What the data for this category looks like
The core data for this category comes from social insurance management systems, personal benefit records, labor relations and work injury identification systems, and the electronic social security card platform. The update frequency is daily. Daily processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Data fields include identity identifiers, payment periods, identification results, benefit application types, and more. Some documents require matching cross-system associated fields. A single file may contain multiple associated materials, with no unified single-document length limit.
Constraints on model integration and configuration
Multiple data sources require the model to adapt to field formats from different systems, and configure integration logic that aligns cross-system data. Daily update frequency requires the model context to support fully refreshed daily file data, to avoid using expired information. Multiple document structures require the model to recognize different document fields, and configure input rules adapted to multiple documents. The core review and calculation link processes long-text files, so sufficient context space must be reserved. The serious potential consequences of errors require model outputs to be traceable, so configure parameters that record intermediate reasoning steps.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate spliced content from multiple associated files, and support complete loading of cross-system data |
maxTokens | 2000–3000 token | Meets the output length requirements for a single review result, covering complete content such as eligibility judgment and benefit calculation explanations |
quoteMaxToken | 500–800 token | Accurately extracts fragments of key supporting materials from files, avoiding redundant content interfering with review logic |
temperature | 0.1–0.3 | Ensures stability of review results, preventing random fluctuations that cause deviations in benefit calculations |
AIPROXY_API_ENDPOINT | Fill in the exclusive proxy address deployed for the service | Adapts to interface forwarding requirements for multi-system docking, and ensures the security of social insurance data transmission |
AIPROXY_API_TOKEN | Fill in the access token bound to the proxy service | Restricts unauthorized access, complying with security management specifications for social insurance data |
Three common configuration mistakes
- Phenomenon: The model's review result does not include chain-of-thought reasoning content, and no intermediate step records appear in the call logs. Reason: The model's chain-of-thought output configuration is not enabled, or the selected model does not support returning reasoning process data.
- Phenomenon: A 403 status code is returned when calling the proxy interface, and normal model responses cannot be obtained. Reason: The
AIPROXY_API_TOKENconfiguration is incorrect, or the proxy service has not authorized the current access IP address. - Phenomenon: The model cannot be called normally after configuring the proxy address, with a service not found prompt. Reason: The access address formats of OneAPI and AIPROXY are mixed, and
AIPROXY_API_ENDPOINTis not filled in accordance with the specifications of the target proxy.
How to confirm correct configuration
- Upload a single work injury identification material and unemployment insurance benefit application material, trigger model review, and check whether the returned result includes a matching description of the key fields of the material.
- View the model call logs in FastGPT v4.17.0, confirm that the request header for
AIPROXY_API_TOKENis correctly included, and no 401 or 403 status codes are returned. - Adjust the
temperatureparameter to 0.2, call the same file three times, and check whether the results remain consistent without random deviations. - Switch the configured multi-channel models, confirm that all configured models can load normally and return review results.
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.