What the data for this category looks like
Data for advertising compliance recordkeeping originates from advertising platform backends, customer qualification documents, contract systems, and publicly available regulatory rules. Update frequency follows either single delivery campaign or monthly cycles. Daily documents include ad review forms, qualification attachments, delivery records, and rectification notices. Fields cover compliance review identifiers, delivery cycles, rectification statuses, and similar attributes. Single document sizes typically fall between 100KB and 10MB. Data association dimensions include identifiers such as delivery activity ID and customer entity name.
Constraints on workflow orchestration
Data sources for advertising compliance recordkeeping are scattered. Workflows must support pulling different types of documents across multiple systems, and multi-source data synchronization logic must be configured. Data update schedules are irregular. Workflows must support triggering based on delivery campaign nodes to match single campaign or monthly update rhythms. Diverse document types require adaptation to file parsing nodes for different formats. Clear field association dimensions require parameter binding to link activity IDs with corresponding data. Errors may lead to consequences such as lack of evidence for regulatory inspections. Failure retry and exception alert logic must be configured.
Configuration Settings
Configurations on this page are based on FastGPT v4.17.0. Specific configuration items are listed below:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind delivery activity ID, compliance review status, customer qualification library ID | Adapt to field concatenation for multi-source data, match the document association logic of advertising compliance recordkeeping |
| Trigger timing | Trigger after a single delivery campaign completes | Match the rhythm of data updates per campaign or monthly cycle |
nodeId | Assign unique identifiers to cross-system pull nodes and AI chat nodes | Facilitate troubleshooting of abnormal nodes and filtering output content from specified nodes |
| Retry count | 3 times, 60-second interval | Address temporary rate limiting scenarios from advertising platform APIs |
| Timeout threshold | 300 seconds | Adapt to the time limit for pulling data across multiple systems |
| Context length | Set based on actual measurements | Adapt to content lengths of different compliance documents |
Three Common Configuration Mistakes
- All responses from multiple AI chat nodes in the workflow are displayed in the final chat session. This occurs because the
nodeIdfilter for required output nodes is not configured, and output filtering logic is not set up. - Variable references when selecting a knowledge base fail to match the qualification library for the corresponding delivery activity. This occurs because the association between the knowledge base ID and activity ID is not bound in
variables. - The workflow cannot trigger multi-round follow-up questions after execution. This occurs because a session context persistence node is not configured, and conversation state is not retained.
How to Verify Successful Configuration
- Manually trigger the workflow following a simulated delivery campaign, and verify that pulled documents include category-specific documents such as ad review forms and delivery records.
- Review workflow node logs, and confirm that
variablesparameters are correctly bound to delivery activity ID and compliance review status fields. - Simulate an advertising platform API rate limiting scenario, and confirm that nodes execute retries per the configured retry count and interval.
- Initiate a multi-round conversation, and confirm that only responses from AI chat nodes with the specified
nodeIdare displayed.
The values provided here are common starting points, and should be evaluated against samples appropriate for the specific use case.
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.