What the data for this category looks like
Data for this category comes from the national integrated government service platform item database, administrative licensing item lists, departmental business approval systems, and electronic certificate repositories. The update frequency is daily. Documents processed daily include service guides, application forms, commitment letters, acceptance notices, correction notification letters, administrative licensing approval decisions, and licenses. The data covers structured item fields and unstructured document content. The data structure updates synchronously with adjustments to legal items, and only includes standardized fields specified by official authorities.
What constraints do these characteristics impose on the model access and configuration link
Multi-source, scattered data with daily updates in administrative licensing scenarios require model access configuration to support context stitching of multi-source documents and incremental index updates. Strict formats and official terminology of legal documents require limiting the randomness of generated content to avoid non-legal expressions. Cross-department collaborative business logic requires configuration to support tool calls to retrieve external data such as electronic certificates. Legal time limits for business handling require controlling the response time of model generation and retrieval to ensure the process complies with legal nodes.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Administrative licensing scenario business documents include multi-source content such as item lists, service guides, application materials, requiring sufficiently long context to match complete handling logic |
temperature | 0.1–0.3 | Must strictly match legal terminology and handling requirements, reduce generation randomness to avoid incorrect guidance |
maxTokens | 2000–3000 characters | Business handling guidance must be clear and concise, control output length to adapt to reading habits in government affairs scenarios |
quoteMaxToken | 6000–10000 token | Must fully reference official service guides and item list content to ensure accuracy of guidance basis |
toolChoice | Auto | Must support tool calls such as cross-department electronic certificate retrieval to adapt to cross-entity collaboration needs of administrative licensing |
AIPROXY_API_ENDPOINT | Fill in according to the proxy address of government-compliant large models | Adapt to data security and compliance requirements of government affairs scenarios |
Three common mistakes
- Phenomenon: Non-legal terminology appears in handling guidance content generated by large model calls. Cause: The
temperatureparameter is set too high, generation randomness is not limited, and official government terminology requirements are not clearly specified in the system prompt. - Phenomenon: Knowledge base retrieved documents do not match the current handling item. Cause: The
maxContextparameter is not configured correctly, resulting in the context window being insufficient to cover the complete matching conditions of the item list, or the data source is not associated with the latest administrative licensing item list. - Phenomenon: Third-party large model calls return authentication failure. Cause:
AIPROXY_API_TOKENis filled incorrectly, or the proxy address does not adapt to access restrictions of the government affairs network.
How to confirm the configuration is complete
- Input standardized administrative licensing handling inquiries, check whether the generated guidance content matches the legal process in the official service guide.
- View the knowledge base retrieval log to confirm that the returned document sources cover the configured government affairs data sources.
- Trigger an incremental synchronization task to confirm that the index update process executes normally at the daily frequency.
- Test third-party large model calls to confirm that identity verification and interface calls have no abnormalities.
The values provided 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.