What Data for This Category Looks Like
Data for this category originates from public periodic reports of listed domestic small home appliance entities, and publicly available industry supply chain monitoring data. Quarterly reports are released every quarter, annual reports once per year. Each financial report document includes four core sections: operating data, product structure, channel analysis, R&D and supply chain. Each section contains structured tables and written explanations. Core fields include small home appliance segment revenue, raw material procurement costs, online channel sales revenue, and inventory turnover days. Corresponding units are Renminbi yuan, Renminbi yuan, Renminbi yuan, and days, respectively.
Constraints for Vector Models and Indexing
Small home appliance financial reports have numerous structured subfields. Independent vector index branches must be configured for each subfield to prevent semantic confusion across fields. The quarterly update rhythm requires indexes to support low-frequency batch updates. Full-rebuild high-frequency refresh strategies are not suitable. Documents contain both short structured numeric fields and long analytical text. Vector models must balance encoding performance for both short text and long context. Cross-section associated data, such as revenue and channel data, must support joint indexing to improve retrieval accuracy. Field units must be standardized before indexing to ensure consistent vector encoding.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vector_model | bge-large-zh-v1.5 | Adapts to encoding needs for short structured fields and long analytical text in Chinese financial reports, supports batch embedding tasks |
chunk_size | 800–1200 characters | Structured table segment data in small home appliance financial reports is mostly under 500 characters per segment, while long analytical text paragraphs are around 1000 characters. This range balances segmentation completeness |
index_refresh_interval | 7 days | Adapts to the quarterly update rhythm of small home appliance financial reports, balances data timeliness and resource consumption from index rebuilding |
retrieve_top_k | Top 10 results | Covers retrieval needs for multiple subfields in small home appliance financial reports, avoids excessive redundant fragments interfering with results |
similarity_threshold | 0.72–0.78 | Filters low-relevance redundant fragments, adapts to the high semantic similarity characteristics of structured fields in small home appliance financial reports |
index_mapping_field | content and metadata.source_type | Uses text content and source type as core index fields, supports filtering retrieval results by report type |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: Connection timeout error with 500 status code occurs when integrating a vector model after Docker deployment. Cause: Network interconnection parameters for the vector model service were not configured in docker-compose.yml, preventing the main service from accessing the index model container.
- Issue: The exported knowledge base dataset.csv only contains the index field and no content field. Cause: The content field mapping configuration during knowledge base export was not enabled. Only index metadata was exported, not the original text content.
- Issue: Embedding failure error occurs when importing text using the
bge-largevector model. Cause: Thechunk_sizeparameter was not adjusted to adapt to the text length of small home appliance financial reports. The default segment length exceeds the model's maximum context limit.
How to Verify Proper Configuration
- Upload a single test document of a small home appliance financial report, check the vector embedding task logs, and confirm that the vector model loads normally and the embedding process has no errors.
- Enter specified retrieval keywords, verify that the number of recalled results matches the set
retrieve_top_kvalue, and that the result fields match the configured index mapping fields. - Export the knowledge base dataset file, check that it contains the original text content and corresponding metadata, and confirm that the export configuration meets expectations.
- Check the scheduled task logs of the index service, confirm that the index refresh cycle matches the set
index_refresh_intervalvalue.
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.