What the data for this category looks like
The data for this category mainly comes from the integrated budget management system, department staff establishment system, prior-year final accounts, project library, and the annual budget preparation notice issued by the Ministry of Finance. Its update frequency is annual. Documents processed daily include department budget drafts, project application forms, performance target forms, staff expense calculation forms, budget approvals, and similar materials. Most fields relate to budget subject codes, staff establishment numbers, project expenditure amounts, performance indicator values, and similar items. There is no unified fixed unit; matching must be done based on specific business scenarios.
What constraints do these characteristics impose on the "workflow orchestration" link
The annual update feature requires workflows to adapt to fixed trigger points within the annual cycle, to avoid repeated or missed processes. The need to integrate multi-source data requires workflows to support interfacing with different systems and unifying data formats. The diverse document types and complex fields require workflows to recognize the structure of different documents and extract corresponding fields for policy matching. The authority of data sources requires workflows to perform preliminary compliance checks on input data, to avoid invalid content entering the Q&A stage.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables reference scope | Only bound to current workflow node output | Prevent irrelevant cross-node data from interfering with the accuracy of policy Q&A |
nodeId trigger order | Follow data preprocessing → policy matching → result output | Align with the conventional logic of budget preparation review |
| Single conversation context limit | 8000–12000 token | Match the average length of department budget documents, avoid exceeding large model limits |
| Workflow timeout threshold | 600 seconds | Adapt to the time required for multi-source data pulling and document parsing |
| Maximum number of recalled results | Top 10 | Balance the comprehensiveness and response speed of Q&A |
| Document preprocessing switch | Enable segment truncation | Avoid errors caused by large budget documents exceeding context length |
Three Common Configuration Mistakes
- Multiple child node AI conversation results are displayed after workflow execution: The cause is that a result aggregation node is not configured in the workflow, causing the output of each child node to be exposed directly.
- A 400 error is returned when uploading a budget document with more than 12000 characters: The cause is that the segment truncation function of document preprocessing is not enabled in the workflow, and content exceeding the context length is not processed.
- Only the model name is bound when configuring
variables, and parameters such as MaxToken and temperature are not set: The cause is that corresponding parameters are not supplemented in the configuration panel of the model node, only the variable name is referenced without associating specific settings.
How to Verify Correct Configuration
- Trigger the workflow, check that the output panel only contains the results of the last AI node, with no redundant conversation content from intermediate nodes.
- Upload a department budget draft document with more than 12000 characters, confirm that the workflow can parse normally and return policy matching results, with no 400 error.
- Adjust the
variablesreference scope to cross-node, verify that subsequent nodes cannot call outputs from irrelevant nodes, confirming that the scope restriction takes effect. - Modify the node trigger order via
nodeId, simulate skipping the data preprocessing link, confirm that the workflow triggers a corresponding exception prompt.
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.