What data for this category looks like
Data sources for this category include real-time quotes from spot trading platforms, publicly released documents of steel mill ex-factory prices, weekly port inventory reports, trader purchase-sale-stock ledgers, customs import and export declaration data, and monthly research reports from industry associations. Update rhythms vary significantly: spot quotes are updated in real time on trading days, port inventory data is updated weekly, industry research reports are released monthly, and internal purchase-sale-stock ledgers are entered in real time alongside transactions. Document types include structured Excel purchase-sale-stock ledgers, long-text PDF research reports, and CSV bulk quote lists. Fields include product names with specifications (for example, rebar HRB400 φ16mm), origin, unit price, inventory volume, and transaction volume. Most units are yuan/ton and ten thousand tons.
Constraints on deployment and upgrade
Mixed data structures and differentiated update rhythms create multiple constraints for deployment and upgrade. Pre-set parsing rules must be configured for multi-format documents to support structured ledger field extraction and segmentation of long-text research reports. Real-time updated spot quotes require incremental synchronization mechanisms to avoid resource consumption caused by full reprocessing. Entity recognition configuration must be enabled for product name fields with specifications to ensure accurate matching of trading categories during recall. The upgrade process must support new data source formats while maintaining the stability of existing parsing logic, to avoid impacting connected historical data.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports bulk import of large purchase-sale-stock Excel files, multi-page industry research reports, and similar documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents interruptions during parsing of ultra-long PDF research reports and large CSV quote lists |
Segment Length | 800–1200 characters | Adapts to semantic coherence of long-text industry research reports in the steel sector, while controlling single-segment recall granularity |
Recall Count | Top 8 entries | Covers effective recall ranges for multiple data sources including spot quotes, inventory data, and industry research reports |
Similarity Threshold | 0.72–0.80 | Filters low-relevance non-steel sector data, retaining accurate trade-related information |
Incremental Sync Interval | 15 minutes | Adapts to real-time update requirements for spot quotes, balancing resource usage and data freshness |
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 against local samples is recommended before finalizing settings.
Three common mistakes
- Symptom: A
token encodererror occurs when calling the model after deployment, and the service restarts indefinitely. Cause: Environment variables for model calls are not configured correctly, leading to failure to load encoding dependency packages for the corresponding model. Additionally, the Docker mount path does not correctly map the model cache directory. - Symptom: Knowledge base responses deviate from the steel trading scenario, and field matching for Excel purchase-sale-stock ledgers is incorrect. Cause: Entity extraction configuration is not enabled for product name fields with specifications specific to the steel sector. Segment length settings are unreasonable, leading to semantic fragmentation. Recall threshold is set too low, resulting in inclusion of irrelevant data.
- Symptom: The content extraction module fails to extract specification and unit price fields from steel ledgers after being enabled, returning null values. Cause: Custom field extraction rules are not configured. The default extraction rules do not cover steel trade-specific fields including product names with specifications and units such as yuan/ton, leading to extraction failures.
How to confirm proper configuration
- Upload a typical steel purchase-sale-stock ledger Excel file, and check if the parsed fields include product name, specification, unit price, and inventory volume to confirm that the parsing rules are effective.
- Trigger an incremental synchronization task, check the system logs to confirm that only newly added data source data is synchronized, and no full reprocessing is performed.
- Initiate a query targeting the steel trading scenario, and check if the relevance of recall results meets the preset similarity threshold requirements.
- Test the content extraction module by uploading a steel industry research report, and confirm that the extracted core business fields meet expectations.
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.