What the Data for This Category Looks Like
The data for this category mainly comes from advertising platform APIs, attribution analysis systems, CRM customer management systems, and behavioral data collected via landing page embedded tracking. Data update cadence falls into two categories: daily and real-time. This corresponds to regularly produced daily and weekly reports, price adjustment records, material test sheets, optimization review documents, and other materials. Data fields include impressions, clicks, conversion cost, budget balance, and more. Units include yuan, times, cost per thousand impressions, and more. Field formats vary slightly across different advertising channels.
Constraints Imposed on Workflow Orchestration by These Characteristics
Multi-source heterogeneous data sources require the workflow to configure multiple nodes to adapt to different data pulling and cleaning formats, to avoid parsing failures caused by field format differences. The dual update cadence of data requires the workflow to support two trigger modes: real-time data nodes must be bound to short-interval polling, while daily report nodes can be configured with scheduled triggers. The non-standard structure of daily documents requires the field mapping node in the workflow to support custom matching rules, adapting to document formats exported by different channels. The time-consuming abnormal indicator locating link can automatically trigger the indicator anomaly detection node via the workflow, reducing repetitive manual troubleshooting operations.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Define advertising data field mapping rules, including fields such as impressions, conversion cost | Adapt to format differences of multi-source heterogeneous data, unify workflow input formats |
| Node connection rules | Left side is data input node, right side is result output node, bottom side is branch judgment node | Match standard connection logic of workflow nodes, avoid confusion in connection point usage |
| Single pull data limit | Top 1000 records | Adapt to single-call restrictions of advertising platform APIs, avoid triggering rate limits |
| Anomaly trigger threshold | Conversion cost exceeds preset benchmark interval (calibrated via actual testing) | Automatically trigger change review, corresponding to the most time-consuming abnormal indicator locating link |
| Scheduled task interval | 600 seconds for real-time data nodes, 86400 seconds for daily report nodes | Match the real-time and daily data update cadences of this category |
nodeId | Assign a unique identifier to each advertising channel node | Facilitate workflow debugging and node status tracking |
Three Common Mistakes
- Phenomenon: The workflow does not automatically trigger dialog prompts after startup, and can only be triggered manually. Cause: The automatic execution rules defined in
variablesare not bound to the trigger node, only manual startup parameters are configured. - Phenomenon: Confusing right and bottom connection points during node connection, leading to incorrect process execution logic. Cause: The connection rule that the right side is for serial result output and the bottom side is for conditional branch judgment is not clarified, and branch logic is incorrectly connected to the right connection point.
- Phenomenon: After local deployment, the workflow code node prompts that there is no running result after execution. Cause: The storage path of node output is not configured in
variables, or permission restrictions of the deployment environment prevent the generation of execution logs.
How to Confirm the Configuration is Correct
- Trigger the update event of the corresponding advertising data, check whether the workflow automatically starts execution.
- Manually connect different types of nodes, confirm that the right connection point only supports serial processes, and the bottom connection point supports branch judgment.
- Simulate the scenario where the knowledge base is empty, check whether the workflow skips the large model search step.
- View the node execution log, confirm that the code node has normal output content.
The values provided are common starting points and should be evaluated 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.