What the data for this category looks like
Typical stakeholders for this category include game publishing compliance teams, content auditors, and game producers. Data primarily comes from game text, story scripts, and art resources during game development and publishing phases. It also includes daily content self-checklists, sensitive content modification records, user agreements, compliance review minutes, and publicly available regulatory compliance requirements. Data updates align with game version iterations or event launch schedules, with one update per version or event. Document structures primarily use structured fields, including content type, compliance items, modification status, and review comments. Some documents link to art resource paths and version identifiers.
What constraints these characteristics impose on the context and token workflow
Documents for game content compliance self-check are mostly long texts. Single text lengths for items like story scripts and compliance review minutes vary widely. They are updated frequently alongside versions or events. This requires context retrieval to cover the latest sensitive content modification records and review comments. Unlike game edition number and publication declaration, this compliance self-check scenario occurs before declaration and during version updates. Context must cover frequently updated version content. Data sources include non-text fields such as art resource paths. Teams must distinguish valid compliance content from redundant information during context splicing, to avoid invalid token usage. Public regulatory requirements must be accessible for reference at any time. Context retrieval must prioritize matching core compliance item fields, while also limiting total token count to prevent exceeding the model's carrying capacity due to overly long content.
How to configure settings
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text length of game story scripts and compliance review minutes, avoiding truncation of core compliance content |
quoteMaxToken | 1500–2000 token | Limits the token upper limit for a single quoted document, preventing a single piece of content from occupying too much context space |
| Single recall upper limit | Top 6 entries | Matches the volume scale of daily documents, avoiding recalling excessive redundant historical modification records |
| Context sorting rule | Sorted by update time in descending order | Prioritizes loading the latest sensitive content modification records and compliance review comments, ensuring content timeliness |
| Total input token limit | Calibrated based on actual testing | Adapts to the carrying capacity of the deployed quantized model, avoiding token verification failures |
Three common configuration errors
- Token validation failed errors appear in the chat interface, and answers are truncated during long-text compliance queries. The cause is incorrect configuration of
maxContextorquoteMaxTokenvalues, leading to input content exceeding the model's carrying range. - A 400 <400> InternalError.Algo.InvalidParameter: Range of max_tokens sho error is received. The cause is that no reasonable interval for
maxContextorquoteMaxTokenhas been set, and the system default parameters do not match the long text content of games. - No entry for configuring maximum tokens can be found, making it impossible to adjust context space. The cause is failing to locate the corresponding configuration item in the Context and Token module of application settings, and mistaking other functional parameters for the token setting entry.
How to confirm successful configuration
- Import a complete game story script and latest compliance review minutes, and test whether the context retrieval results include the latest sensitive content modification records.
- Initiate a conversation containing a long-text compliance query, and check whether token verification failed or content truncation occurs.
- View the Context and Token module on the application configuration page, and confirm that
maxContextandquoteMaxTokenhave been filled with reasonable value ranges. - Simulate multi-round compliance review conversations, and verify that the context can continuously load the latest review comments without obvious content loss or overflow.
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 2026-10-19.