What Data for This Category Looks Like
Data sources for document and material drafting in urban water utilities include pipeline inspection logs, water quality test reports, drainage permit archives, water supply billing ledgers, and more. Daily inspection logs update daily, with fields including inspection time, point number, exception type, and other details. Water quality test reports update per sampling cycle, with fields including sampling point, pH value, turbidity, total bacterial count, and other details. Common units include NTU, mg/L, and CFU/mL. Document structures include structured tables and semi-structured text, with individual content lengths ranging from tens of characters to thousands of characters.
What Constraints These Characteristics Impose on Workflow Orchestration
Water utility data is multi-source and dispersed, with differing update rhythms. This requires workflows to support multiple trigger timing configurations, tailored for daily inspection and periodic detection data pulls. Fields include professional measurement units and specific codes. Add a parameter verification step to the workflow to ensure input field formats comply with requirements. Individual document lengths vary widely. Configure flexible length control rules during context transfer to avoid exceeding model processing limits. Bind fixed-format water utility documents to variable filling rules for their corresponding templates to ensure output conforms to industry standard formats.
How to Configure Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing | Configure in separate nodes at 0:00 daily and 0:00 on the first day of each ten-day period | Water utility data is updated in batches based on daily inspections and periodic detections |
variables | Bind pipeline point codes and water quality indicator threshold variables | Dynamic matching of water utility professional fields is required for verification and document filling |
| Single Search Limit | Top 8 entries | Keyword matching for water utility professional documents needs to cover core indicators to avoid redundant results |
| Maximum Context | 8000–12000 token, calibrated based on actual testing | Context transfer for long individual documents needs to adapt to model processing capabilities |
| Parameter Verification Switch | Enabled | Water utility fields include professional units and codes, so invalid input must be filtered |
nodeId | Bind the unique identifier of the data source node | Accurately associate water utility data with corresponding update cycles |
Three Common Configuration Mistakes
- No optional variables appear when selecting variable references in the knowledge base search node. This occurs because the knowledge base name variable was not defined in the
variablesconfiguration item in advance, so the node cannot read the corresponding parameter. - The execution result of the AI chat node is written into the session return content. This occurs because the chat output switch for the node was not turned off, so the workflow node’s execution result mixes into the user interaction flow.
- Documents with incorrect field formats appear after workflow execution. This occurs because the parameter verification switch was not enabled, so water utility data with invalid units or codes was not filtered.
How to Verify Successful Configuration
- Trigger the test node for the data source, confirm that water utility data for the matching update cycle is pulled, and verify that the trigger timing configuration works.
- Add a test variable in the
variablesconfiguration item, access the variable reference interface of the knowledge base search node, confirm that the variable can be selected normally, and verify that the variable configuration works. - Enable the parameter verification switch, input test data containing invalid units, confirm that the workflow intercepts invalid input, and verify that the verification logic works.
- Run the long document processing test flow, confirm that context transfer works normally, and verify that the maximum context configuration adapts to the document length.
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.