What the data for this category looks like
The data for this category primarily comes from monthly supply and demand reports from national industry associations, weekly warehouse receipt and price data from domestic commodity exchanges, and brokerage firm basic chemical sector research reports. Update frequency ranges from weekly to monthly. Some emergency market research reports are updated in real time. Most documents are a mix of structured and semi-structured text. They contain fields such as product grade, production volume, import volume, and price index. Common units include tons, yuan/ton, and 10,000 tons per month. Document lengths vary widely. Configuration should be determined based on in-house sample statistics or actual testing.
Constraints for Vector Models and Indexing
The heterogeneous multi-source data and mixed structured/semi-structured text characteristics of this category impose multiple constraints on the vector model and indexing workflow. First, compatible field formats from different sources must be supported. Normalization processing must be applied to information such as product grades and pricing units to prevent entity mismatches during retrieval. Second, the weekly to monthly high-frequency update requirement demands index support for incremental construction and updates. This reduces resource consumption from full reindexing. Third, the wide range of single-document lengths requires adaptable segmentation rules. This avoids truncation of core supply and demand data and price information. Fourth, there is a high demand for precise entity matching. The vector model must balance semantic understanding and industry entity recognition capabilities.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Plastics and rubber research reports contain numerous structured tables and long-text analysis. This range balances semantic completeness and retrieval accuracy, avoiding overly dispersed single-segment information. |
chunk_overlap | 150–200 characters | Retains contextual association after long-text segmentation. This ensures continuous information such as supply and demand balance sheets and price trends is not truncated. |
embedding_model | bge-m3 (or industry-grade vector models of equivalent level) | This model’s ability to recognize chemical industry entities and semantic associations aligns with the retrieval needs of research reports for this category. |
retrieval_top_k | Top 8–12 results | Research report content has high segmentation granularity. Excessive recall introduces irrelevant information. Insufficient recall fails to cover multi-dimensional analysis. |
enable_structured_filter | Enabled | This category’s data includes clear structured fields such as product grades and price ranges. Filtering can narrow the retrieval scope to improve relevance. |
index_refresh_interval | 1 hour | Weekly updated warehouse receipt data and emergency market research reports require timely synchronization. This avoids index data lag. |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to determine configuration based on actual testing on in-house samples.
Three Common Configuration Errors
- Phenomenon: When using the
bge-m3vector model, retrieval similarity scores are generally higher than 0.9. Some irrelevant documents are prioritized in recall. Cause: No adaptation processing has been applied to plastics and rubber industry entities such as product grades and pricing units. The model’s recognition of semantic boundaries for industry terminology is insufficient, leading to overly high similarity scores. - Phenomenon: After index construction is complete, no matching results are returned during retrieval. The backend log shows the
index_emptystatus code. Cause: No normalization processing has been applied to structured fields. "LLDPE" and "linear low-density polyethylene" from different sources are identified as different entities, causing filter conditions to fail to match. - Phenomenon: All results returned via the retrieval interface have similar relevance. It is impossible to distinguish core research reports from auxiliary analysis content. Cause: The
retrieval_top_kvalue is too large. Structured filtering is not enabled, resulting in a large volume of low-relevance background information being recalled simultaneously.
How to Verify Proper Configuration
- Upload a single typical research report containing product grades and price data. Check the segmented text blocks. Confirm that core information is not truncated and segment overlaps cover adjacent key content.
- Enter precise industry queries such as "LLDPE 7042 monthly price trends". Verify that retrieval results prioritize documents containing the corresponding entities. Confirm that structured filtering rules are functioning normally.
- Upload a new weekly warehouse receipt data document. Trigger incremental indexing. Confirm that the new document can be retrieved within the configured refresh cycle.
- Adjust the vector model’s similarity threshold. Verify that relevance filtering of retrieval results meets business requirements.
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.