Model Integration and Configuration for Steel Trade

What data for this category looks like

Data sources for steel trade intelligent due diligence include steel mill ex-factory price databases, regional spot trading platforms, logistics carrier waybills, customs import and export declarations, trader inventory and sales ledgers, and industry association supply and demand reports. Update frequencies vary across sources: spot prices update daily, waybills and declarations update weekly, inventory and sales ledgers sync daily, and industry reports update monthly.

Documents are split into two categories: structured fields and unstructured attachments. Structured fields include steel category, specification model, price per ton, inventory, logistics mileage, and similar items, with units mostly tons, yuan per ton, and kilometers. Unstructured attachments include scanned purchase-sale contracts, quality inspection reports, and similar items, with individual attachments reaching tens of thousands of characters in length.

What constraints do these characteristics impose on model integration and configuration?

The multi-source, multi-unit, and long-document nature of steel trade due diligence data creates clear constraints for model integration and configuration.

Structured data contains unit inconsistency issues, such as coexisting tons and kilograms, or yuan per ton and yuan per kilogram. This requires integrated reranking models to support semantic alignment of multi-unit numerical values.

Unstructured attachments are mostly long documents. The configured context window must adapt to text parsing of tens of thousands of characters to avoid truncating key clauses.

Update frequencies differ widely across data sources. Differentiated incremental sync scheduling parameters must be configured to balance real-time performance and system resource usage.

Steel trade due diligence data involves associated analysis of upstream and downstream industrial chains, which raises higher requirements for the model's context association capability. Model recall and reranking parameters must be adjusted to ensure effective integration of multi-dimensional data and avoid bias from single data sources.

Recommended Configuration Settings

Configuration Item	Recommended Value	Rationale
`reranker_model_path`	`/data/models/bge-reranker-v2-m3`	Adapts to text semantic alignment requirements for steel trade due diligence. This model's matching accuracy for structured numerical values and long text meets scene requirements
`max_context_length`	`8192 characters`	Adapts to parsing requirements for long documents such as purchase-sale contracts and quality inspection reports, avoiding truncation of key clauses
`reranker_top_k`	`Top 5–8 results`	Steel trade due diligence requires associating multi-dimensional upstream and downstream data. 5-8 recall results cover core associated dimensions
`SYNC_INTERVAL_MINUTES`	`30 minutes`	Balances real-time performance for spot prices and system resource usage, adapting to daily updated spot data sync requirements
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Adapts to parsing durations for large attachments such as purchase-sale contracts and customs declarations, avoiding timeout failures
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Adapts to upload requirements for large-volume purchase-sale contracts and customs declarations in steel trade scenarios

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to conduct actual tests on your own samples before finalizing settings.

Three Common Mistakes

After calling the bge-reranker model, GPU video memory usage stabilizes at 6-7 GB, with usage present on every available GPU. Cause: The reranker_batch_size parameter is not set. The default batch processing scale is adapted for general scenarios and cannot meet multi-document parallel parsing requirements for steel trade due diligence.
When configuring domestic mainstream large models, invalid token errors are returned, while oneapi interface tests run normally. Cause: The CUSTOM_HEADERS parameter is not correctly configured in FastGPT's model configuration. Some domestic models require additional authentication headers.
After adding domestic mainstream large models via the oneapi channel, the corresponding model option does not appear in the application creation interface. Cause: FastGPT's model cache has not been refreshed, or the enabled_models field in the channel configuration does not include the unique identifier of the corresponding model.

How to Confirm the Configuration Is Complete

Enter the model management interface, view the list of integrated domestic large models, and confirm that the target model is displayed and in an available state.
Upload a steel trade purchase-sale contract to trigger a parsing task, and check that no timeout or format error prompts appear in the parsing logs.
Initiate a due diligence report generation task, and confirm that the number of returned reranking results matches the configured reranker_top_k value.
View the GPU monitoring panel, and confirm that the reranker model's video memory usage stabilizes in a reasonable range, with no abnormal spikes.

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.