What the data for this category looks like
Air pollution control research report data mainly comes from public monitoring reports of ecological environment departments, annual analyses from industry associations, corporate emission reduction technical documents, and third-party monitoring station data. The update rhythm follows monthly monitoring bulletins, quarterly industry updates, and annual policy interpretations. There is no fixed high-frequency update schedule, but periodic bulk update requirements exist. The document structure includes two core content types. One is structured monitoring data with units, such as PM2.5 concentration (μg/m³) and NOx emissions (tons). The other is unstructured technical solutions, policy clauses, and industry analysis text. The length of individual documents varies widely, ranging from hundreds of-word monitoring briefings to tens of thousands-word in-depth research reports.
What constraints do these characteristics impose on vector models and indexing?
The mixed content structure of air pollution control research reports requires vector models to adapt to both domain-specific terminology text and structured numerical fields with units. This prevents a single model from failing to cover the semantic features of both data types. The non-fixed update rhythm requires indexes to support incremental updates, reducing resource consumption from full index rebuilding. The wide range of document lengths requires flexible segmentation strategies. This avoids both loss of key information from long text truncation and semantic fragmentation from overly fine short text segmentation. Structured fields with clear units need to be included in index metadata, enabling precise filtering by dimensions such as monitoring sites and pollutant types.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed short monitoring data and long technical text in air pollution control research reports, balancing semantic completeness of segments and index granularity |
vector_model | Calibrated via actual testing | Supports domestic open-source vector models, adapting to vector representation of domain-specific terminology and numerical fields |
index_type | HNSW index | Balances retrieval speed and recall accuracy under medium data volumes, meeting daily retrieval needs |
incremental_update_enabled | true | Adapts to the monthly/quarterly incremental update rhythm of research reports, reducing computational overhead from full index rebuilding |
recall_top_k | Top 10 results | Covers multi-dimensional monitoring data and policy information, avoiding missing key content in a single retrieval |
metadata_filter_enabled | true | Supports filtering retrieval results by structured fields with units, such as monitoring sites and pollutant types |
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 misconfigurations
- Phenomenon: Retrieval results only return single document fragments, and cannot cover cross-document associated information. Cause: Configured to build separate indexes for individual documents instead of using a unified global index, which does not meet the needs of cross-document associated retrieval for research reports.
- Phenomenon: A
400 Bad Requesterror is triggered when adding or deleting data in the vector database. Cause: Did not enable theincremental_update_enabledconfiguration and attempted a full index rebuild directly, or did not configure a metadata synchronization interface to link with an external management system, making incremental update operations impossible. - Phenomenon: Retrieval results cannot be filtered by pollutant concentration thresholds. Cause: Did not include structured numerical fields in index metadata, or did not enable the
metadata_filter_enabledconfiguration, making conditional retrieval of fields with units impossible.
How to confirm the configuration is correct
- Upload a single research report containing both monitoring data and policy text, check if the index generation log includes a prompt that metadata field indexing is complete, confirming that structured fields have been correctly identified.
- Perform an incremental update operation, verify whether newly uploaded research reports are automatically added to the index without triggering a full index rebuilding process.
- Initiate a retrieval that includes domain-specific terminology and structured field conditions, check that the number of returned results matches the
recall_top_kparameter configuration. - Switch the vector model to a domestic open-source version, verify that the retrieval function operates normally with no compatibility error prompts.
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.