What data for this category looks like
Data for this category primarily comes from advertising placement platforms, material libraries, user interaction data, and conversion data. Most updates run daily or weekly. Common documents include creative scripts, material test sheets, placement data reports, and iteration checklists. Core processing objects include test data for different creative combination versions, placement performance records, and optimization directions. Typical data fields include creative material type, placement time slot, click volume, conversion cost, and others. No fixed unified standardized field format exists. Teams adjust based on individual test requirements.
Constraints on context and token workflows
Multiple scattered data sources require integrating information from advertising platforms, material libraries, user interactions, and other categories. This integration can easily exceed the carrying limit of maxContext. Daily or weekly updates cause historical test data to accumulate over time. Single-round summary context length continues to grow. Enforce strict token usage limits to avoid processing timeouts. Flexible daily document structures, long-text creative scripts, and multi-line material test sheets add extra token consumption. Screen valid information targeted to avoid redundant content occupying excessive tokens. Context accuracy directly affects the accuracy of test conclusion summaries. Balance information completeness and token usage efficiency.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Summary data for information feed ad creative testing usually includes multiple test record sets and historical iteration content. This range covers conventional summary needs and avoids exceeding token limits |
quoteMaxToken | 2000–3000 token | Prioritize core placement data and creative script content. Limiting quoted tokens avoids context overload |
| Similarity threshold | 0.5–0.7 | Filter low-relevance historical test records. This reduces invalid token usage while retaining sufficient reference information |
| Single search limit | Top 10 entries | Historical feed test records are usually sorted by placement effect. Retaining the top 10 entries covers main optimization directions and avoids excessive token consumption from too many entries |
| Context splicing rule | Sorted by placement time slot from newest to oldest | Latest test data provides more reference value for current optimization. Prioritizing it reduces token consumption from redundant information |
Three common configuration mistakes
- Phenomenon: A context token count exceeded error pops up during runtime, and the task terminates. Cause: No reasonable
maxContextvalue is set. Teams include full historical creative scripts and placement data in the context, causing token usage to exceed platform limits. - Phenomenon: Task execution times out, and no summary results are returned. Cause:
quoteMaxTokenis set too large, and teams do not filter valid data by placement effect. The context splicing length exceeds processing time limits. - Phenomenon: Referenced historical test data is irrelevant to current creative testing, and valid optimization conclusions cannot be drawn. Cause: The similarity threshold is set too low. Teams introduce large amounts of low-relevance redundant data. This occupies additional tokens and interferes with core information judgment.
How to confirm configurations are set correctly
- Upload 1 to 2 sets of typical creative test data and historical summary documents. Manually check the token length after context splicing. Confirm the length does not exceed the preset
maxContextvalue. - Run a small test task. Check whether the task execution duration meets expectations. Confirm no timeout occurs.
- Randomly select one historical test data entry. Verify whether the system references content relevant to current test requirements. Confirm the similarity threshold is set reasonably.
- View the token consumption statistics panel in FastGPT v4.17.0. Confirm that each summary task’s token usage stays within the preset range.
The values provided here are common starting points. They should be evaluated against the relevant deployment’s own test 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.