What the data for this category looks like
Data for this category typically comes from art asset libraries, project management systems, game engine resource directories, and outsourced delivery packages. Daily associated documents include art requirement sheets, asset specification sheets, outsourced feedback sheets, and acceptance records. Fields include unique asset identifiers, version numbers, delivery status, polygon counts, resolution, submission dates, and more. Units include pixels, triangle counts, date formats, and others.
Data update frequencies fall into two categories: daily (such as acceptance records and real-time asset status) and weekly (such as bulk outsourced delivery summaries). Single document lengths vary widely, ranging from feedback records as short as tens of characters to specification parameter descriptions as long as hundreds of characters.
What constraints these characteristics impose on the context and token workflow
Multi-source heterogeneous data sources require the context to distinguish document formats and field specifications across different systems, to avoid mixing irrelevant data that interferes with analysis. Asset data with different update frequencies requires the context pool to refresh on a set cycle, to ensure summarized publishing instructions and operation reports use the latest versions.
The wide variation in single document length causes significant fluctuations in token consumption. Short texts risk missing key information, while long texts risk exceeding the model’s context limit. Additionally, fields such as asset version numbers and unique identifiers must be included in the context association logic, to prevent content confusion across versions or assets, which would reduce the accuracy of publishing instructions and operation reports.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Top 20 context entries | Art asset-related documents are mostly independent single records. Excessive context will cause token overflow and reduce the accuracy of model responses |
quoteMaxToken | 1500–2000 token | Asset specification sheets include long parameters such as polygon counts and resolution. Setting this threshold prevents long texts from exceeding the model’s context limit |
| Context source filtering | Filter by document type | Limit association to only art requirement sheets, asset specification sheets, outsourced feedback sheets, and acceptance records; exclude data from unrelated systems |
| Context refresh cycle | Daily/weekly | Match the data update frequency to ensure summarized publishing instructions and operation reports use the latest versions |
| Context association threshold | Calibrate via actual testing | Asset association rules vary across different projects. Adjust association matching standards based on actual scenarios |
Three common misconfigurations
- Phenomenon: The
maxContextconfiguration is set to 30 context entries, but only 2 context entries are displayed in the conversation details. Cause: The multi-source context association switch is not enabled, so only document data from a single system is loaded. - Phenomenon: The AI response contains unexpected JSON format content. Cause: No context source filtering is set, and system log data unrelated to art assets is mixed in, causing the model to generate content that does not meet expected formats.
- Phenomenon: The large language model prompts that the context token count exceeds the limit. Cause: No
quoteMaxTokenthreshold is configured, and complete high-resolution asset specification documents are loaded directly, exceeding the model’s context capacity limit.
How to confirm the configuration is correct
Under FastGPT v4.17.0, the configuration effect can be checked via the following steps:
- Review the context list to confirm that only the four target document types are included: art requirement sheets, asset specification sheets, outsourced feedback sheets, and acceptance records.
- Enter the unique identifier of a specific asset to verify that the associated context only includes historical records for that asset, with no content from other unrelated assets.
- Check the token consumption prompt in the model response to confirm that frequent context token over-limit alerts do not appear. Adjust relevant thresholds based on actual scenarios.
- Adjust the
maxContextconfiguration value to verify that the number of context entries displayed in the conversation details matches the configured value.
The values provided here are common starting points and should be measured against in-house 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.