What data for this category looks like
This category of data originates from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. The update cycle is annual. Commonly processed documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. Fields cover income and expenditure details, asset and liability balances, budget execution difference explanations, and similar items. Common units are yuan and ten thousand yuan. Document formats primarily combine structured tables and explanatory text.
What constraints these characteristics impose on the "workflow orchestration" link
The annual update cycle requires workflows to support centralized scheduling and processing of batch annual data, and reserve resource space for batch node execution. Data from multiple systems requires configuring cross-system data pulling and standard alignment steps in the workflow to ensure unified data format across exported files from different systems. The mixed document structure of structured reports and explanatory text requires workflows to support both structured field extraction and unstructured text parsing nodes. Diverse field units require configuring unit conversion rules in the data verification link to avoid audit rejection caused by inconsistent standards.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Knowledge base selection | Bind the knowledge base identifier passed via variables | The scope of audit knowledge bases for different budget units in declaration scenarios can be dynamically adjusted via API calls to adapt to multi-entity audit requirements |
| Text content extraction | Set single processing length to 800–1200 characters | Adapt to the parsing needs of long-text final account explanations in final account documents, avoid truncating key difference explanation content |
| Database query | Configure single query timeout to 600 seconds | Match the time-consuming characteristics of annual batch data queries, avoid task interruptions caused by excessive data volume |
| Workflow node triggering | Execute according to the approval node bound via nodeId | Ensure the audit process advances in the order of file submission, avoid process timing confusion |
| Result verification | Filter the top 5 abnormal data entries | Ensure core verification items cover major data anomalies, balance audit efficiency and accuracy |
Three common mistakes
- Symptom: The workflow runs with a prompt "The specified knowledge base was not found", returning status code 404. Cause: The knowledge base identifier was not passed via the
variablesparameter during API calls, causing the node to fail to match the corresponding knowledge base scope. - Symptom: The text content extraction node fails to run, with the interface displaying "Model parsing exception". Cause: The single processing length was not adjusted for the long-text structure of final account documents, causing the model to fail to fully parse paragraph content.
- Symptom: The database query node times out, and the task status changes to failed. Cause: A reasonable timeout duration was not configured, failing to adapt to the time-consuming requirements of annual batch data queries.
How to confirm the configuration is complete
- Trigger an API call, pass test
variablesparameters, check if the workflow automatically matches the corresponding knowledge base. - Upload a standard final account document, run the text content extraction node, check if the parsing result covers the core fields of the document.
- Perform a database query test, confirm that the task completes within the preset duration with no timeout errors.
- Submit multiple test files, check if the workflow executes node tasks in the order of submission.
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.