What the Data for This Category Looks Like
Data used for air pollution control-related investment research comes from multiple sources: real-time and near-real-time monitoring data from ecological environment monitoring stations, self-monitoring reports from air pollutant-emitting enterprises, industry technical standard documents, meteorological observation data, and scientific research literature. Update cycles cover hourly (for monitoring concentrations), monthly (for enterprise emission inventories), and irregular (for standard revisions). Document formats include structured CSV/Excel files with fields such as point codes, pollutant concentrations, and wind speed, PDF-format technical specifications, and plain-text scientific research abstracts. Fields and units include μg/m³ (pollutant concentration), m/s (wind speed), ℃ (air temperature), plus identifying fields such as enterprise unified social credit codes and monitoring point longitude and latitude.
What Constraints These Characteristics Impose on Model Integration and Configuration
The multi-source mixed format of air pollution control investment research data requires unified parsing support for both structured and unstructured data during model integration. Frequently updated monitoring data requires the vector database’s incremental synchronization mechanism to adapt to hourly update cycles, avoiding index delays that reduce retrieval timeliness. The professional attributes of multiple fields require configuring field-level weights to ensure core pollutant concentration data receives higher priority during retrieval. Parsing long documents such as complete emission inventories requires adapting to longer context windows to prevent key information from being truncated. Identifying fields including unified social credit codes and longitude and latitude must retain metadata to avoid confusion between monitoring data from different points during retrieval.
How to Set the Configuration
| Configuration Item | Recommended Range/Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Air pollution control investment research documents often contain long monitoring reports and technical standards. This range covers parsing and retrieval needs for most long documents |
embedding_batch_size | 32–64 | Adapts to batch import of structured monitoring data, balancing memory usage and import efficiency |
recall_top_k | Top 10–15 results | Balances the comprehensiveness of information required for investment research and the relevance of retrieval results, avoiding excessive redundant data that disrupts analysis |
parse_file_timeout_seconds | 600 seconds | Parsing large emission inventory Excel files or long PDF documents takes significant time. This duration covers the parsing process for most documents |
field_weight_config | Configure with 0.6 weight for pollutant concentration, 0.3 for meteorological parameters, and 0.1 for point information | Matches the core analysis logic of air pollution control investment research, ensuring core indicators receive higher retrieval priority |
rag_mode | Enable hybrid retrieval mode | Supports precise simultaneous retrieval of structured numerical data and unstructured text data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Phenomenon: After switching vector models, the knowledge base synchronization status remains incomplete for an extended period, and the original model configuration cannot be restored. Cause: No breakpoint resume parameter for incremental synchronization is configured, causing interrupted index tasks to fail to recover after model switching.
- Phenomenon: Occasional
LLM_model_response_emptyerrors occur when calling the large model, and logs show the response field is empty. Cause: No timeout retry mechanism is configured for the model, and long-text investment research requests exceed the model’s default timeout threshold. - Phenomenon: After initiating multiple retrievals for the same investment research question, the returned knowledge base answers differ. Cause: No unified field weight and recall threshold are configured, causing different batches of retrieval results to include field data with varying priority levels.
How to Confirm the Configuration Is Properly Set
- Upload a typical air pollution control monitoring report, check if the parsed text fragments retain core fields such as pollutant concentration and point code, and verify consistency between the parsed result and the original document.
- Initiate a retrieval that includes a pollutant concentration query, check if the number of recalled results matches the configured
recall_top_kvalue, and if the sorting follows the preset field weight logic. - Test switching the vector model, check if the index task starts and completes normally, and if the original model configuration can be restored.
- Initiate multiple identical investment research question queries, verify that the returned knowledge base answers remain stable with no significant fluctuations.
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.