What the Data for This Category Looks Like
Air pollution control research report data mainly comes from publicly available monitoring datasets from ecological environment departments, annual reports from industry associations, technical literature from research institutes, and public disclosure documents of enterprise pollution discharge permits. Update rhythms vary across data sources: real-time monitoring point data updates hourly or daily, while industry policies and technical reports update quarterly or annually. Document structures include modules such as monitoring point codes, pollutant concentrations, governance technology parameters, and emission reduction calculation tables. Fields cover monitoring points, PM2.5 concentrations, SO2 emissions, and other items, with units mostly professional environmental measurement units such as μg/m³, tons/year.
What Constraints These Characteristics Impose on Deployment and Upgrade
Multi-source heterogeneous data sources require adapting access and parsing for multiple data formats during deployment, including API interfaces, PDF documents, and structured tables. Differentiated update rhythms require configuring scheduling rules that combine incremental and full synchronization. Upgrade phases must be compatible with synchronization logic from old and new versions. Complex document structures and specialized fields require preset rules for text splitting, term recognition, and unit standardization. During upgrades, compatible mappings of legacy configurations must be retained. The multi-unit field system requires configuring unit conversion rules during deployment to avoid unit mismatch issues during retrieval.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Air pollution control research reports often contain long texts and complex tables; insufficient timeout duration will cause parsing interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single collection-style research reports may integrate multiple rounds of monitoring data, so their overall size is generally larger than general documents |
maxContext | 800–1200 characters | The content is dense with professional terms, so sufficient context must be retained to ensure semantic coherence |
Recall count | Top 8–12 results | Multi-dimensional monitoring data and technical solutions need to be covered; too few results will lead to missing key information |
Similarity threshold | 0.72–0.80 | Professional term matching requires high accuracy to avoid retrieving irrelevant general environmental protection documents |
CUDA_VISIBLE_DEVICES | 0,1 | Adapt to dual-GPU deployment scenarios; adjust the parameter value based on the actual number of graphics cards |
The parameter values provided on this page are conventional 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, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: An
Access denied for user 'root'@'localhost'error occurs after restarting the Docker container. Cause: Incorrect user UID mapping between the host machine and the container under the Ubuntu system, leading to abnormal permissions on the mounted database configuration file. - Symptom: Field extraction results for old-version research reports are missing after cross-version upgrade. Cause: Intermediate version upgrade scripts were not executed, so the database table structure did not complete compatible updates.
- Symptom: Only a single GPU's video memory is occupied during model invocation. Cause: The
CUDA_VISIBLE_DEVICESparameter was not configured to specify available GPUs; the system defaults to only calling GPU 0.
How to Confirm Proper Configuration
- Upload a local air pollution control research report document, check whether preset fields such as monitoring points and pollutant concentrations are correctly extracted after parsing, and confirm that the text splitting logic meets expectations.
- Manually trigger an incremental synchronization task, check that the synchronization log only includes data source files with update times later than the last synchronization, and confirm that the scheduling cycle configuration is effective.
- Initiate a retrieval request containing professional terms, check whether the number of returned results and matching degree conform to the preset configuration, and confirm that the retrieval rules are operating normally.
- View the container runtime logs, confirm that no database permission errors or insufficient video memory warnings appear, and confirm that the runtime environment configuration is correct.
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.