What the data for this category looks like
Data for this category comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, evaluation agency reports and financial statements. The update frequency is monthly. Daily processed documents include property registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval forms. Core fields cover asset ownership information, evaluation benchmark values, transaction approval status, transaction amount, and more. The character count of single documents varies widely. Some evaluation reports include multi-page attachment data.
What constraints these characteristics impose on the workflow orchestration link
Decentralized data sources require workflows to adapt to multi-system interface calls. variables must be used to pass access parameters for different systems. Diverse document types and wide character count ranges require configuring nodes adapted to different document structures during orchestration. Monthly updated data requires the workflow's knowledge base synchronization cycle to align with the update rhythm, to avoid using expired data. High rigor requirements for asset ownership and transaction data require adding pre-check links in the workflow to ensure input fields are compliant and prevent adverse consequences from data errors.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Configure a number of variables corresponding to the number of connected systems, with each variable bound to system interface parameters | Adapt to the access requirements of multi-system data sources, ensuring accurate cross-system data transfer |
| Knowledge base selection scope | Bind the exclusive property rights and asset knowledge base for this category | Match the document types and data sources of this category, avoiding interference from irrelevant knowledge bases on results |
| Node concurrency limit | 4–8 concurrency/node | Calibrated based on single-node hardware configuration through actual testing, preventing service interruptions caused by resource exhaustion |
| Timeout threshold | 600 seconds | Adapt to the time consumption requirements of multi-system interface calls and long document processing, avoiding task termination mid-process |
| Document chunking rule | Adjust chunk length according to document type: 800–1000 characters for property registration forms, 1200–1500 characters for evaluation reports | Match the structure and character count ranges of different documents, improving retrieval and recognition accuracy |
Three common mistakes
- Phenomenon: The workflow fails to retrieve URL parameters of the current page after startup, and cannot pass dynamic access identifiers for state-owned assets systems. Cause: The workflow start node is not configured with
variablesbound to URL parameter variables, and the automatic parameter transfer function is not enabled. - Phenomenon: Service crashes occur after a single node runs beyond the preset concurrency limit, and the interface displays an insufficient resources error. Cause: The concurrency limit is not reasonably set based on the single-node hardware configuration, and concurrency current limiting protection is not enabled.
- Phenomenon: After the image recognition node is triggered, the subsequent AI processing flow does not start automatically, and manual triggering of subsequent steps is required. Cause:
nodeIdis not configured to bind the output of the image recognition node to the next node, and the automatic flow switch is not enabled.
How to confirm the configuration is correct
- Launch a simulated workflow, pass standard property registration form data, and verify whether the system access parameters bound to
variablesare correctly transferred. - Gradually increase the node concurrency load, confirm that the service runs stably without abnormal errors.
- Select multiple target knowledge bases, initiate a retrieval request, and confirm that only documents related to this category are returned.
- Trigger the image recognition node, confirm that subsequent processing nodes automatically flow without manual intervention.
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 2026-09-28.