What This Category of Data Looks Like
Wind power research report data primarily comes from securities firm power equipment industry research reports, public reports from China Electricity Council, and project filing documents from provincial energy administrations. Updates follow a schedule of quarterly regular reports and annual industry analyses, with temporary supplementary documents released when wind power projects connect to the grid or policies are adjusted. Document structures include modules such as project installation parameters, cost calculations, grid connection conditions, and regional consumption capacity. Standardized fields include single-unit capacity (unit: MW), hub height (unit: meters), annual utilization hours (unit: hours), and unit cost (unit: yuan/kW).
What Constraints Do These Characteristics Impose on Vector Models and Indexing
Wind power research reports contain large volumes of professional parameters and structured data. Retrieval for financial scenarios must support semantic encoding for both unstructured text and structured fields, to prevent loss of parameter semantics. Frequently updated temporary documents require indexes to support incremental writing, eliminating retrieval delays caused by full reconstruction. The structured multi-field characteristics of these reports require indexes to support hybrid retrieval, combining vector similarity and parameter threshold filtering to meet precise matching needs. The segmented structure of long document chapters requires segmentation strategies to retain contextual associations of parameters, avoiding semantic fragmentation that reduces retrieval accuracy after splitting.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Wind power research reports contain long sections of parameter analysis and project descriptions. This range can retain parameter context and avoid semantic fragmentation |
recall_count | 15–20 entries | Professional content density of wind power research reports is high. A sufficient number of recalled entries is needed to cover potentially relevant project and policy information |
similarity_threshold | 0.72–0.85 | Balances semantic matching accuracy for professional terminology and recall coverage, adapting to precise retrieval requirements in financial scenarios |
rerank_return_count | 5–8 entries | Core conclusions and parameters of wind power research reports are concentrated. Too many redundant results will reduce analysis efficiency |
VECTOR_STORE_TYPE | For private deployment, Zilliz or Milvus can be selected | Adapts to the incremental update requirements of wind power research reports, and supports hybrid retrieval and structured field filtering |
INDEX_BATCH_SIZE | 50–100 entries per batch | Adapts to frequent writes of temporary documents, avoiding timeout errors caused by excessive single-index pressure |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Core parameter fields of wind power projects are missing from vector recall results, making retrieved results unable to support professional analysis. Cause: Contextual association of parameters was not retained during segmentation, leading to loss of parameter semantics due to splitting.
- Phenomenon: The console returns a
400 Bad Requesterror, indicating index write timeout. Cause:INDEX_BATCH_SIZEwas not set to a value adapted to frequent temporary documents. Too many entries written in a single batch exceeded system thresholds. - Phenomenon: Abnormal fluctuations in recall count occur after migrating vector storage from PGSQL to Zilliz. Cause: Index shard configuration of the vector storage was not updated synchronously, leading to inconsistent recall rules between the old and new storage.
How to Verify Correct Configuration
- Upload a single wind power research report test document, check the segmentation preview module, confirm that the paragraph containing parameters is not split to the point of semantic fragmentation.
- Submit a retrieval request containing wind power professional terminology, verify whether the recalled results include core parameter information of the target project.
- After switching the vector storage from the default type to Zilliz, perform batch document import, confirm that no errors occur during index writing.
- After configuring the private deployment reranking model, submit a retrieval request with professional wind power terminology, verify whether the matching accuracy of the results meets business requirements.
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.