What the data for this category looks like
Financial report data for publicly traded kitchen and bathroom appliance companies is sourced from regular reports and temporary announcements publicly disclosed by domestic and overseas stock exchanges. Updates occur quarterly, annually, and temporarily triggered by major operating events. Individual financial report documents vary widely in length. They include category-specific revenue breakdowns, channel sales data, production cost breakdowns, R&D investment details, inventory turnover metrics, and other content. Fields cover product categories, revenue amounts, sales quantities, cost breakdown details, and more. Units include Chinese yuan, units sold, operating days, and others.
Constraints on vector models and indexing from these characteristics
The multi-field parallel features of category-specific revenue breakdowns require vector models to support mixed encoding of structured fields and unstructured text. This prevents semantic confusion across different business dimensions. The wide variation in document length requires indexing segmentation strategies to adapt to text blocks of different lengths. This avoids semantic breaks from over-segmentation or redundant context from under-segmentation. The temporary update trigger mechanism requires indexes to support flexible switching between incremental and full updates. This ensures data timeliness. The presence of multiple unit fields requires clear definition of field semantic boundaries during vector modeling. This prevents vector space misalignment caused by unit differences.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Kitchen and bathroom appliance financial reports include long revenue breakdowns and channel analyses. This range preserves semantic integrity while avoiding vector encoding deviations caused by overly long single blocks |
top_k | Top 3–5 results | Financial report analysis focuses on core category data. Too many retrieved results introduce irrelevant information, while too few fail to cover complete analysis dimensions |
similarity_threshold | 0.72–0.85 | Semantic differences between revenue data of different categories must be distinguished. A threshold that is too low introduces irrelevant category retrieval results, while a threshold that is too high may miss information from relevant sub-categories |
enable_incremental_index | Enabled | Financial reports have scenarios with temporary updates. Incremental updates reduce resource consumption from full index reconstruction |
embedding_model | Calibrated for business scenarios | Kitchen and bathroom appliance financial reports include structured fields and unstructured text. Vector models adapted for mixed encoding are required |
index_batch_size | 200–300 items | Adapts to the rhythm of batch uploading financial report documents, avoiding timeouts from single-batch index operations |
The parameter values provided on this page are common 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 errors
- Retrieval results return abnormally high similarity values outside the normal threshold range. This occurs when the
similarity_thresholdparameter is not configured, or when the parameter value does not match the output range of the current vector model. This leads to ineffective filtering of retrieval results. - Knowledge base uploaded documents show incomplete indexing, or the
m3eoption is not present in the vector model selection list. This occurs when the service address and key configuration for the corresponding vector model are not completed in system settings. This prevents normal invocation of the model. - Timeout errors occur during index construction, returning a
504status code. This occurs when theindex_batch_sizeparameter is not adjusted. The number of documents in a single batch of index operations exceeds the system resource capacity limit, leading to interruption of the index process.
How to confirm proper configuration
- Upload a single kitchen and bathroom appliance financial report document. Check the knowledge base index progress page to confirm the index status shows as completed.
- Enter a retrieval term related to a product category. Check the similarity value range of retrieval results to confirm the values fall within the threshold range of the current configuration.
- Trigger a simulated temporary update operation. Check if the index only updates newly added or modified document content, and does not perform a full reconstruction.
- Check the vector model invocation logs to confirm the selected model normally returns encoding results with no error messages.
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.