fill in the correctAIPROXY_API_ENDPOINTaddress.
How to Confirm Successful Configuration
- Upload a standard-format regulatory document draft, and check whether the model's returned review opinion covers the three core review dimensions: formulation authority, release procedures, and clause legality.
- View the FastGPT v4.17.0 request log to confirm that the
maxContextparameter matches the total token count of the uploaded document and associated reference documents. - Adjust the
temperatureparameter to 0.2 and 0.4, compare the rigor of the two review results, and verify that the impact of the parameter on output content matches expectations. - Test sending a test request using the
AIPROXY_API_TOKENto confirm there are no authentication errors or connection abnormalities.
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.