This documentation is developed using the workflow features of FastGPT v4.17.0.
What the data for this category looks like
Data sources for this category include enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. The update frequency is daily. Commonly involved documents include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance documents, and supplier complaint materials. Data fields include procurement method, budget basis, scoring method, supplier qualification conditions, contract performance clauses, and more. No unified fixed unit applies; matching measurement standards correspond to individual business data entries.
What constraints these characteristics impose on workflow orchestration
Daily updated data sources require workflows to be configured with scheduled trigger nodes to avoid delays or omissions from manual execution. Multi-source data with varying formats requires adaptation to interface formats of different platforms, so a unified data parsing link must be added to workflows. Diverse document types require input nodes to support parsing of multiple document formats, while execution logs for each link must be retained to meet audit traceability requirements. The potential for severe consequences from business errors requires workflows to be configured with clear failure retry and alert mechanisms to prevent the spread of abnormal processes.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Scheduled trigger node | Execute at 2:00 AM daily | Matches daily data update frequency, avoids peak business hours |
| JSON input box variable binding | Allow selection of global variables and node output variables | Adapts to requirements for splicing multi-source business data |
| Single AI call context upper limit | 8000–12000 token | Covers the typical text length of a single bidding document plus bid evaluation records |
| Failure retry count | 2 times | Avoids process interruptions caused by temporary network fluctuations |
| Workflow output rules | Only display the result of the last AI conversation node | Meets the requirement that ledger summary reports only need final output content |
variables configuration | Bind data source variables such as procurement plans and bid evaluation records | Automatically pull the latest daily updated business data |
Three common configuration errors
- Symptom: Cannot select predefined global variables in the JSON input box; the input box only supports manual entry of JSON text. Cause: The variable binding permission for the workflow is not enabled, or the corresponding variable is not declared as a type that can be called by the JSON input box.
- Symptom: After the workflow runs, all output results from AI conversation nodes are displayed, with intermediate content not filtered out. Cause: The workflow output filtering rules are not configured; all node execution results are retained by default.
- Symptom: Uploading a large bidding document triggers a 400 error, and workflow execution is interrupted. Cause: No file content truncation or preprocessing logic is added to the workflow, and input text exceeding the large model context limit is not handled.
How to verify correct configuration
- Trigger the scheduled trigger node, check whether the workflow starts automatically at the set time, and confirm that the full process is completed without manual intervention.
- View the workflow's variable binding configuration, confirm that the JSON input box can normally call predefined procurement, bid evaluation, and other data source variables.
- Simulate text input exceeding the context length limit, verify whether the workflow triggers the preprocessing logic to avoid 400 errors.
- Check the workflow's output node configuration, confirm that only the summary result of the last AI conversation node is displayed, with no redundant intermediate content.
The values provided are common starting points and should be evaluated against individual deployment 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.