What the data for this category looks like
Data for this category primarily comes from online monitoring platforms of environmental regulatory authorities, operation logs of enterprise pollution control facilities, compliance reports from third-party testing institutions, and environmental impact assessment (EIA) approval documents. Data update rhythms fall into two categories: online monitoring data updates hourly in real time, internal enterprise operation logs are updated daily in aggregate, and EIA documents are updated at the project level as static content. Document structures usually include fields such as monitoring point number, pollutant name, concentration value, emission limit, operation parameters of treatment equipment, and compliance judgment result. Concentration units are mostly mg/m³. Operation parameters include quantitative indicators such as rotational speed and power.
What constraints these characteristics impose on the "model access and configuration" link
Hourly real-time updates of online monitoring data require the model access link to configure a vector index update mechanism that supports high-frequency batch data processing. This prevents retrieval result deviations caused by index lag. Multi-source heterogeneous document structures and diverse field definitions require configuring field mapping rules. These rules adapt to format differences across data sources, ensuring the model can accurately extract core information required for compliance judgments. Standardization requirements for pollutant concentration units need unit verification and conversion parameters configured during model access. This avoids inference errors caused by inconsistent units. The existence of project-level static documents requires configuring incremental index update logic. This only regenerates vectors for newly added or modified documents.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_batch_size | 16–32 items/batch | Air pollution control monitoring data has a small per-item data size. This batch processing range balances memory usage and processing speed |
vector_index_refresh_interval | 1 hour | Matches the hourly update rhythm of online monitoring data, preventing retrieval result deviations caused by index lag |
parse_field_mapping | Map to standard fields using "pollutant name/concentration value/monitoring time" | Core fields of air pollution control data are concentrated in these three categories. Unified mapping improves model retrieval accuracy |
max_context_length | 8000–12000 characters | A single due diligence report may contain multiple sets of monitoring data and equipment logs. This length can fully carry associated information |
model_api_qps_limit | 50–100 requests/minute | Adapts to the batch call demands of high-frequency monitoring data, preventing task interruptions caused by interface rate limiting |
unit_check_switch | Enabled | Air pollution control data includes multiple types of quantitative units. Enabling this automatically verifies and converts units, improving inference accuracy |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, 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 mistakes
- Phenomenon: After replacing the
embedding_model, the retrieval results of the existing knowledge base have obvious deviations, or historical data cannot be recalled. Cause: Vector index reconstruction was not performed. Existing vectors were generated based on the old model and do not match the vector space of the new model. - Phenomenon: The model proxy service deployed via Docker restarts continuously and cannot access external models normally. Cause: Interface access permissions or port mappings were not configured correctly. The service restarts automatically after startup due to connection failures.
- Phenomenon: Frequent rate limit errors are triggered when calling the model in the production environment, with status code
429returned. Cause: Themodel_api_qps_limitparameter was not adjusted based on the business scenario. The default configured QPS is insufficient to support batch calls of high-frequency monitoring data.
How to confirm the configuration is complete
- Submit a historical air pollution control due diligence document, check the running logs of the vector generation task, and confirm that the preset core field information has been correctly extracted.
- Manually trigger a vector index refresh operation, check the index update progress, and confirm that it matches the configuration logic of
vector_index_refresh_interval. - Call the model interface to test the compliance judgment task, check whether the returned results include standardized quantitative data, and confirm that the unit verification switch is active.
- Simulate high-frequency batch data submission, observe the interface call status, and confirm that no rate limit errors are triggered, adapting to actual business call demands.
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.