What this category of data looks like
Data for this category comes from ad platform delivery creative libraries, landing page screenshot storage, product qualification documents, and public advertising law compliance clauses. Data updates run daily or in real time, and are updated immediately when delivery creatives go live or review opinions are generated. Core daily processed documents include individual ad creative copy, landing page screenshot files, violation notes marked by reviewers, and creative modification history records. Data fields include creative unique identifiers, violation type tags, review timestamps, associated account information, and other relevant items.
What constraints do these characteristics impose on the vector models and indexes workflow
Individual data for ad creative review includes multiple content types: copy, landing page screenshots, review opinions, and more. Text-based creatives must be split into chunks of reasonable length to avoid semantic fragmentation that disrupts vector extraction. Real-time updated delivery creatives and review records require indexes to support incremental writes, avoiding performance loss from full index reconstruction. Structured violation tags in review opinions require indexes to support tag-based hybrid retrieval to improve accuracy. Compliance clauses and creative content from different sources must be aligned to a unified vector dimension to ensure consistent cross-source semantic matching.
How to set configurations
These configurations are adapted for the ad creative review scenario for FastGPT v4.17.0:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures single-paragraph semantic integrity for ad creative copy, avoids vector extraction deviations caused by excessive length |
| Overlap Length | 100–200 characters | Retains semantic connections between adjacent chunks, prevents loss of critical information across chunks |
maxToken | 8192 token | Matches the input limit of mainstream vector models, covers the complete semantics of long-copy creatives |
defaultToken | Calibrated via actual testing | Adapts to the default query context length for review scenarios, balances recall accuracy and performance |
| Similarity Threshold | 0.75–0.85 | Filters low-match violation reference content, reduces false positive risks |
| Single Search Limit | Top 10 entries | Controls the number of returns per search, avoids excessively long context that affects review efficiency |
Three common misconfigurations
- A
503status code error occurs, prompting that no available models are available in the current group. The cause is that a dedicated vector model group has not been configured for the ad creative review scenario, and the resource quota of the default group has been exhausted by other tasks. - Knowledge base capacity planning deviates from actual needs, failing to accurately match the storage requirements of existing creatives. The cause is that estimates were not calculated based on the average character count per ad creative and the total number of creatives, and only general formulas were used.
- Landing page screenshot materials cannot generate valid vectors for compliance verification. The cause is that multimodal vector processing configuration was not enabled, and vectors were only generated for text-based materials, missing the processing workflow for non-text materials.
How to confirm the configuration is applied correctly
- Import a single typical ad creative, check the vector generation progress and results, confirm that the chunk length and overlap length configurations are active.
- Initiate a compliance clause search, verify that the configuration’s impact on the number of returned results matches expectations, without relying on fixed thresholds.
- Submit a real-time updated creative for testing, confirm that the index incremental write function operates normally, with no performance anomalies triggered by full reconstruction.
- Check system logs, confirm that the
maxTokenanddefaultTokenconfiguration parameters are correctly read and applied.
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.