What the data for this category looks like
Data sources for water utility intelligent due diligence reports include daily operation ledgers of water utility operators, data exported from pipe network monitoring systems, water supply statistical reports from local housing and urban-rural development departments, third-party audit reports, and corporate annual financial statements. Update cycles cover daily, monthly, and annual frequencies. Daily operation ledgers are updated daily. Monthly operation reports are generated monthly. Quarterly financial reports are released quarterly. Annual audit reports are updated annually. Document structure includes basic entity information, water supply service scope and facility parameters, monthly operation data, annual financial summaries, compliance inspection records, and key project progress. Fields and units follow these standards: water supply scale is measured in cubic meters, pipe network length in kilometers, revenue in ten thousand yuan, serviced population in person-times, and equipment operating duration in hours.
What constraints do these characteristics impose on vector models and indexing?
Water utility due diligence data includes structured ledgers, semi-structured reports, and unstructured audit reports. Vector processing must support multi-format input. Update cycles cover multiple periods. Indexing must support both incremental and full update modes. A large number of repeated basic fields and time-series operation data exist in documents. Field weight settings must distinguish core information from redundant content. Adaptation to time-series feature encoding is also required. Field naming varies across documents from different sources. Field standardization mapping must be completed before vectorization. Long documents account for a high proportion. A reasonable segmentation strategy must be configured to avoid truncating core due diligence data.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Water utility due diligence reports contain long sentences of operation data and compliance clauses. This range preserves the integrity of field associations |
vector_model | bge-large-zh-v1.5 | Water utility due diligence reports contain specialized terminology and time-series data. This model delivers stable encoding performance for Chinese professional texts |
index_refresh_interval | 1 hour | Monthly operation data is updated daily. This interval balances index timeliness and computing resource consumption |
max_upload_file_size | 500 MB | Single annual due diligence report PDFs or CSV files typically do not exceed this threshold, preventing upload timeouts |
similarity_threshold | Calibrated via actual testing | Core matching fields vary significantly across different due diligence scenarios. Adjustments must be made based on actual recall results |
top_k | Top 10 entries | Core associated information in water utility due diligence reports is usually concentrated in a small number of relevant documents. This quantity covers most matching results |
The parameter values provided on this page are common starting points 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 errors
- Phenomenon: After uploading and vectorizing CSV-format water utility operation data, the actual number of entries stored in the database is less than the number of entries in the source file. Cause: Some rows contain unescaped special characters such as commas and line breaks, causing the parser to truncate or skip the row data.
- Phenomenon: When inputting two water utility due diligence texts with distinct semantic differences, the similarity scores returned by vector calculation are abnormal. The score difference is extremely small or even identical. Cause: Pre-standardization processing is not performed for specialized fields in water utility due diligence reports such as pipe network numbers and water supply areas, leading to deviations in text feature encoding.
- Phenomenon: The collection creation prompt shows success, but the page always displays that the index has not been established. Cause: The
index_refresh_intervalparameter is not configured, or the parameter is set to0, causing the index generation task to not trigger.
How to confirm proper configuration
- Upload a single-page water utility operation report PDF. Check whether the console log shows records of successful chunking. Verify that chunk lengths fall within the preset
chunk_sizerange. - Import a CSV file containing 100 test entries. Compare the number of stored entries with the number of entries in the source file to confirm no abnormal data loss.
- Input a core text from a water utility due diligence report, such as "Average daily water supply of XX pipe network". Check the ranking and score distribution of recall results to confirm that similarity calculations meet expectations.
- Check the operation logs of index refresh tasks to confirm that the scheduled task corresponding to the
index_refresh_intervalparameter has started normally.
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.