This document applies to FastGPT v4.17.0
What the core data for this use case looks like
Core data for delivery effect optimization comes from ad platform backends, attribution systems, CRM lead databases, and landing page statistics tools. Data update frequency is typically daily or real-time. It includes fields such as impressions, clicks, conversions, customer acquisition cost, and budget consumption, with units of times, times, units, yuan per conversion, and yuan respectively. Regularly produced documents include daily delivery reports, weekly delivery reports, price adjustment operation records, material AB test sheets, and optimization review reports. Document structure primarily follows a timeline plus indicator details. Some reports include cross-statistical results from multiple data sources, and single reports can contain tens of thousands of data entries.
Constraints imposed on workflow orchestration
Format differences across multiple data sources require configuring unified data cleaning nodes in workflows to prevent execution failures caused by missing fields or inconsistent formats. Real-time updated indicators need trigger-based workflows, which start processes using data update events instead of manual triggering. For large batch data volumes, split tasks to avoid node timeouts. Add field compliance check steps to workflows when validating multiple document types, to ensure input data meets subsequent analysis requirements. Locating abnormal indicators requires associating log nodes across multiple data sources to enable quick problem backtracking, so workflow node orchestration must balance data flow and exception backtracking logic.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Key-value pairs containing core indicator fields and data source paths | Adapt to unified data reading requirements across multiple data sources |
nodeId | Unique identifiers corresponding to code execution nodes and log output nodes | Ensure precise invocation of workflow nodes and log positioning |
| Code execution node log output | Enable console output and specify a log storage path | Facilitate troubleshooting of code execution errors and data processing issues |
| Workflow trigger condition | Trigger based on data update events or scheduled cycles | Match the real-time or daily data update rhythm |
| Node timeout threshold | 600 seconds | Adapt to the maximum time consumption requirement for single-batch data processing |
| Model channel configuration | Select the corresponding large model based on the advertising analysis scenario | Meet the requirements of delivery effect prediction and anomaly analysis |
Three Common Misconfigurations
- No log output from code execution nodes after workflow runs. The cause is failure to configure a log output path in
variablesor enable the node log switch. - The AI model dropdown list in workflows is empty. Troubleshooting steps: Required environment variables for the App/Admin server are missing, including
AIPROXY_API_ENDPOINT(AI Proxy service root address, example: http://aiproxy:3000, no need to append paths such as /v1) andAIPROXY_API_TOKEN(AI Proxy admin token, consistent with AI Proxy server configuration and non-empty), or corresponding model channel configuration has not been added. - Workflow execution timeout during large data volume processing. The cause is failure to set a reasonable node timeout threshold and failure to split data batches, leading to excessive single-node load.
How to Verify Successful Configuration
- Check that the App/Admin server environment variables include
AIPROXY_API_ENDPOINT(AI Proxy service root address, example: http://aiproxy:3000, no need to append paths such as /v1) andAIPROXY_API_TOKEN(AI Proxy admin token, consistent with AI Proxy server configuration and non-empty), and that configured values meet requirements. - Run a test workflow, view the output logs of the code execution node, and confirm logs print normally.
- Navigate to the workflow editing page, verify that the AI model dropdown list loads available models.
- Import a small batch of test data, run the workflow, and confirm the output result matches the expected format.
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.