What the data for this category looks like
Water industry research report data comes from financial institution public utility sector research report databases, public announcements from water industry regulatory authorities, and third-party public utility industry data platforms. The regular update rhythm is monthly. Ad-hoc updates are added when major pipeline retrofits, water quality standard adjustments, or water enterprise financial reports are released.
Document structure includes four modules: policy interpretation, operational data reports, project feasibility analysis, and water quality monitoring reports. The operational data module contains multiple columns of structured numerical fields. Fields and units are as follows: water supply scale (unit: 10,000 cubic meters/day), pipeline length (unit: kilometers), water turbidity (unit: NTU), operation and maintenance cost (unit: yuan/cubic meter).
What constraints these characteristics impose on model access and configuration
The presence of multiple structured numerical fields requires configuring field recognition and mapping parameters to ensure the model can accurately associate water industry operational data with financial analysis requirements. The mixed document structure (long text analysis + structured tables) requires configuring segmented parsing rules to distinguish table structured data from plain-text policy interpretation, avoiding content confusion during retrieval. The uncertainty of ad-hoc updates requires configuring an incremental sync trigger mechanism to avoid excessive computing resource usage from full syncs. Dense professional terminology and unit annotation differences require configuring unit normalization and domain vocabulary loading parameters to improve semantic matching accuracy.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
parse_table_enable | Enabled | Water industry research reports contain large volumes of structured operational data tables. Enabling this setting extracts structured fields for precise retrieval |
field_mapping_list | Configured as ["供水规模","管网长度","水质浊度","运维成本"] | Covers core evaluation dimensions for water enterprises in financial analysis, ensuring the model identifies key retrieval fields |
max_context_length | 8000–12000 characters | Adapts to the average post-parsing text length of individual water industry research reports, matching the context window specifications of mainstream large models |
recall_top_k | Top 6–8 results | For water industry research report scenarios, single-round retrieval needs to cover multi-dimensional data for the same project or region. Excessive results will exceed context limits |
auto_sync_interval | 7 days | Matches the regular monthly update cycle. Manual sync triggers can be used for ad-hoc updates, balancing sync frequency and resource usage |
unit_normalization_enable | Enabled | Water industry research reports from different sources may have unit annotation differences. Enabling this setting unifies field units to improve matching accuracy |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Issue: Knowledge base retrieval returns results that cannot be parsed into standard JSON format, and model calls throw format errors. Cause: The table structured parsing switch is not enabled, causing table content in mixed documents to be returned as plain-text garbled code, damaging the JSON structure.
- Issue: Tool call links in advanced orchestration cannot automatically match the water industry research report retrieval tool. Cause: The dedicated field mapping list is not configured, so the model cannot identify core retrieval dimensions of water industry research reports and cannot trigger tool call logic.
- Issue: Rerank model call returns an abnormal number of results or throws parameter errors. Cause: The rerank return count is not adjusted based on the number of fields in water industry research reports, or a rerank model that does not support multi-field semantic matching is used.
How to confirm the configuration is complete
- Upload a single water industry research report, check if the parsed structured fields fully match the preset mapping list, to confirm the parsing configuration is effective.
- Initiate a test retrieval, input a professional water industry analysis question, check if the number of recalled results falls within the expected range, to confirm the recall parameter configuration is correct.
- Simulate an ad-hoc update scenario, manually trigger a sync task, check if newly uploaded research reports can be retrieved normally, to confirm the sync configuration is effective.
- Call the test interface, check if the returned retrieval result format is standard JSON, to confirm the parsing and format processing configuration is correct.
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.