What this category's data looks like
Data sources for this category include state-owned asset investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. The update frequency is quarterly. Daily processed documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset evaluation reports, and post-investment evaluation reports. Fields include project identifiers, investment amounts, project approval nodes, post-investment operational data, and similar items. No additional statistical or percentage data is added.
What constraints these characteristics impose on workflow orchestration
To pull multi-source data, workflows must support cross-system interface calls. Store project identifiers passed across nodes via variables. Add scheduled trigger nodes to workflows to calibrate data pulling scope each quarter, matching the quarterly update frequency. Assign independent nodeId for each document to process multiple document types, adapting to their respective parsing rules. Business errors can cause severe consequences, so configure failure retry and exception alert links in workflows to avoid state-owned asset losses or liability risks.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Store core fields such as project number and post-investment data by project dimension | Used to pass project identifier data pulled from cross-systems across workflow nodes |
nodeId | Assign a unique identifier to each document parsing node | Used to locate processing logic for different types of documents (feasibility study reports, post-evaluation reports) |
| Code execution dependency library installation path | /app/custom_lib directory | Official recommended custom library installation location to prevent dependency loss after container restart |
| Tool call trigger condition | Trigger when a valid project ID exists in variables | Matches the business process trigger logic for investment projects |
| Timeout threshold | 1200 seconds | Covers the maximum time required for feasibility study report parsing and cross-system data pulling |
Three common mistakes
- Symptom: The code execution module prompts that the imported third-party library cannot be found during runtime. Cause: Dependencies were not installed to the specified custom directory inside the FastGPT container, and were only installed in the local environment without syncing to the container.
- Symptom: The tool call module does not trigger the corresponding system interface. Cause: A valid project identifier was not passed via
variables, and the trigger condition did not match the business process start node. - Symptom: Multiple AI conversation nodes cannot execute in parallel. Cause: Parallel node configuration was not enabled in workflow orchestration, or independent
nodeIdwas not assigned to each parallel node to avoid conflicts.
How to confirm the configuration is complete
- Access the code execution node of the workflow, review the dependency library installation log, and confirm library files are written to the
/app/custom_libdirectory. - Trigger the workflow and submit a test project ID, verify the ID is correctly stored in
variables, and confirm the tool call module initiates requests normally. - Configure multiple parallel AI conversation nodes, review the workflow running status, confirm nodes start simultaneously and no
nodeIdconflict errors occur. - Simulate a cross-system data pulling timeout scenario, verify the workflow triggers timeout alerts or retry logic within 1200 seconds.
The values given are common starting points and should be measured against local sample data.
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.