What the Data for This Category Looks Like
Data sources for black goods research reports include monthly monitoring reports from the China Household Electrical Appliances Association, public investor relations documents from leading home appliance brands, aggregated offline store POS data, and online e-commerce platform sales rankings. These reports serve as reference materials for financial institutions conducting industry analysis and making investment decisions. Updates follow a regular monthly schedule, with special research reports added during quarterly new product launch cycles. Document structures include both structured fields and free-text analysis sections. Structured fields cover report ID, issuing institution, release time, covered categories (TVs, projectors, audio equipment, etc.), core monitoring data (shipment volume in ten thousand units, average price in yuan), and technical parameter comparisons. The free-text section includes industry trend interpretations and competitive landscape analysis. Individual report lengths range from thousands to tens of thousands of characters. Special research reports include multiple sets of horizontally comparative parameter tables.
Constraints Imposed on Vector Models and Indexes
The coexistence of structured numerical values, parameter fields, and free text in black goods research reports requires vector models to support vectorization of mixed-type text. This avoids semantic loss for structured data. Monthly and quarterly incremental update requirements mean indexes must support incremental synchronization logic. Full reconstruction every time will cause update delays. A large number of technical parameter paragraphs appear in research reports. When segmenting text, complete parameter groups must be retained. Splitting these groups will break context and reduce retrieval accuracy. Monitoring data for different product categories uses exclusive units and terminology. The preprocessing stage must retain the binding relationship between units and exclusive terminology. This ensures accurate vector encoding.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length of technical parameter paragraphs in black goods research reports, avoiding splitting complete specification descriptions and market data paragraphs |
chunk_overlap | 100–150 characters | Retains contextual connections between adjacent segments, avoiding loss of cross-segment parameter comparison information |
index_type | HNSW | Adapts to the high-dimensional vector retrieval needs of research report data, balancing retrieval accuracy and query speed |
retrieval_top_k | Top 8–12 results | Covers segmented category data across different reports, avoiding omission of technical analysis for niche products |
similarity_threshold | 0.72–0.80 | Filters low-relevance general industry text, focusing on black goods-specific technical and market content |
incremental_index_enabled | Enabled | Adapts to monthly and quarterly incremental update requirements, avoiding delays caused by full index reconstruction |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: Index tasks remain queued with no progress feedback in a Docker deployment environment. Cause: Insufficient memory resources are not allocated to the vector index process, causing tasks to fail to start.
- Issue: Authentication failure errors are returned when calling the embedding model, or retrieval results are empty after index generation. Cause: The API key and access endpoint for the embedding model are not configured correctly, or the selected embedding model does not support Chinese text encoding.
- Issue: The specified large model can chat normally, but the vector index has no progress updates. Cause: The embedding model is not separately specified for the index link, and the default called model is incompatible with the index process, causing index task blocking.
How to Verify Proper Configuration
- View the vector index runtime logs to confirm that each uploaded research report document has completed segment parsing, with no parsing failure error messages.
- Enter black goods-specific retrieval keywords to verify that the retrieval results include specific content such as technical parameters and market data for the corresponding category, with no general industry irrelevant text.
- Trigger an incremental index task to confirm that newly uploaded research reports complete index updates within a reasonable time frame, without requiring a full reconstruction of the entire knowledge base.
- Adjust the similarity threshold configuration, observe changes in the relevance of retrieval results, and confirm that the configuration parameters take effect.
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.