What data for this category looks like
Data for this category is generated during the publication approval process for a single game. Core daily documents include four types: declaration request files, game application forms, copyright certificates, and review reports. Data sources include the National Press and Publication Administration’s service platform, copyright registration systems, game package files, and materials submitted by operating institutions. Each game has a separate set of materials. Updates trigger irregularly based on approval process nodes. The field structure of each material set varies significantly by document type. It includes dedicated fields such as declaration subject information, game content descriptions, and compliance review opinions.
Constraints on model access and configuration
The data characteristics of this category impose multiple constraints on model access and configuration. First, scattered data from multiple sources must adapt to different interface protocols and format parsing rules. Configure a flexible data source access link. Second, each single game uses independent approval materials. Isolate configuration by project to avoid mixing materials across different games. Third, the four core documents have distinct field structures. Adjust the model’s content extraction logic to adapt to different text structures such as application forms and copyright certificates. Additionally, the irregular update rhythm requires a quickly triggerable material re-parsing mechanism. This mechanism adapts to temporary supplementary materials during the approval process.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| maxTokens | 8000–12000 token | Adapts to the content length of a single review report or copyright certificate, avoiding truncation of critical compliance information |
| temperature | 0.1–0.3 | Reduces generation randomness, ensures consistency of compliance material verification, and meets the strict requirements of publication approval |
| quoteMaxToken | 1000–1500 token | Retains complete content of key reference segments for consistency verification across multiple materials |
| AIPROXYAPITOKEN | AI Proxy administrator token, must match the AI Proxy server configuration and cannot be empty. Must not be an application API key or model vendor key | Required environment variable for the App/Admin server. Must not be filled in workflow global variables, application API request headers, or the model management page |
| toolChoice | auto | Automatically selects whether to call tools to complete material verification, adapting to processing needs for multiple document types |
Three common configuration mistakes
- A custom channel call returns a 500 status code. The cause is only upgrading FastGPT without synchronously updating the oneapi dependency version, leading to incompatible underlying interface protocols.
- No sensitive word filtering occurs after calling an OpenAI model. The cause is not configuring the model’s content security trigger rules, only relying on the model’s basic filtering capabilities, and not adjusting the filtering logic for the game compliance scenario.
- A custom channel configuration call fails and returns an empty response field. The cause is incorrect configuration of
AIPROXY_API_ENDPOINT, with added path suffixes such as /v1 or /chat/completions, or configuration pointing to single model addresses such as VLLM/Ollama/Xinference.
How to verify successful configuration
- Upload a single game application form to trigger a model call. Check whether the returned result fully extracts all required fields with no content truncation.
- Check the App/Admin server startup logs to confirm there are no startup verification failure errors for unconfigured AIPROXYAPIENDPOINT or AIPROXYAPITOKEN.
- Adjust the temperature parameter, then call the same review report multiple times. Verify that the consistency of returned results meets expectations.
- Switch between different document types such as copyright certificates and declaration request files. Verify the model’s adaptability.
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 2026-10-19.