What data for this category looks like
Data for this category is generated across the full publication declaration process for a single game. Daily documents include declaration request files, game application forms, copyright certificates, review reports, and more. Data sources include official government service platforms for press and publication administration, copyright registration archives, game package files, and qualification materials submitted by operating institutions. Data update frequency adjusts irregularly based on the declaration progress for each individual game. Each game has a separate, independent declaration data set. Fields include basic game information, qualification certificate numbers, review modification records, and more. There is no unified batch update cycle.
Constraints on context and token management
The data characteristics of this category impose multiple constraints on context and token management. Associated documents for a single game’s declaration vary widely, ranging from structured application forms to lengthy review reports. Token consumption differs significantly. Precisely control the total token count of the context window to avoid exceeding the model’s processing limits. Data updates irregularly alongside declaration progress. The context must support dynamic loading of the latest submitted modification records and qualification materials, to ensure the currently valid version is used during reviews. Each game has a separate independent data set. The context must be limited to the current declaration project, to prevent cross-game data from being mixed in and causing review errors. The declaration process requires cross-document verification of material consistency. The context must retain associated information across multiple documents, to ensure matching content can be quickly retrieved during reviews.
How to configure settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total token count of declaration documents for a single game in this category typically falls within this range. It covers core declaration materials and review records, and fits the standard model window scope of FastGPT v4.17.0 |
quoteMaxToken | 1500–2000 token | The token count of key segments from a single core qualification certificate or review report falls within this range. It meets the core needs of material consistency verification |
| Context history retention count | First 3–5 entries | Communication records for the declaration process are concentrated in the rounds prior to initial review. Excessive records will consume too many tokens, affecting the loading of core review content |
| Document chunk length | 4000–5000 characters | Documents in this category are mostly structured or semi-structured. This length ensures complete information within each chunk and reasonable token consumption |
| Maximum retained retrieval results | First 3–4 entries | Matching content from declaration materials is usually concentrated in a small number of key documents. Excessive results will exceed the carrying capacity of the context window |
Three common misconfigurations
- Phenomenon: The quoted content returned during question answering exceeds the set
quoteMaxTokenlimit, or a token overlimit error appears in logs. Cause: Retrieved document chunk content is not truncated according toquoteMaxToken, and the entire chunk is directly included in the context window. - Phenomenon: Token consumption statistics for each question answering session cannot be found in the interface or logs. Cause: The FastGPT token statistics switch is not enabled, or statistics logic is not configured for the corresponding node, making it impossible to track token usage for individual calls.
- Phenomenon: Declaration data from multiple games is loaded into the context window at the same time, leading to qualification number confusion or content mismatch during reviews. Cause: The context window is not limited to documents from the current declaration project, and no project ID is added as a context filtering condition.
How to confirm correct configuration
- Access the FastGPT knowledge base configuration page, review the configuration values for
maxContextandquoteMaxToken, and confirm they match the preset ranges. - Initiate a simulated declaration review question answering session, verify the returned context content only includes documents from the current declaration project, with no cross-project data mixed in.
- Review the question answering logs, confirm that the token consumption for each call does not exceed the preset
maxContextlimit, and that the token count of quoted content aligns with thequoteMaxTokensetting. - Upload an updated review report, initiate a question answering session again, and confirm the context loads the latest version of the document content.
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.