What the data for this category looks like
White goods financial report data comes from publicly listed companies’ periodic reports, temporary announcements on stock exchanges, and publicly available industry operating data documents. Updates follow statutory disclosure rules. Periodic reports launch annually, semi-annually, and quarterly. Temporary announcements go live immediately when major operating data shifts occur. Document structures include consolidated financial statements, product category operating analysis, cost breakdowns, channel layouts, R&D investment details, and more. Fields cover revenue, sales volume, cost, and other metrics, using standard units such as RMB yuan and ten thousand units. Each document includes multi-dimensional structured and unstructured content.
Constraints for vector models and indexing
The update rhythm and content traits of white goods financial reports create multiple constraints for the vector models and indexing workflow. First, sudden updates to temporary announcements require indexing to support incremental mode, eliminating resource consumption and time delays from full reindexing. Second, operating data across product categories in financial reports has strong correlations. Text chunk splitting must retain semantic integrity, otherwise semantic breaks during vector recall will reduce analysis accuracy. Third, operating data fields vary across product categories. Indexing must retain metadata associations to ensure retrieval results accurately match specific product categories and report periods. Fourth, financial reports include many specialized financial terms. Vector models must adapt to this field’s semantic traits, otherwise semantic recall bias will occur.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Paragraphs for product category operating analysis in white goods financial reports are relatively long. This segmentation range retains the integrity of core semantic units such as revenue and cost for a single product category |
similarity_threshold | 0.72–0.85 | Financial report analysis has high requirements for semantic accuracy. This range filters irrelevant recall results and ensures retrieval relevance |
recall_top_k | Top 10 results | Financial report analysis requires coverage of multi-dimensional operating data. Sufficient recall volume ensures comprehensive information |
incremental_index_enable | Enabled | Adapts to sudden updates to temporary announcements, reducing resource consumption and time delays from full indexing |
index_update_interval | Synchronize temporary announcements once per hour | Matches the disclosure rhythm of temporary announcements, ensuring indexing timeliness for the latest operating data |
vector_model_dim | Follow official specifications of the selected model | Ensures consistency between index vectors and vector model output dimensions, avoiding dimension mismatch errors during retrieval |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, 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 errors
- Vector recall similarity scores fall outside the 0-1 range, even exceeding 10,000. Cause: No normalization is applied to vector model outputs, so similarity calculations are not calibrated to the standard semantic similarity range.
- After importing a financial report dataset, the interface shows an "Indexing" status that persists beyond the preset duration. Cause: No reasonable text segmentation parameters are configured, and long text block processing times out, causing indexing to stall.
- After configuring a custom vector model channel, retrieval requests still call the large language model. Cause: No dedicated call link for the vector model is specified in the retrieval configuration, so the large language model is mistakenly used as the vector retrieval backend.
How to verify correct configuration
- Upload a single financial report sample, check that segmented text block lengths match the configured
chunk_sizerange, and confirm segments retain complete product category operating paragraphs. - Initiate a financial report keyword search, check that returned result similarity scores fall within a reasonable range, and adjust the threshold based on business needs.
- Submit an incremental indexing task for temporary announcements, check that indexing progress updates as expected, with no prolonged stalling.
- After configuring multi-replica deployment, verify index vector consistency across all replicas to ensure no deviation in retrieval results.
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.