This page is written based on FastGPT v4.17.0.
What This Category of Data Looks Like
Data for this category mainly comes from object storage, data warehouses, annotation platforms, and customer acceptance feedback. Update frequency aligns with each batch delivery or version release. Daily generated documents include dataset descriptions, version records, delivery lists, and acceptance reports. Common data fields include version number, sample difference description, delivery package list, and acceptance results. There is no fixed unified unit, and values are adjusted to fit different delivery scenarios.
Constraints on the Workflow Orchestration Link
Decentralized data sources require workflows to connect multiple types of system interfaces, and adapt to call rules of different storage and platforms. Unfixed update frequency requires workflows to support on-demand or scheduled triggering to avoid redundant execution. Structural differences across multiple types of documents require workflows to include built-in field mapping rules to unify information extraction logic for different documents. Requirements for verifying version numbers and delivery completeness require adding verification nodes in workflows to ensure delivery packages match records. Access to customer acceptance feedback requires supporting asynchronous result reception and synchronization to version records to ensure data traceability.
How to Configure
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
variables | Populate version number, delivery package path under 1000 MB, and acceptance status as global parameters | Used to pass core batch information across nodes, adapt to multi-version delivery scenarios |
nodeId | Bind to data verification nodes and version record nodes | Ensure compliance of execution order across workflow links, avoid node conflicts |
| Global variable assignment rules | Extract fields from object storage metadata and acceptance reports | Automatically populate fields such as version number and sample count, reduce manual operations |
| Multi-document processing configuration | Configure extraction rules separately for PDF and CSV formats, process document fragments no longer than 500 characters each time | Adapt to different types of delivery documents, ensure accuracy of information extraction |
| Result push trigger condition | Trigger after delivery completeness verification passes | Ensure delivery instructions and operation reports are only pushed after verification passes, avoid invalid pushes |
Three Common Mistakes
- Phenomenon: The
variablesparameter in the workflow is not correctly bound, resulting in empty global variables and missing version numbers in delivery instructions. Cause: No parameter mapping is configured in the workflow entry node, and external incoming information such as version numbers is not synchronized tovariables. - Phenomenon: When delivering multiple documents, only information from some documents is extracted, and the number of results does not match the actual number of deliveries. Cause: No independent extraction rules are configured for documents of different formats, resulting in some document formats not being recognized normally.
- Phenomenon: After the workflow is executed, the operation report is not displayed in the AI dialog box as expected. Cause: The result display node is not bound to the conversation output node, and the trigger logic for pushing results to the conversation interface is not configured.
How to Confirm Configuration Is Correct
- Enter the workflow parameter configuration page, check whether the
variablesparameter is bound to the required global variables, and confirm that the parameter mapping relationship is correct. - Upload multiple test delivery documents, trigger workflow execution, and check whether information from all documents is correctly extracted.
- Execute a complete workflow process, confirm that delivery instructions and operation reports are generated as expected and pushed to the specified nodes.
- View the workflow execution log, confirm that the execution status of each node is successful, and there are no error messages.
The values given 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.