What the data for this category looks like
Steel trade research report data comes primarily from public supply and demand data released by industry self-regulatory organizations, collected data from commodity information institutions, and public disclosure documents from steel mills and traders. Update cycles cover multiple timeframes: short-term market documents are updated daily or every two days, monthly analysis documents are updated monthly, and in-depth industry reports are updated quarterly or semi-annually. Document lengths vary widely, from hundreds-of-word transaction bulletins to tens-of-thousand-word in-depth analysis reports. Most documents include these modules: core data summaries, category-specific market trends, regional price spreads, supply and demand balance analysis, and future market forecasts. Core fields include trading variety, trading location, quoted amount, inventory scale, shipment volume, publishing institution, and publishing time. Quoted amount is measured in yuan per ton, inventory scale in ten thousand tons, and shipment volume in tons.
What constraints these characteristics impose on vector models and indexing
The wide range of document lengths, from hundreds-of-word transaction bulletins to tens-of-thousand-word in-depth reports, requires indexing systems to support adaptive document splitting. This prevents information loss for short documents or exceeding vector model context windows for long documents. Documents contain structured numerical fields and unstructured analytical text. This requires vector models to support mixed-field encoding, or separate vectorization of structured fields followed by fusion with text vectors. A high proportion of frequently updated short-term market documents requires indexing systems to support incremental index updates. This reduces resource consumption from full index rebuilds. Dense industry-specific terminology with unique meanings requires vector models to have industry domain adaptation capabilities, or require encoding effect optimization using domain vocabularies.
How to set the configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the context window of mainstream vector models, balances single-chunk information density and recall completeness |
chunk_overlap | 100–150 characters | Compensates for context breaks after document splitting, prevents professional terms or continuous analysis content from being truncated |
recall_top_k | Top 8–12 results | Core information in steel trade research reports is concentrated. Excessive recall introduces redundant data, while insufficient recall risks missing critical signals |
similarity_threshold | 0.72–0.80 | Differentiates steel industry-specific terminology from general text, reduces false recall of non-target research reports |
index_update_strategy | Incremental update | Adapts to the high-frequency update characteristics of steel trade research reports, reduces resource consumption from full index rebuilds |
embedding_dim | 768 dimensions | Adapts to the standard output dimensions of mainstream open-source vector models, balances vectorization accuracy and storage costs |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: A
401 Unauthorizederror is returned when calling the vector model, and the console shows that the request does not carry valid authentication information. Cause: The API key for the vector model is not configured correctly, or the key has insufficient permissions to access the vector model service. - Symptom: After custom document splitting, the actual order of document chunks stored in the index does not match the custom splitting result, and some duplicate chunks are automatically removed. Cause: The system enables global document chunk deduplication by default. Failing to turn off this switch causes duplicate chunks to be filtered out, disrupting the preset order.
- Symptom: After batch uploading steel trade research reports, some documents have empty vector results during index merging. Cause: No vector model adapted to industry terminology is set, or no domain vocabulary is loaded. This prevents professional terms from being correctly encoded, leading to failure of the vectorization process.
How to confirm correct configuration
- Upload a standard-format steel trade research report, verify that the number of parsed document chunks matches the calculation result based on the
chunk_sizeandchunk_overlapconfigurations. Confirm that the segmentation parameters are effective. - Submit a retrieval request for steel products, verify that the number of recall results matches the
recall_top_kconfiguration, and check that the similarity scores of returned results meet the presetsimilarity_thresholdrequirements. - Upload an updated research report, verify that the indexing system only processes new or modified documents without requiring a full rebuild. Confirm that the incremental update configuration is effective.
- View the vector model call logs, confirm that the request carries valid authentication information and no
401 Unauthorizederror occurs. Verify that the model access configuration is correct.
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.