What the data for this category looks like
Environmental monitoring research report data mainly comes from public bulletins issued by ecological environment authorities, compliance reports from third-party monitoring institutions, and independent enterprise monitoring logs. Update frequency varies based on monitoring type. Regular regional monitoring is updated monthly or quarterly. Online monitoring data for key pollution sources is pushed hourly or in real time. A single document typically includes structured fields such as monitoring point code, monitoring period, measured pollutant concentration values (units: μg/m³, mg/m³), basis for exceeding standard judgments, and compliance analysis conclusions. Some long documents include attachments of original sampling records and instrument calibration data.
What constraints these characteristics impose on knowledge base retrieval and recall
The multi-dimensional structured fields and time-staged update features of environmental monitoring research reports create multiple constraints for the retrieval and recall process. Precise matching of pollutant concentration values requires recall to associate semantic keywords, as well as match numerical ranges and units corresponding to fields. Time-staged online monitoring data needs incremental recall to avoid reloading historical data repeatedly. The monitoring point code, as a unique identifier, must be combined with spatial attributes to filter recall results and narrow the retrieval scope. Some documents include original sampling record attachments, so parsing and recalling attachment content is required to prevent missing key information.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | shaw/dmeta-embedding-zh | This model has high matching accuracy for professional terminology and numerical units in the environmental monitoring field, and adapts to the text characteristics of research reports |
recall_top_k | Top 10-15 | Environmental monitoring research reports include multi-dimensional fields, so enough candidate documents must be recalled to cover relevant monitoring periods and points |
similarity_threshold | 0.75-0.85 | Filter low-relevance historical monitoring data, and retain valid recall results for the same pollutant and same monitoring point |
chunk_size | 800-1200 characters | Balance the semantic integrity of structured monitoring data and analytical text, and avoid splitting key numerical information into separate segments |
parse_attachment | Enabled | Environmental monitoring research reports often include attachments of original sampling records and instrument calibration data. Parsing attachment content is required to complete recall |
incremental_update_interval | 1 hour | Adapt to the hourly update rhythm of online monitoring data for key pollution sources, and reduce redundant overhead from repeated retrieval |
The parameter values provided on this page are general recommendations that serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- The symptom is that large model-generated responses only include conclusive content, and do not reference specific monitoring values and compliance clauses from the research report. The cause is that
chunk_sizeis set too small, which splits key paragraphs containing specific numerical values during segmentation, leading to incomplete recalled content. - The symptom is that knowledge base retrieval takes a long time, with no obvious improvement even on hardware configurations of 8 cores, 64G memory, and RTX2070 graphics card. The cause is that
recall_top_kis set to more than 15 entries, and GPU acceleration adaptation for vector retrieval is not enabled. - The symptom is that retrieval results include documents for non-target monitoring points, and cannot accurately match the user-specified regional scope. The cause is that spatial attribute filtering configuration is not enabled, and the monitoring point code field is not used to filter results.
How to Confirm Configurations Are Properly Set
- Upload an environmental monitoring research report document, check if all structured fields and attachment content are included in the parsing result, to confirm that configurations are effective.
- Initiate a retrieval request that includes specific pollutant concentration ranges and monitoring points, verify that returned results cover the specified monitoring period and point range, to confirm that relevant filtering configurations are effective.
- View retrieval latency logs, compare the retrieval delay between incremental updates and full updates, to confirm that the incremental update configuration adapts to the data update rhythm.
- Initiate the same retrieval request more than three times, verify the sorting and content consistency of returned results, to confirm that sorting configurations are effective.
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.