What the Data for This Category Looks Like
Data for air pollution control intelligent due diligence reports comes primarily from four sources: real-time and historical data exported from pollution source automatic monitoring equipment, project approval and acceptance documents for emission reduction initiatives, pollutant test reports issued by third-party organizations, and compliance records published by environmental protection departments.
Data updates follow two schedules: routine monitoring data updates monthly or quarterly. Due diligence reports for new projects are updated in stages as the project progresses.
Each complete report includes five core modules: basic project information, governance facility parameters, pollutant emission indicators, compliance verification records, and rectification ledgers. Fields in the reports include pollutant concentration (μg/m³), emission reduction volume (tons/year), equipment operating duration (hours), acceptance document numbers, and more. Some reports also include longitude and latitude coordinates of monitoring locations.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Workflow
Multi-source, heterogeneous data from air pollution control due diligence reports requires vector models to support joint encoding of structured numerical fields and unstructured text. This prevents the loss of semantic associations for quantitative information such as pollutant concentration and emission reduction volume that occurs with single-text encoding.
The staged update feature requires indexes to support incremental synchronization. Only generate vectors for newly added or modified report fragments to reduce resource consumption from full index reconstruction.
Quantitative fields with clear units need normalization processing before vector generation. This eliminates interference from differing value magnitudes on similarity calculations.
Additionally, the clear module division within reports requires indexes to be stored in shards by module. This prevents irrelevant content across modules from being included in retrieval results.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Air pollution control due diligence reports contain extensive professional text and quantitative data. This segment length preserves complete semantic associations within modules and avoids splitting that disrupts data logic |
vector_model | bge-large-zh-v1.5 | This model delivers stable semantic encoding performance for environmental protection professional text, and can accurately match associations between pollutant indicators and compliance requirements |
recall_top_k | Top 8–12 entries | Compliance verification for air pollution control due diligence reports covers multi-dimensional indicators. Too many recalled entries introduce irrelevant content, while too few miss critical information |
index_refresh_interval | Once per hour | Routine monitoring data updates monthly or quarterly. This refresh frequency balances resource consumption and data timeliness, and adapts to the staged update rhythm |
normalize_embedding | Enabled | Reports include quantitative fields of different magnitudes. Normalization processing unifies vector space distribution and improves the accuracy of similarity calculation |
spatial_index_enable | Enabled | Some reports include longitude and latitude coordinates of monitoring locations. Enabling spatial indexes supports location-based targeted retrieval, adapting to due diligence needs for regional air pollution control |
The parameter values provided on this page are common recommendations used to establish a starting point 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 settings.
Three Common Mistakes
- Phenomenon: A
504 Gateway Timeouterror occurs during vector retrieval, or the interface returnsrequest timed out after 60s. Cause: Retrieval timeout thresholds are not optimized for long text segments of air pollution control reports, and incremental indexes are not enabled, leading to full scanning of excessive data. - Phenomenon: The knowledge base status remains "Rebuilding" for more than 24 hours, and index switching cannot be triggered manually. Cause: Incremental index rules are not configured, and the number of files processed in parallel during full index reconstruction is not limited, leading to resource exhaustion.
- Phenomenon: Retrieval results include content from multiple knowledge bases, and it is not possible to prioritize retrieval of indexes from a specified knowledge base according to preset priorities. Cause: Retrieval weight parameters for knowledge bases are not configured, or indexes for high-priority knowledge bases are not set as independent namespaces.
How to Confirm the Configuration Is Correct
- Upload a test air pollution control due diligence report, check the vector generation log, and confirm that the normalization mark for quantitative fields has taken effect.
- Manually trigger an incremental index, check the index update duration, and confirm that it matches the preset refresh interval configuration.
- Initiate a query containing pollutant concentration keywords, verify the module matching degree of the retrieval results, and confirm that the shard index retrieval logic is correct.
- Check the index namespace of the knowledge base, confirm that the index prefix of the high-priority knowledge base has been set separately, and that the retrieval range can be specified via interface parameters.
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.