What Data for This Category Looks Like
Data sources for this category include ad platform creative libraries, landing page screenshots, product qualification documents, and advertising law compliance clauses. Data is updated daily or in real time. Fields cover text content of individual ad creatives, landing page URLs, historical review opinions, modification operation records, and other metrics. Individual data entries include core fields such as creative unique identifiers, violation point annotations, review status, and operation time. Common document formats are structured text, image OCR recognized text, and full compliance clause text.
What Constraints These Characteristics Impose on Context and Token
Multiple data sources require context to integrate creatives, review opinions, and compliance clauses in different formats, leading to significant fluctuations in token consumption. Real-time or daily update cycles require context to be refreshed regularly to avoid loading expired review records. Diverse document structures require reasonable chunking of long-text creatives and screenshot OCR content to prevent single chunks from exceeding model token limits. The most time-consuming tasks of copy review and landing page verification must prioritize high-relevance content for context inclusion to reduce unnecessary token usage, while strictly controlling total context length to avoid review omissions or incomplete violation point annotations caused by overflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total length of multi-source ad creatives, landing page text, and review opinions to avoid context overflow |
quoteMaxToken | 2000 token | Limits the token count for a single review opinion or modification record to prevent single entries from consuming excessive context quota |
| Context chunk length | 800–1200 characters | Matches the single-block processing length of ad creative copy and landing page OCR text, balancing token consumption and retrieval accuracy |
| Context recall count | Top 3 entries | Prioritizes loading the most relevant ad creatives and latest review opinions to reduce unnecessary token usage |
| Context refresh frequency | Real time / Every 6 hours | Aligns with the daily or real-time update rhythm of ad creative review data, ensuring context includes the latest compliance and review information |
Three Common Misconfigurations
- Phenomenon: Token statistics exceed the configured
maxContextquota during model calls, with an error indicating context length limit exceeded. Cause:quoteMaxTokenis not configured, leading to redundant historical review opinions being repeatedly included in the context. - Phenomenon: New context fields return empty, and latest review modification records cannot be loaded. Cause: Context refresh frequency is not set, so the system does not trigger pulling and loading of the latest data.
- Phenomenon: Offline model runtime only displays first-token latency, which is inconsistent with the total runtime statistics rules for online models. Cause: Model call time statistics configuration is not adjusted for the time-consuming landing page verification link in ad creative review.
How to Verify Proper Configuration
- View the context loading logs in FastGPT v4.17.0, confirm that only specified category documents such as ad creatives and review opinions are loaded, and verify that the loaded count matches the configured recall count.
- Simulate a model call for a single ad creative review, check the token consumption statistics panel, and confirm that the total token count does not exceed the
maxContextconfigured quota. - Check the detailed model call logs, confirm that the token count of single referenced content does not exceed the
quoteMaxTokenlimit. - Compare real-time updated review data, confirm that the context refresh frequency meets the configured requirements, and that the latest review opinions have been included in the context.
The values provided here are common starting points, and should be evaluated against your own specific 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.