What data looks like for this category
Air pollution control investment research data originates from real-time collections at environmental monitoring stations, national air pollutant emission standard documents, industry project feasibility study reports, operation and maintenance logs, and academic research literature. Real-time monitoring data updates hourly. Standard documents and industry reports update quarterly to annually. Document structure includes fields such as monitoring point ID, pollutant concentration (unit: μg/m³), timestamp, treatment process parameters, and project budget. Content ranges from short-text alert messages to feasibility study documents tens of thousands of words in length.
What constraints these characteristics impose on vector models and indexing
Real-time monitoring data’s hourly update frequency requires indexes to support incremental writing, to avoid performance loss from full index reconstruction. Mixed document types (short alerts and long feasibility studies) require vector splitting strategies that adapt to texts of varying lengths. Professional terms such as denitrification efficiency and flue gas flow, plus specific units including μg/m³ and tons per hour, require vector models to support semantic encoding for the environmental protection field, to prevent loss of professional semantics. Multi-field mixed data structures require indexes to support hybrid retrieval of dense and sparse vectors, to cover query needs for both numerical parameters and text descriptions.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Air pollution control documents include long-text feasibility study reports and short time-series alerts. This range balances semantic integrity and retrieval accuracy |
recall_top_k | Top 10–15 results | Investment research scenarios require coverage of multi-dimensional monitoring data and project materials. This quantity balances retrieval efficiency and recall coverage |
similarity_threshold | 0.65–0.75 | Many professional terms are present. A threshold that is too low introduces irrelevant retrieval results, while a threshold that is too high misses relevant valid data |
index_type | HNSW | Supports high-frequency incremental writing and low-latency retrieval, adapting to real-time update requirements for air pollution control monitoring data |
incremental_index_enable | Enabled | Avoids performance loss from full index reconstruction, adapting to high-frequency updated monitoring data |
vector_api_timeout | 30 seconds | Some professional vector models take extended time to load environmental protection field texts. This duration covers most request scenarios |
The parameter values provided on this page are common recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- A
401 Unauthorizederror is returned when calling the vector model. The vector model API key is not configured correctly, or the key permissions do not cover vector embedding requests. - Duplicate entries are automatically deleted after custom-split document chunks are stored in the knowledge base, causing the original index order to differ from the split results. The default document deduplication switch is enabled, and deduplication rules are not adjusted for custom-split chunks.
- Knowledge base index construction speed is too slow to support synchronization of real-time monitoring data. Incremental indexing is not enabled, the full index construction mode is used, and no multi-replica parallel index tasks are configured.
How to verify correct configuration
- Upload mixed documents including long-text feasibility study reports and short time-series alerts, check vector embedding task logs, and confirm no timeouts or error returns.
- Initiate retrieval requests for air pollutant concentrations or treatment processes, and verify that the number of returned results matches the configured
recall_top_kparameter. - Upload updated monitoring point data, check index update records, and confirm only new data is written incrementally, with no full reconstruction performed.
- Test duplicate document chunks from custom splits, and confirm deduplication rules match preset configurations.
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.