What Data for This Category Looks Like
The data for steel trade intelligent due diligence reports primarily comes from factory quality certificates issued by steel mills, road and railway logistics waybills, port manifests, bank settlement vouchers, and industrial and commercial public information of upstream and downstream enterprises. Data update frequency follows trade batches; documents for a single trade are synchronized and updated after fulfillment. Most documents are structured tables paired with scanned attachments. Core fields include the trader’s unified social credit code, steel grade, specification and model (such as Φ16mm HRB400), transaction quantity (unit: ton), settlement unit price (yuan/ton), delivery location, payment cycle, and some documents also include the number and results of third-party quality inspection reports.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Professional fields and specialized units for steel trade due diligence data require multi-turn dialogue to guide users to clarify field references, preventing confusion between parameters of different steel specifications. Long documents containing multi-stage waybills require multi-turn dialogue to retain sufficient context for associating trade data across different batches. Prompts must limit output scope to stop the model from generating irrelevant content. Decentralized data that requires verification across each stage requires multi-turn dialogue to gradually guide users to supplement missing document information. Prompts must clearly mark core fields for verification. Data update frequency varies by trade batch, requiring prompts to prioritize the most recently uploaded document data.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Steel trade due diligence report documents are lengthy, requiring sufficient context to link professional fields and stage information across multi-turn dialogue |
chunkSize | 1000–1500 characters | Steel trade documents have numerous fields and professional specifications. Segments that are too long disrupt field associations, while overly short segments lose the contextual logic of upstream and downstream trade |
similarityThreshold | 0.75–0.85 | Low-match, non-steel trade data must be filtered, while similar documents of the same category must be retained to support due diligence analysis |
reRankTopN | Top 3–5 results | Steel trade document data has strong relevance; a small number of highly matched documents can cover information needs for core due diligence stages |
toolCallMaxTurn | 3–5 times | Steel trade due diligence requires gradual verification of multi-stage data including qualifications, logistics, and settlement. Multiple tool calls can supplement missing document information |
promptTemplate | Only generate due diligence reports based on uploaded steel trade document data. Clearly mark field units. Prompt for supplementary corresponding documents if data is missing | Restrict model output scope, adapt to unit requirements for professional steel trade fields, and avoid generating unsubstantiated content |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Obvious discrepancy between tool call results and native platform dialogue effects. Cause: The
promptTemplatehas not been adjusted for steel trade professional fields, causing the model to fail to correctly identify specialized terms such as steel grades and specification models. - Phenomenon: Workflow dialogue logs show no content. Cause: The
logConversationconfiguration item is not enabled, ormaxContextis set too small, causing context to be truncated early and logs to fail to generate properly. - Phenomenon: Model testing works normally in a docker deployment environment, but workflow calls fail. Cause: The API key of the corresponding large model is not bound in the workflow node, or
toolCallMaxTurnis set beyond the context cache threshold limited by container memory.
How to Verify Proper Configuration
- Upload a real steel trade document, trigger multi-turn dialogue, and check whether the model can correctly identify specialized fields such as steel grades, specification models, and quantity units.
- View the workflow dialogue log panel to confirm that multi-round interaction context and tool call records are generated normally.
- Adjust the
similarityThresholdvalue, test recall results under different thresholds, and confirm that irrelevant data is filtered out and valid document information is retained. - Simulate a dialogue scenario with missing fields, and check whether the model prompts for supplementary corresponding document information and generates substantiated content.
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.