What this type of data looks like
Wind power financing daily report data is sourced from project filing announcements on regional energy regulatory platforms, publicly disclosed information from bank credit approval systems, and project repayment announcements from wind turbine manufacturers. Updates occur daily. Each daily report document is a structured entry containing fields including project number, wind power installed capacity, financing amount, financing party, fund provider, loan date, project location, and credit term. The unit for installed capacity is megawatts. The unit for financing amount is ten thousand yuan of RMB. The unit for credit term is calendar months.
Constraints imposed on vector models and indexing
Daily updated bulk data requires the index to support incremental appending, to avoid computational overhead from full reindexing. Structured fields include numeric installed capacity and financing amount. These values must be standardized for units before being converted to vectors, to avoid encoding bias from differing units. Field coverage spans multiple semantic types including identifiers, amounts, dates, and regions. Fields must be split and encoded in blocks before being merged into a total vector, to ensure balanced semantic weight across different dimensions. Each entry also contains sensitive financing information, so the index must support vector filtering based on permission tags, only matching retrieval requests within authorized scope.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | aliyun-text-embedding-v3 | Adapts to the structured multi-field semantics of wind power financing daily reports, and the encoding accuracy for numeric fields meets the matching requirements for financial data |
INDEX_INCREMENTAL | true | Adapts to daily updated bulk data, supports incremental index appending without full reindexing |
chunk_size | 800-1200 characters | The structured content of a single wind power financing daily report is mostly 500-1000 characters long. The segment length covers complete field groups to avoid semantic fragmentation |
recall_top_k | Top 10 entries | Balances retrieval efficiency and result coverage. A recall volume of 10 entries meets the association matching needs for wind power financing projects |
similarity_threshold | 0.75-0.85 | The semantic similarity threshold for structured financial data must be higher than that for general text, to filter low-relevance matching results |
REINDEX_ON_EMBEDDING_UPDATE | Manual trigger | Avoids resource waste from automatic reindexing, only perform full index reconstruction after replacing the embedding model |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: After replacing the embedding model, the original index is directly reused for retrieval, and the relevance of matching results drops significantly. Cause: Full index reconstruction was not performed. Original vectors use the vector space encoding of the original embedding model, and cannot match the vector space of the new model.
- Phenomenon: Retrieval result sorting does not follow semantic similarity, with low-relevance entries appearing first. Cause: No dedicated weight configuration was set for the structured fields of wind power financing daily reports. Only basic vector similarity sorting was used, and the sorting logic was not adjusted based on field priority.
- Phenomenon: When
text-embedding-ada-002is not selected, the interface displays the error promptundefined model must match "^(text. Cause: The system's default model verification rule limits the range of selectable models, and the currently used embedding model was not added to the whitelist configuration.
How to confirm the configuration is complete
- Manually trigger a full index reconstruction after replacing the embedding model, and verify that there are no model matching or vector encoding errors in the system logs.
- Select a complete wind power financing daily report entry as the retrieval term, check the semantic matching degree of the returned results, and adjust the similarity threshold according to actual business needs.
- Import a newly added wind power financing daily report entry for the current day, confirm that the index system automatically completes incremental appending without requiring manual re-import of all data.
- View the field configuration details of the vector index, confirm that structured fields have enabled block encoding, and the semantic weight of each field has been configured according to business rules.
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.