What this category of data looks like
Power industry financial report data originates primarily from public disclosure announcements of listed power enterprises. These include annual reports, semi-annual reports, quarterly reports, and temporary operation announcements. Document structures cover consolidated financial statements, operation analysis sections with data such as power generation volume, power sales volume, grid-connected electricity price, and proportion of installed power capacity, and major event explanations. Fields use multiple units including RMB yuan, kilowatt-hour, megawatt, and percentage. Data is released regularly on a quarterly and annual basis, with temporary announcements updated alongside corresponding major events.
Constraints imposed by these characteristics on vector models and indexing
Power financial reports feature a hybrid structure of structured financial tables and unstructured operation analysis text. Individual document lengths vary widely, creating a risk of context fragmentation during long text segmentation. This requires a more granular segmentation strategy. Operation data field units are closely tied to their associated values. Vector encoding must retain this semantic association to prevent similarity calculation deviations caused by separation of units and values. The update rhythm combining regular financial reports and temporary announcements requires indexes to support mixed scheduling of incremental synchronization and periodic full refresh. Financial report data differs significantly across power business segments, so indexes must support fine-grained recall filtering by business segment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the long text and structured data blocks of power financial reports, preventing key content from being split across multiple segments |
chunk_overlap | 50–100 characters | Retains semantic association between segments, ensuring context integrity of financial and operation data |
vector_normalize | Enabled | Meets configuration requirements for version 4.8.23 and above, corrects semantic similarity calculation deviations from non-normalized vector models |
recall_top_k | Top 8–12 results | Covers relevant data scattered across multiple segments in power financial reports, avoiding omission of key information |
similarity_threshold | 0.70–0.80 | Filters low-relevance non-financial report content, retaining document fragments directly related to power business |
index_refresh_interval | Weekly full refresh + daily incremental synchronization | Balances the regular update requirements of power financial reports and the timeliness of temporary announcements, reducing index maintenance costs |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Retrieval logs show relevant document fragments have been recalled, but the large language model ultimately returns "no matching content found". Cause: The
vector_normalizeconfiguration is not enabled, or the similarity threshold is set too high, causing valid recalled fragments to be excluded from the large language model context. - Symptom: Knowledge base query response times are excessively long, and backend logs show the large language model request carries far more tokens than the preset limit. Cause: The
max_context_tokenparameter is not restricted, and all recalled document fragments are directly passed to the large language model, causing the context length to exceed the model's processing limit. - Symptom: Charts and images in financial report documents cannot be retrieved, and search results contain no text descriptions of relevant visual content. Cause: OCR parsing functionality is not enabled, or the used vector model version does not support image vector encoding. Upgrade to version 4.8.0 or above to resolve this.
How to Confirm Proper Configuration
- A test power financial report document is uploaded. Parsed text segments are reviewed to confirm alignment between segment length and the preset
chunk_sizevalue. - A retrieval request targeting specific financial report data is submitted. The number of returned recalled documents is reviewed to confirm alignment with the
recall_top_ksetting. - The vector model configuration interface is viewed to confirm the enabled status of
vector_normalizemeets preset requirements. - A test financial report document containing charts is uploaded. Search results are checked for OCR parsed text of the charts, confirming the image indexing functionality is properly enabled.
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.