What data for this category looks like
Data for this category primarily comes from financial systems, project management systems, contract ledgers, and business department requirements. Regularly produced documents include annual budget sheets, project initiation documents, project portfolio dashboards, and investment review reports. Fields include budget amount (unit: ten thousand yuan), project cycle (unit: month), priority level, revenue forecast, and more. Data update frequencies are mostly monthly, quarterly, or annually. The update rhythm of different documents ties to business cycles. The overall data structure is relatively fixed, but relationships between fields are complex. FastGPT v4.17.0 and later versions support workflow orchestration adapted to this type of data.
What constraints these characteristics impose on the "workflow orchestration" link
Multi-source data requires workflows to use multiple nodes to pull data from separate sources such as financial systems and project management systems. This avoids overload from single-node requests. Fixed update frequencies require workflows to prioritize scheduled trigger modes. This matches the monthly, quarterly, or annual update rhythms of the business. Complex document structures require splitting into multiple processing nodes. These nodes complete document parsing, field validation, and content generation separately. Specific field and unit requirements require configuring format validation steps in the workflow. This ensures generated documents include fields that meet category requirements and avoid format errors.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow trigger mode | Trigger on a scheduled cycle, interval of 30 days | Matches the monthly/quarterly/annual update frequency of the category |
| Variable mapping rules | Associate variable fields from data sources such as financial systems and project management systems | Corresponds to the multi-source data characteristics of the category |
| Node timeout setting | 600 seconds | Adapts to the time consumption requirements of complex document processing |
| Result validation rules | Validate the field format and units of budget amount and project cycle | Matches the specific field and unit requirements of the category |
| Workflow retry configuration | Retry 2 times after failure, interval of 120 seconds | Addresses fluctuation risks in system calls |
Three common mistakes
- The code node returns an AxiosError: connect ECONNREFUSED. Troubleshooting steps: Verify that the App/Admin server has correctly configured the
AIPROXY_API_ENDPOINT(root address of the AI Proxy service, format example: http://aiproxy:3000, do not append paths such as /v1 or /chat/completions, this is not a single-model address for VLLM/Ollama/Xinference, etc.) and non-emptyAIPROXY_API_TOKEN(AI Proxy administrator token, consistent with the AI Proxy server configuration) environment variables, and that these are not confused with application API keys, model vendor keys, etc. - Calling a specified workflow returns a 400 Bad Request when the required
variablesornodeIdparameters for the workflow are not correctly passed. - The locally deployed workflow configuration interface experiences text input lag when there are many nodes. This occurs because the rendering logic of page nodes is not optimized, leading to performance pressure from too many DOM nodes.
How to confirm the configuration is complete
- Verify that the App/Admin server environment variables are configured with
AIPROXY_API_ENDPOINT(root address of the AI Proxy service, format example: http://aiproxy:3000, do not append paths such as /v1 or /chat/completions, this is not a single-model address for VLLM/Ollama/Xinference, etc.) and non-emptyAIPROXY_API_TOKEN(AI Proxy administrator token, consistent with the AI Proxy server configuration), and that these are not confused with application API keys, model vendor keys, etc. - Trigger a test workflow and confirm that node logs successfully pull corresponding data from sources such as financial systems and project management systems.
- Confirm that documents generated by the workflow include fields such as budget amount and project cycle that meet category requirements, and that formats conform to unit specifications.
- Test the scheduled trigger function to confirm that the workflow automatically executes according to the set 30-day cycle.
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.