What the data for this category looks like
The operational data for information feed ad creative testing typically comes from advertising platform interfaces, internal material libraries, user interaction data and conversion data. The data update cycle is daily or weekly, to match daily operation summary needs. Daily associated documents include creative scripts, material test sheets, delivery data reports and iteration checklists. Core fields include creative identifiers, material formats, impression volume, conversion counts, test cycles and delivery account identifiers. No additional custom statistical units are used.
What constraints these characteristics impose on HTTP interfaces and external systems
Multiple data sources require interfaces to support multi-platform authentication and field pulling. This avoids redundant operations for cross-system data integration. Daily or weekly update cycles require interface call frequencies to match the business cycle. Calls must not trigger platform rate limits too frequently, nor delay data so much that reports become outdated. Summarization needs for multiple document types require interfaces to support data filtering by specified conditions. This ensures pulled content aligns with the fields in test sheets and iteration checklists. Errors in creative data directly affect delivery effectiveness. Interface failure handling mechanisms must balance stability and data accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {ad platform API key} | Advertising platform interfaces typically use Bearer tokens for authentication, which matches the data source requirements for creative testing |
variables | ["ad_account_id", "creative_type", "test_period"] | Matches the core fields of material test sheets, making it easy to accurately pull operational data for corresponding creatives |
responseData | Top 20 results | Information feed creative testing single batches typically cover a limited volume of materials. 20 results meet conventional summarization needs |
| Interface call timeout | 30 seconds | Ad platform interface responses typically fall within the 10-20 second range. 30 seconds covers most normal requests |
| Batch pull interval | Every 6 hours | Operational reports are typically updated daily or weekly. A 6-hour interval balances data timeliness and interface call costs |
| Failure retry count | 2 retries | Avoids triggering ad platform rate limits from repeated requests, and meets control requirements for error consequences |
Three common errors
- Symptom: HTTP module requests return a 401 Unauthorized error. Cause: The
Authorizationparameter is not used to carry the correct ad platform API key, or the key has not been granted permission to pull creative data. - Symptom: The
responseDatafield is empty after calling the interface. Cause: Thevariablesparameter is not included in the request to specify query conditions, or the specified fields are not supported by the ad platform interface, resulting in an empty data set being returned. - Symptom: The process terminates immediately after an interface call times out. Cause: No reasonable retry mechanism is configured, and occasional jitter in ad platform interfaces is ignored. This may lead to missing operational report data.
How to confirm configurations are set correctly
- Call the FastGPT v4.17.0 test interface, check if the returned results include the fields specified by the
variablesparameter, to confirm the data pull range is correct. - View the interface call logs, confirm that the
Authorizationparameter carries authentication information correctly, with no format errors. - Simulate a single timed-out request, confirm the system executes retry operations according to the configuration, and does not directly throw unhandled exceptions.
- Compare the original ad platform data exported manually with the
responseDatacontent pulled by the interface, confirm data consistency meets business expectations.
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.