What this category of data looks like
This data category draws primarily from the national integrated government service platform item database, administrative licensing item list, departmental business approval systems, and electronic certificate databases. Updates occur daily. Daily documents include service guides, application forms, commitment letters, acceptance notices, correction notification letters, administrative licensing decision letters, and license certificates. Data fields include item codes, legal establishment basis, handling conditions, application material lists, and other items. The structure is fixed and strongly bound to legal licensing items.
Constraints on Model Access and Configuration
The daily data update requirement demands adapting incremental synchronization logic during configuration. This avoids excessive computing resource usage from full re-pulling. Multi-source, heterogeneous document types require configuring distinct chunking and parsing rules. These rules adapt to the clause-based structure of service guides and the structured fields of electronic certificates. The strong binding between data and legal licensing items requires strictly limiting the Q&A scope during model access. Responses are restricted to officially released item lists and basis, while retaining the ability to cite sources to ensure compliance.
Configuration Settings
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multiple associated documents for administrative licensing items, ensuring complete context of policy basis and application materials. This configuration is stably supported in FastGPT v4.17.0. |
maxTokens | 2000–3000 token | Controls single-round response length to avoid exceeding compliance output limits in government service scenarios. |
quoteMaxToken | 1500 token | Limits the fragment length of cited documents to ensure extracted policy basis is accurate and non-redundant. |
toolChoice | auto | Allows the model to independently determine whether to call tools to retrieve electronic certificates or approval data, adapting to cross-departmental data retrieval scenarios. |
temperature | 0.1–0.3 | Maintains response rigor and prevents generation of content inconsistent with legal basis. |
AIPROXY_API_ENDPOINT | Fill in the dedicated node according to the deployment environment | Adapts to network isolation requirements in government service scenarios to ensure data transmission security. |
AIPROXY_API_TOKEN | Key bound to the dedicated node | Used for identity verification to prevent unauthorized access. |
Common Configuration Errors
Three frequent errors occur during setup:
- A
Tool call Parser nerror is returned when calling tools. The cause is that the model's inference content is wrapped in think tags and placed in the content field, which does not conform to the FastGPT tool call parameter parsing format. - A
chat: llm—model—response-emptyerror occurs when calling the flowchart. The cause is that the model did not generate valid output content, which may stem from the context exceeding themaxTokenslimit or the data source not being loaded correctly. - Failing to limit the Q&A scope during configuration causes the model to generate content beyond the scope of administrative licensing items, leading to compliance risks.
How to Verify Successful Configuration
A single administrative licensing service guide document is uploaded to trigger a policy basis Q&A, and the response is checked for accurate citation of legal basis clauses in the document. A cross-departmental electronic certificate retrieval scenario is simulated to verify whether the toolChoice configuration allows the model to initiate a tool call request. The temperature parameter is adjusted to 0.5, and the response is checked for statements deviating from legal basis to confirm the parameter takes effect. The daily incremental synchronization task log is reviewed to confirm that the data update frequency meets the configuration requirements.
The values provided are common starting points and should be measured against relevant 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.