What the data for this category looks like
Paint and ink industry research report data sources include public statistical materials from the China Coatings Industry Association, chemical industry research reports from securities firms, annual and quarterly reports of listed coating enterprises, and data from commodity supply chain trading platforms. Update cycles are as follows: association data is updated quarterly, securities firm research reports are released on demand alongside industry trends, and enterprise reports are updated annually or semi-annually. The document structure of a single research report includes industry overview, core raw material price trends, production and sales data for segmented categories, downstream application proportions, policy compliance requirements, and trend forecasts. Core fields and units include raw material unit price (yuan/kg), production capacity (tons), shipment volume (tons), compliance standard numbers, limit requirements, and similar content.
What constraints do these characteristics impose on the vector models and indexing link?
The multi-source dispersed nature and differentiated update cycles of paint and ink research reports require vector indexes to support incremental data synchronization, avoiding resource consumption caused by full reconstruction. The mixed unit characteristics of fields require the preprocessing link to complete unit conversion, or annotate field attributes when inputting to the vector model to adapt to mixed data. Research reports contain both quantitative indicators and qualitative analysis content, so the index needs to balance semantic vector recall and precise field matching capabilities. Long-text industry trend analysis fragments require a reasonable segment length to avoid semantic fragmentation that affects recall accuracy. Some policy documents contain fixed-format compliance clauses, so the index needs to support weight tuning for specific fields to improve the recall priority of core information.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Core paragraphs of paint and ink research reports are mostly 500-1500 characters, this range preserves complete semantic units |
chunk_overlap | 100–150 characters | Prevents key information from being truncated after segmentation, ensures semantic coherence between adjacent segments |
embedding_model | bge-large-zh-v1.5 | Adapts to semantic understanding of Chinese chemical industry terminology, improves recall accuracy for professional content |
top_k_retrieval | Top 8–12 results | Core relevant information in paint and ink research reports is usually scattered across 3-5 documents, this range covers valid results while reducing redundancy |
similarity_threshold | 0.72–0.78 | Semantic similarity threshold for chemical industry terminology needs to be higher than general scenarios, avoids recalling irrelevant research reports |
incremental_index | Enabled | Research reports are updated quarterly or on demand, incremental indexing reduces resource consumption from full reconstruction |
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 case-by-case analysis, and testing on local samples is recommended before finalizing settings.
Three common errors
- Phenomenon: After calling the chunk-mode pushdata API to upload research reports, the interface continuously displays "Indexing" status and finally returns no data. Cause: The
incremental_indexconfiguration is not enabled, or the field format of the uploaded data does not match the preset structured rules, causing the indexing task to get stuck. - Phenomenon: Retrieved results are irrelevant to the paint and ink theme, with overall poor relevance. Cause: A general-domain embedding model is used, and the
similarity_thresholdis not adjusted to a reasonable range for chemical scenarios, leading to incorrect recall of non-professional content. - Phenomenon: The last segment of some long documents fails to complete indexing, and the interface returns a parsing failure prompt. Cause: The
chunk_sizeis set incorrectly, the character count of the last segment exceeds the model input limit, orchunk_overlapis not configured, causing segment information to break.
How to confirm the configuration is correct
- Upload a single test research report, check the running logs of the indexing task to confirm there are no errors related to segment parsing or vector generation.
- Input professional terminology of the paint and ink industry for retrieval, verify that the theme of returned results is directly related to the target category.
- Upload incrementally updated research report data, confirm that the indexing task only processes newly uploaded files and does not trigger full index reconstruction.
- Adjust retrieval-related parameters, compare the number of recall results under different configurations, confirm that the parameter values match the business scenario.
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.