What the data for this category looks like
Water treatment research report data primarily comes from public reports from environmental industry associations, completed municipal water project documents, technical white papers from water treatment equipment manufacturers, and real-time public monitoring data from water quality monitoring stations. Update cycles include monthly industry dynamic updates, quarterly project case updates, and real-time water quality data updates. Document structure includes fields such as project number, treatment scale, influent/effluent water quality indicators (such as COD, SS, unit: mg/L), process flow diagram descriptions, compliance standard numbers, and operation and maintenance records. Each document often contains multiple sets of structured parameters and technical descriptions combining text and images.
What constraints do these characteristics impose on model integration and configuration
The feature that structured parameters of water treatment research reports are bound to units requires configuring parsing rules that support structured field recognition during model integration, to avoid recognition errors caused by separation of parameters and units. The multiple update frequency data source types require configuring a switching mechanism between incremental pull and full pull, to avoid excessive computing resource occupation from full pull operations. The feature that a single document contains multiple sets of process parameters requires configuring context segmentation to retain the context associated with parameters, to prevent truncation of key process descriptions. The requirement to reference compliance standard numbers requires configuring the association logic between knowledge base metadata and model calls, to ensure that retrieval results can accurately match corresponding industry standards. These constraints differ from general research report scenarios, requiring targeted adjustments to model integration parameters and rules.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
recall count | top 15-20 entries | Water treatment research reports have high single-document parameter density; too many recalled entries will introduce irrelevant process descriptions, while too few will miss key parameters |
rerank return count | top 5-8 entries | The core process parameters and compliance requirements of a single research report are concentrated in 3-5 paragraphs; retaining a small number of entries after reranking can compress the context length |
maxContext | 8000-12000 characters | Water treatment research reports often contain multiple sets of process parameters and text-image descriptions; an overly long context will exceed the window limits of most open-source models |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large water treatment research reports come with attachments such as equipment drawings and monitoring reports, so parsing time is longer than that of general documents |
similarity threshold | 0.72-0.80 | The numerical matching accuracy of water quality parameters is high; a threshold that is too low will introduce research reports from non-corresponding processes, while a threshold that is too high will miss relevant entries |
UPLOAD_FILE_MAX_SIZE | 500 MB | Water treatment research reports often come with multiple equipment drawings and batch monitoring data reports, so support for large file uploads is required |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and testing on local samples is recommended before finalizing settings.
Three common misconfigurations
- Phenomenon: The locally deployed reranker model does not appear in the system model list. Cause: The endpoint address and requestAuth authentication credentials of the custom model were not added in the system configuration file, or the configured port is not open for external access.
- Phenomenon: The number of results returned by model calls does not match the configured
rerank return count. Cause: The return format of the reranker model is not bound to FastGPT's reranking logic, or the model's own return parameters do not return sorted scores and text as required. - Phenomenon: The units of water quality parameters are lost in the parsed research report. Cause: The structured field extraction function of document parsing is not enabled, or the configured segment length is too short, truncating the context associated with parameters and units.
How to confirm successful configuration
- Enter the system model management page, and check whether the endpoint address and authentication credentials of the custom reranker model match the configuration of the locally deployed service.
- Upload a test water treatment research report, and check whether the parsed document retains parameter fields with units such as COD and treatment scale.
- Initiate a research report retrieval and question-answering request, and check whether the number of returned results matches the configured
recall countandrerank return count. - Check the system operation logs to confirm that the request parameters of model calls match the configured
maxContextandsimilarity threshold, with no timeout or authentication errors.
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.