What the data for this category looks like
Water treatment financial report data comes from public annual and semi-annual financial reports of water utility operators, compliance monitoring monthly reports from environmental authorities, and industry-specific research documents. Update frequency is quarterly for enterprise financial reports, and monthly for compliance monitoring data. Document structure includes three sections: financial accounting, environmental compliance, and project operation. The financial accounting section contains revenue, cost, and cash flow fields. The environmental compliance section contains pollutant concentration and compliance rate fields. The project operation section contains facility runtime and chemical consumption fields. Units include mg/L (pollutant concentration), hours (runtime), tons (water treatment volume), and other professional measurement standards.
What constraints these characteristics impose on vector models and indexing
Multi-unit professional fields require vector models to support semantic representation of water industry terminology and measurement standards, preventing vector matching deviations caused by unit confusion. Period-separated, multi-source data requires indexes to support incremental updates and multi-source data merging, ensuring consistent timeliness between compliance data and financial report data. The document structure that mixes long-text project descriptions and structured indicators requires a chunking strategy that balances semantic completeness and retrieval efficiency, avoiding splitting cross-section related information. Cross-section business associations require indexes to support joint recall of multi-dimensional fields, improving the accuracy of financial report analysis.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Water treatment financial reports include compliance indicators with fixed units, such as COD mg/L. Chunking must retain the connection between indicators and their context to avoid semantic fragmentation. |
chunk_overlap | 150–200 characters | Compliance data and project operation descriptions have cross-paragraph associations. Overlapping chunks maintain semantic coherence. |
top_k | Top 8–12 entries | Financial report retrieval needs to cover multi-dimensional indicators including revenue, compliance, and cost. Too many recall results will introduce irrelevant data. |
similarity_threshold | 0.72–0.80 | Distinguish similar compliance indicator descriptions in financial reports to avoid mismatching different emission parameters. |
incremental_index_cron | 0 0 2 * | Enterprise financial reports are updated quarterly. Daily incremental indexing at 2 AM covers updates to temporary compliance reports. |
embedding_model | bge-large-zh-v1.5 | Adapts to Chinese financial report terminology and unit semantics, with stable vector representation performance for professional fields. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three common configuration errors
- Calling the indexing model returns a 401 Unauthorized error. The cause is failing to correctly configure
openai_api_baseas the proxy address, resulting in key verification failure. - Duplicate compliance indicator entries appear in retrieval results. The cause is not enabling the
dedup_enabledparameter, or the vector store not being configured with a unique ID index, leading to the same financial report fragment being indexed multiple times. - Recall results from locally generated embedding model vectors have poor correlation. The cause is not adjusting adaptation parameters for water industry professional terms, or using a general-purpose embedding model.
How to confirm proper configuration
- Upload a single water treatment financial report sample, check if the chunk preview retains complete compliance indicators and their corresponding units.
- Run a test retrieval, enter a professional query term, and verify that the similarity scores of the recall results fall within the set threshold range.
- Check the incremental indexing task logs to confirm that the task automatically triggers daily at 2 AM and has no abnormal errors.
- Verify the unique ID configuration of the vector store, retrieve different fragments of the same document, and confirm that there are no duplicate recall results.
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.