What data falls under this category
Data for this category primarily comes from art asset libraries, project management systems, engine resource packages, and outsourced delivery packages. Daily produced documents include art requirement sheets, asset specification sheets, outsourced feedback sheets, and acceptance records. These documents contain fields such as asset unique identifier, version number, resolution, polygon count, delivery deadline, and acceptance status. Data update frequency is mostly daily or weekly, synchronized with asset iterations and outsourced progress.
Constraints on context and token handling
Art asset-related documents mostly contain professional parameters and have a high proportion of long text. They also require association with asset version information from multiple sources, which creates requirements for context window capacity. Field differences across multiple data sources demand precise matching of asset identifiers and version numbers during context recall, to avoid redundant token consumption. High-frequency updated outsourced feedback and acceptance records require controlled context recall time ranges, to prevent expired data from occupying tokens. Numeric fields such as polygon count and resolution must maintain consistent formatting during context concatenation, to avoid additional token overhead.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | Top 8 entries | Matches the number of documents associated with multiple art asset sources, avoids recalling excessive redundant data |
quoteMaxToken | 4000–6000 token | Adapts to the concatenation needs of long text such as art asset specifications and requirement sheets |
| Context recall time range | Last 30 days | Matches the update frequency of outsourced feedback and acceptance records, prevents expired data from occupying tokens |
| Retrieval matching threshold | 0.75–0.85 | Precise matching of asset unique identifiers and version numbers, reduces false recall rate |
Three common configuration errors
- The difference in token consumption between self-hosted and online versions is not clearly marked, leading to cost estimation deviations. This occurs because
quoteMaxTokenis not configured based on the actual concatenation volume of long art asset text, and token billing rules for different deployment scenarios are not distinguished. - Search results cannot associate asset version information with context, leading to low matching accuracy of review results. This occurs because context recall rules based on asset unique identifiers are not configured, and only general document content is recalled.
- Incorrect configuration of the
/fastgptcontext path for private deployments causes the application to fail to load context normally. This occurs because the access path is not set as required, or reverse proxy related configurations are not updated synchronously.
How to confirm correct configuration
- Submit test documents such as art requirement sheets and asset specification sheets via the FastGPT v4.17.0 backend configuration interface. Confirm context recall results include matching asset versions and outsourced feedback records.
- View the token consumption statistics panel. Confirm the
quoteMaxTokenconfiguration does not exceed the preset threshold, and no abnormal token waste occurs. - Access the
/fastgptpath. Confirm the privately deployed application can normally load the configuration page, and no 404-level errors are present. - Trigger the change review process. Confirm search results are associated with the current asset's context information, and no disconnected or mismatched content exists.
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.