What data looks like for this category
Data sources for this category include investment supervision systems of the State-owned Assets Supervision and Administration Commission, enterprise ERP and project management systems, industry market data, audit and financial statements. Update frequency is quarterly.
Daily documents covered include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset evaluation reports, post-investment evaluation reports. Data fields include project ID, investment amount, project initiation approval nodes, post-investment operation metrics, post-evaluation scores, and more. Most document structures combine structured tables and unstructured reports. Information across the full project lifecycle—initiation, implementation, post-evaluation—must be covered.
Constraints on workflow orchestration from these characteristics
Quarterly updated data requires workflows to support batch pulling and scheduled execution. This prevents processing excessive historical data in a single run.
Mixed input from multiple document types requires workflows to have configured differentiated parsing nodes. These nodes adapt to field extraction rules for structured tables and unstructured reports.
Cross-system data sources require workflows to integrate multiple data source connection nodes. This ensures compliance and completeness of data sources.
Full project lifecycle field coverage requires workflows to configure variables mapping rules by initiation, implementation, post-evaluation phases. This ensures accurate alignment of ledger fields with content from documents in each phase.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Split variable groups by project phase, bind each group to a corresponding data source. For example, bind initiation phase variables to ERP project data, and post-evaluation phase variables to post-investment operation data | Adapts to field mapping requirements for multi-phase data in this category, avoids global variable conflicts |
| Node trigger rules | Trigger after data source updates complete, execute after a 10-30 minute delay | Avoids empty field issues caused by cross-system data synchronization delays |
| AI chat node visibility | Only retain output visibility for the final report generation node. Hide output from all other nodes | Streamlines workflow output, focuses on ledger summary and final reports |
nodeId | Assign a unique nodeId to each data source pulling node | Facilitates workflow debugging and accurate association of data transfer between nodes |
| Timeout threshold | 600 seconds | Matches average response time for cross-system data pulling, prevents workflow interruptions |
| Batch processing limit | Process 50-100 project data entries per run, calibrated based on actual testing | Matches project volume scale for quarterly updates, prevents workflow timeouts |
Three common misconfigurations
- Scenario: After a workflow runs, all AI chat node responses appear in the final chat conversation. Cause: Visibility rules for individual AI chat nodes are not configured. All node output is retained by default.
- Scenario: When different users call the same workflow, values for identically named
variablesare not isolated per user. Data cross-contamination occurs. Cause: User-level variable isolation configuration for the workflow is not enabled. The global variable pool is used by default. - Scenario: When testing a workflow based on question classification, only the first question triggers knowledge base reference. Subsequent questions return no associated knowledge base results. Cause: The workflow does not have a context reset node configured. Subsequent questions reuse context from prior nodes, causing abnormal knowledge base recall logic.
How to confirm correct configuration
- Review workflow node configurations. Confirm
variablesare split by project phase and bound to corresponding data sources. Verify that variable mapping fields match document fields for this category. - Trigger a test run. Check that only content from the target node appears in the final output. Confirm that AI chat node visibility configuration is active.
- Call the workflow using different test accounts. Confirm that values for identically named variables are isolated per user. No data cross-contamination is observed.
- Run multi-round question classification testing. Confirm that each question triggers knowledge base reference. Verify that context transfer logic operates normally.
The values provided here are common starting points. They should be evaluated against the samples of the entity using the workflow.
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.