What the data for this category looks like
Data sources for water treatment financing daily reports include local ecological environment department’s water-related project approval announcements, local financial institutions’ green credit release ledgers, and project filing information from third-party environmental service institutions. Updates run daily, matching the standard cycle for daily report submission. The core content consists of structured tables, plus text extraction fields from project filing scan documents. Fields include project ID, water treatment project name, affiliated region, financing amount, financing institution, approval date, project processing scale, environmental compliance level, and others. The financing amount unit is ten thousand yuan, and the project processing scale unit is ten thousand tons per day.
What constraints do these characteristics impose on workflow orchestration
Multiple heterogeneous data sources require configuring multiple parallel pull nodes in the workflow, plus a field alignment step to ensure consistent field mapping across different data sources. The daily update attribute requires configuring a scheduled trigger mechanism for the workflow, as well as incremental pull logic to avoid repeated processing of historical data. The mixed structured and unstructured document structure requires splitting workflow nodes to handle table data and attachment text separately, and configuring parsing timeout parameters to adapt to long document parsing. Specific field unit requirements require adding unit verification and conversion nodes to prevent business errors caused by inconsistent units.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Scheduled Trigger Cycle | Daily 09:00 | Matches the business morning submission requirement, ensuring generated daily reports can be used for internal circulation in a timely manner |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Water treatment project filing attachments are mostly long documents, requiring sufficient time to complete full text parsing |
Number of Recalled Entries | Top 5 | The scale of knowledge base documents related to water treatment financing is moderate; excessive entries will lead to redundant context |
Similarity Threshold | 0.75–0.85 | Filter low-relevance knowledge base recall results, retaining content strongly related to project approval and financing policies |
Field Validation Rules | Financing amount ≥ 0, Processing scale ≥ 0 | Ensure that pulled data source fields conform to business logic, preventing invalid data from entering subsequent links |
maxContext | 8000 characters | Adapt to the context length requirements of workflow conversations, avoiding exceeding the model’s processing limit |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- The phenomenon where all
<ai conversation>node replies appear in the workflow’s chat conversation after the workflow runs. The cause is that the hidden parameter for node output is not configured, and the output of all AI conversation nodes is appended to the workflow context by default. - The phenomenon where field values that do not conform to industry units appear in the generated daily report, such as marking processing scale as "tons per day" instead of "ten thousand tons per day". The cause is that the field unit conversion node is not configured, and unit standardization processing is not performed on the pulled raw data.
- The phenomenon where during workflow testing based on question classification, only the first question triggers knowledge base reference, and subsequent questions have no knowledge base recall results. The cause is that the context variable is not reset after the classification node, and residual context from previous interactions interferes with the recall logic of subsequent nodes.
How to confirm the configuration is complete
- Manually trigger the workflow once, check the running logs of the scheduled trigger node, and confirm that the data source pull time matches the configured trigger cycle.
- Check the running logs of the field validation node, and confirm that all pulled data source entries pass the legitimacy check.
- Initiate a conversation with the workflow, verify that only the output content of the specified AI conversation node is displayed, and the output of other nodes is not shown.
- Test the workflow triggered by different user IDs, confirm that the values of user-specific variables are bound to the corresponding IDs, and no globally shared situations occur.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.