What the data for this category looks like
The daily documents for this category include basic government service item catalogs, implementation lists, service guides, standard material catalogs, flowcharts, and frequently asked questions. Data sources are the national basic government service item catalog, administrative licensing item list, power and responsibility list, and local government service item databases, with a monthly update frequency. The document structure uses item codes as the core associated field, including standardized fields such as acceptance conditions, application materials, and handling procedures. Some documents include cross-department collaborative responsibility descriptions.
What constraints these characteristics impose on model access and configuration
Monthly updated data sources require the knowledge base sync cycle to match the update rhythm to avoid data lag. The association structure centered on item codes requires the model to accurately match the corresponding fields during retrieval, to prevent confusion across items. Multi-source, cross-level data sources require configuring verification logic to compare the consistency of item descriptions across different channels. The standardized field structure requires the content extracted by the model to strictly correspond to preset fields, to avoid generating irrelevant information outside the business scope. This category is for background rule maintenance work, and does not require handling individual applicant’s specific handling cases. Therefore, the model does not need to generate guidance content for applicants.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single government service item documents have relatively long content, so sufficient context must be retained for cross-document comparison |
maxTokens | 4000 token | The effective content length of core documents such as service guides is moderate, to avoid exceeding the model’s output limit |
quoteMaxToken | 2000 token | Sufficient original citation fragments must be retained to verify the accuracy of item fields |
temperature | 0.1–0.3 | Maintain the rigor of output content, and avoid generating uncertain statements that deviate from business rules |
AIPROXY_API_ENDPOINT | Fill in the address provided by the accessed model service provider | Complete address configuration for third-party model interfaces |
AIPROXY_API_TOKEN | Fill in the secret key provided by the accessed model service provider | Complete authentication configuration for third-party model interfaces |
Three Common Configuration Errors
- 404 status code errors occur. The cause is that
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not filled in correctly, so the third-party model interface cannot be connected and authenticated. - Missing or incomplete item fields are returned in retrieval results. The cause is that the standardized field structure of the document is not aligned, so the model cannot accurately match the preset business fields.
- Knowledge base data lags behind official updates. The cause is that an automatic sync task matching the monthly update rhythm is not configured, resulting in a discrepancy between local data and official data sources.
How to Verify Successful Configuration
- Access the FastGPT v4.17.0 management backend, navigate to the model configuration page, and verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the information provided by the service provider. - Upload a single standard service guide document, trigger the model verification task, and confirm that the returned field extraction results match the acceptance conditions, application materials and other content in the document.
- Configure the monthly automatic sync task, confirm that the task executes on time, and that the update time of the knowledge base data after synchronization matches the official data source.
- Adjust the
temperatureparameter to 0.2, test that the generated verification results remain rigorous, and contain no redundant or off-scope content.
The values provided are common starting points, and should be measured against the sample data of the respective deployment.
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.