What Data for This Category Looks Like
Core data for steel trade comes from mill inspection certificates, road and sea logistics waybills, trade purchase and sales contracts, customs declarations, and industry reference price directories. Data update frequency follows three tiers: spot transaction prices are updated daily, contract ledgers are synchronized weekly, and credit assessment materials are updated monthly. Most documents are structured tables with nested unstructured annotations. Core fields include material grade, specification and size, settled tonnage, tax-included unit price, delivery cycle, and payment terms. Common units are tons, yuan per ton, and workdays.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The multi-dimensional structured characteristics of steel trade data require the retrieval process to support both precise field matching and semantic-related recall. This prevents field matching deviations caused by relying solely on semantic recall. Frequently updated spot price data requires limiting the recall range to recent valid intervals, to prevent referencing expired quotes. The nested table structure in long documents requires retaining complete row groups during segmentation, to avoid splitting that breaks business logic connections. Additionally, differences in field units across multi-source heterogeneous data require unit normalization verification before retrieval, to ensure business consistency of recall results.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 10-15 results | Steel trade due diligence requires covering multi-dimensional information such as quotes from multiple suppliers and logistics timelines. Too few results cannot meet full reference needs, while too many will increase context redundancy. |
Similarity Threshold | 0.75-0.85 | Most steel trade fields are technical terms. A threshold that is too low will introduce irrelevant supplier data, while a threshold that is too high may miss compliant quotes for the same category. |
Segment Length | 800-1200 characters | Most steel trade documents contain long tables and business annotations. This range retains complete logical connections between table row groups and clauses. |
Incremental Update Trigger Threshold | File modification time difference ≥24 hours | Spot prices are updated daily. This ensures the latest quote data is synchronized in a timely manner, preventing recall of expired information. |
hnsw.max_scan_tuple | Calibrated via actual testing | This parameter controls the scan range for vector retrieval. When steel trade data volume is large, adjust this value to balance retrieval speed and recall completeness, and avoid configuration errors. |
Semantic Reference Limit | No more than 60% of total recall count | Due diligence reports need to rely on both structured field matching and semantic association. Over-reliance on semantic recall will overlook accurate contract clause data. |
The parameter values provided on this page are general recommendations for starting configuration points. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: Knowledge base search returns error
invalid configuration parameter name "hnsw.max_scan_tuple". Cause: An unsupported parameter name for the current vector engine was used, and the configuration did not align with the version specifications of the deployment environment. - Symptom: Half of 100 uploaded 5KB files fail during training, and automatic deduplication does not work when re-uploading. Cause: The knowledge base's file hash verification function was not enabled, and the automatic duplicate file skip logic was not configured, leading to redundant vectors generated by duplicate training.
- Symptom: The proportion of semantic retrieval references is too high, and irrelevant non-steel category data appears in due diligence reports. Cause: The
Semantic Reference Limitparameter was not set, causing semantic recall to cover non-target documents outside the business scope.
How to Verify Configurations Are Correct
- Submit a retrieval request for steel trade technical terms, check the field matching degree of returned results, and adjust the
Similarity Thresholdto a range suitable for the business scenario. - Upload a single test document and trigger training, review backend logs to confirm no configuration-related errors, and verify the legality of parameter configurations.
- Batch upload multiple similar trade contract documents, check whether the knowledge base automatically filters duplicate content, and confirm that the deduplication logic is enabled.
- Calculate the average time taken for multiple consecutive retrievals, adjust vector retrieval-related parameters, and keep the time within a range acceptable for the business.
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.