What the data for this category looks like
This category’s data is generated across the full lifecycle of collection tasks. Sources include customer written requirements, public dataset APIs, outputs from self-developed collection tools, and internal sample inventory data. Four core document types are produced regularly: collection plans, sample specification sheets, authorization and compliance statements, and task scheduling tables. Data updates follow a per-project cycle, with weekly synchronization of the latest sample scope and field requirements within each project. Data fields include sample coverage, data format requirements, compliance check items, and execution node time limits. There are no unified fixed units; matching must be done according to collection targets.
What constraints these characteristics impose on workflow orchestration
As a category that determines how raw data is obtained, the exclusive data characteristics of collection task design require that workflow orchestration must cover the compliance check pre-link. Multi-source data input requires workflows to support parallel pulling across multiple nodes, to avoid task blocking caused by interruption of a single data source. Sample scope updated per project cycle requires workflows to support dynamic adjustment of node parameters via variables, without needing to repeatedly modify the overall process. Frequent confirmation needs for field specifications require workflows to include editable field check nodes, to adapt to format requirements of different customers. The compliance check link must be a pre-branch, otherwise it will trigger the risk of infringement or invalid samples.
How to configure settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
variables | Updated per project, bound to sample scope and field specification parameters | Adapts to the per-project/weekly update rhythm, no need to restructure the workflow |
nodeId | Assign a unique identifier to each compliance check and data pulling node | Facilitates locating specific links with execution anomalies |
| HTTP request timeout setting | 300–600 seconds | Adapts to the average response duration of public data source pulling, avoids interrupting valid tasks due to short timeouts |
| Loop nesting configuration | Support embedding HTTP requests within loop nodes | Adapts to the need for batch pulling of multiple batches of data sources |
| Single pull sample upper limit | 1000 entries | Adapts to the single return limit of public data sources, avoids data overload |
| Branch trigger rule | Trigger subsequent branches based on the execution result of a single node | Avoids resource conflicts caused by parallel execution of multiple branches |
Three common misconfigurations
- Symptom: An empty value is returned when calling a knowledge base assistant workflow via API. Cause: Context parameters required for knowledge base calls were not correctly bound in
variables, causing workflow nodes to fail to obtain valid input. - Symptom: A quote type error is reported when referencing configuration variables. Cause: Variable wrapping format was not used in accordance with FastGPT v4.17.0 specifications, causing parameter parsing failure.
- Symptom: The tool call node outputs both an AI reply and triggers execution of the next level branch. Cause: Branch trigger logic was not correctly configured, and dual-branch parallel execution paths were enabled by default.
How to verify correct configuration
- Manually trigger the workflow, check the execution logs of each node, confirm that the
variablesparameters have correctly loaded the bound sample scope and field rules. - Submit the variable reference configuration, verify that no quote type error is reported, confirm that the format meets FastGPT v4.17.0 requirements.
- Adjust the HTTP request timeout setting to 600 seconds, simulate a data source request with a long response duration, confirm that the task will not terminate early due to timeout.
- Configure a loop nesting node and embed an HTTP request, run a batch pulling test, confirm that the loop logic executes normally with no resource conflicts.
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.