What this category's data includes
This category of data mainly comes from the regulatory and rule repository, higher-level law database, departmental policy document repository, and administrative regulatory document filing system. Update frequency is irregular. Daily processed documents include regulatory document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Most documents are structured text, containing fields such as authority basis, article content, and opinion feedback. There is no unified fixed single-document length standard. Most content involves the boundaries of administrative power and the setting of citizens' rights and obligations.
What constraints these characteristics impose on model access and configuration
Decentralized data sources and irregular update cycles require model access to support dynamic synchronization configuration for multiple data sources. Documents contain specialized legal terminology and hierarchical basis content, so context parameters adapted for long-text processing must be configured. Strict matching of higher-level laws and authority bases is required, so recalled reference content must have high accuracy and authority. The review process requires retaining a complete chain of basis, so parameters that enable traceable reference source tracking must be configured. Document lengths vary widely across different scenarios, so flexible adjustment of context length limits must be supported.
How to set configurations
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Regulatory document drafts contain multi-segment hierarchical content, requiring sufficient context to compare higher-level laws and authority bases |
temperature | 0.1–0.3 | Legality review requires strict matching of legal articles, reducing randomness to ensure conclusion rigor |
AIPROXY_API_TOKEN | Exclusive key generated by the platform | Ensures identity verification security for model calls, preventing unauthorized access |
maxTokens | 2000–3000 token | Output of review opinion documents must fully cover legality judgment basis, avoiding truncation of key conclusions |
quoteMaxToken | 500–800 token | Accurately reference higher-level legal article content, controlling reference fragment length to ensure readability |
Three common configuration mistakes
- Phenomenon: After adding a third-party model API key, the system prompts that no available channels are found. Cause: The access group permission for the corresponding model has not been configured on the platform, or the service area bound to the key does not match the platform's default group.
- Phenomenon: No create model operation icon is displayed in the upper right corner of the backend interface. Cause: The currently logged-in account does not have model management permissions, or the FastGPT version has not been updated to v4.17.0.
- Phenomenon: Custom models perform inconsistently in conversations and workspaces. Cause:
maxContextandtemperatureparameters have not been uniformly configured across different scenarios, or the backend service for model calls has not synchronously updated the configuration.
How to confirm successful configuration
- Submit a single regulatory document draft for testing, check whether the returned results include clear references to higher-level legal basis, and verify the matching degree between the referenced content and the preset data sources.
- View the model call log records, confirm that
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINThave been correctly loaded, and there are no authentication or connection errors. - Adjust the value range of the
maxContextparameter, test input documents of different lengths, and confirm that the system can process normally without context overflow or content truncation. - Compare the consistency of review results across different configurations, confirm that the values of parameters such as
temperaturemeet the rigor requirements of the business scenario, and there are no obvious random deviations.
The values given 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.