What the data for this category looks like
Data sources include state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, valuation institution reports and financial statements. The update frequency is monthly. Routine documents include property rights registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, asset disposal approval documents. There are both structured fields such as ownership number, original asset value, evaluation value, and unstructured PDF reports and image-based supporting materials. The data structure includes multi-level asset details and approval process nodes.
What constraints these characteristics impose on the "workflow orchestration" link
Pulling data from multiple sources requires the workflow to configure multiple sets of data variables to avoid cross-system interface conflicts. The monthly update rhythm requires binding timed trigger nodes to ensure the periodicity of data synchronization and summary. Ledger data and unstructured documents from large nodes can easily exceed the AI context length. Implement split processing or configure an appropriate context limit. Importing image-based supporting materials requires an additional format conversion step, otherwise the AI model cannot directly recognize them. Process errors may cause serious consequences. Configure failure retry and audit log nodes.
How to configure the workflow
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Configure multiple system data variable groups such as state-owned assets supervision and asset management | Adapt to multi-source data pulling requirements and avoid single-variable conflicts |
| Chunk Length | 800–1200 characters | Split large-node ledger data to avoid AI context over-limit errors |
| Maximum Context | 16000 token | Match the context capacity limit of mainstream AI models and adapt to the content volume of ledger summaries |
| Retry Count | 2–3 times | Respond to occasional fluctuations in state-owned asset system interfaces and reduce process failure rates |
| Timed Trigger Cycle | 09:00 on the 1st of each month | Align with the business rhythm of monthly data updates |
| File Upload Size Limit | 1000 MB | Accommodate unstructured documents such as large asset evaluation reports |
nodeId | Bind core nodes for data verification, AI analysis, and report generation | Ensure that workflow nodes execute in accordance with business logic order |
Three common configuration mistakes
- Workflow execution prompts context length over limit and returns a 400 status code. The cause is that large-node ledger data is not split and directly passed to the AI model, exceeding the context limit.
- The analysis result generated after image import is an empty field. The cause is that the script node for image-to-base64 conversion is not configured, and the image encoding is not correctly passed to the AI model.
- Calling the workflow via API returns a parameter error and cannot upload files. The cause is that files and
variablesparameters are not passed in the required format, resulting in interface verification failure.
How to verify the configuration is correct
- Manually trigger the workflow and check whether multi-system data configured in
variablesis correctly pulled to the corresponding nodes. - Upload a single large asset evaluation report to verify whether a context over-limit error occurs, confirming that the chunking and context configuration takes effect.
- Upload a test image, run the image-to-base64 script node, and check whether the output encoding format meets the input requirements of the AI model.
- Call the test API to upload files, check whether the returned status code meets expectations, and confirm that the interface configuration is correct.
The values provided here 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.