What the Data for This Category Looks Like
Financial institutions use water treatment intelligent due diligence reports for project due diligence. These reports draw data primarily from water regulatory public platforms, real-time sampling data from on-site water quality monitoring stations, operation logs from water treatment equipment manufacturers, project environmental impact assessment (EIA) approval documents, and regular compliance inspection records. Data update rhythms vary significantly: real-time water quality parameters update minute-by-minute, equipment vendors archive operation logs daily, and EIA and compliance reports update alongside project phases. Single report documents have a fixed structure, including fields such as project basic information, influent and effluent water quality indicators (including COD, ammonia nitrogen, total phosphorus, etc., mostly in mg/L units), chemical dosage (unit: kg/day), equipment operating hours, detailed operation costs, and compliance judgment results.
Constraints From These Characteristics for Model Access and Configuration
Multi-source heterogeneous data sources require configuring multi-format parsing adapters to support inputs in CSV, JSON, PDF, and other formats. Real-time water quality parameters update minute-by-minute, so the average latency of model calling interfaces must be controlled within a reasonable range to avoid generating due diligence reports with outdated data. The fixed field and unit system requires the model to strictly match preset field names and units when generating reports, to prevent parameter confusion or unit errors. Longer single report content requires configuring reasonable segment lengths and recall counts to ensure core compliance and water quality indicators are fully recalled. Compliance judgment results included in due diligence reports require the model to prioritize recalling compliance standard documents for similar water treatment projects when associating with the knowledge base.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | The core fields of water treatment due diligence reports are mostly short paragraphs. Excessively long segments cause semantic fragmentation, while excessively short segments increase context splicing costs. This interval covers the complete semantics of a single set of water quality indicators and corresponding operation instructions. |
recallTopK | Top 6–8 entries | Water treatment due diligence requires associating compliance standards and historical operation records for the same project. Too many recalls introduce irrelevant data, while too few miss core compliance judgment basis. |
similarityThreshold | 0.75–0.85 | Strict matching of water quality parameter units and compliance thresholds is required. A threshold that is too low introduces incorrect associations, while a threshold that is too high fails to recall compliance reference documents for similar projects. |
apiTimeout | 600 seconds | Batch processing multiple water treatment due diligence reports requires reserving sufficient time for multi-source data parsing and model inference to avoid task interruptions due to timeouts. |
multiRoundHistoryMaxLen | 6 conversation rounds | Interactions for water treatment due diligence usually revolve around water quality abnormalities and compliance questions. 6 rounds cover the complete problem tracing chain while avoiding context overload. |
modelApiKey | Calibrate based on actual testing | Different large models have varying processing capabilities for structured water treatment data. Key configuration requires adjustment via testing interface connectivity and generation accuracy. |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to conduct tests on in-house samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After setting
multiRoundHistoryMaxLento 6, the model still fails to obtain context from the previous round. Cause: The multi-turn conversation switch is not enabled at the same time, or the configured model does not support context window reuse, resulting in historical conversations not being correctly passed to the model. - Phenomenon: Calling the model interface returns a
401 Unauthorizederror, and interface testing fails. Cause:modelApiKeyis not configured correctly, or the key has insufficient permissions to call the specified large model interface. - Phenomenon: After modifying the workflow, the published channel does not synchronize the updated logic. Cause: The update button for the published channel was not clicked, or the channel release was not re-triggered, resulting in the old version of the workflow still running.
How to Confirm the Configuration Is Complete
- Perform a model interface connectivity test, check if the returned results include the correct parameter format, and adjust the
modelApiKeyandapiTimeoutconfigurations based on test results. - Upload a standard water treatment due diligence report, check if the number of recalled knowledge base entries meets expectations, and adjust the values of
recallTopKandsimilarityThreshold. - Initiate more than two rounds of interaction tests, verify that the model can correctly associate content from the previous round of conversation, and confirm that the
multiRoundHistoryMaxLenconfiguration takes effect. - Batch import multiple test data sets, check if tasks are completed within the time set by
apiTimeout, with no timeout interruptions.
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.