What the data for this category looks like
Carbon steel-related data mainly comes from spot trading platforms, official factory price publication channels of steel mills, and production and inventory statistics from industry monitoring institutions. Update cadence varies by dimension: spot prices are updated daily, monthly production and import/export data is released the following month, and quarterly inventory data is updated at the end of each quarter. Most documents are in structured table format, covering core metrics for subcategories such as rebar, wire rod, and hot-rolled coil. Fields include category name, report period, transaction average price (unit: yuan per ton), daily average output (unit: ton), social inventory (unit: 10,000 tons), and some documents include regional price difference comparison details.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
The multi-dimensional update cadence and structured characteristics of carbon steel data create clear constraints for multi-turn dialogue and prompt engineering configurations. First, update cycles vary significantly across different indicators. Multi-turn dialogue must support users adding restrictions on report periods and data types. Prompts must explicitly require the model to label the release time and update frequency of data. Second, subcategories are abundant and field units are unified. Prompts must guide the model to accurately match the specified steel category, avoiding confusion between price or output data of different categories. Third, structured documents account for a large proportion. Multi-turn dialogue must support triggering field extraction instructions, and prompts must explicitly specify the output format to ensure returned content meets the structured requirements of due diligence reports.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Carbon steel due diligence reports require integrating multi-dimensional historical data. Sufficient context preserves category and time restriction conditions across multi-turn dialogues |
prompt_template | Fixed prefix + user question + data timeliness restriction + category restriction | Carbon steel data has large differences in update frequency and subcategories. Forcing explicit restrictions avoids vague or incorrect responses |
recall_top_k | Top 6–8 entries | Carbon steel data has many sub-dimensions. An appropriate number of recalled entries can cover indicators of different categories and regions, avoiding missing key information |
similarity_threshold | 0.75–0.85 | Carbon steel data fields are highly standardized. A relatively high threshold filters irrelevant non-steel category data and ensures accurate recall |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Carbon steel monthly/quarterly reports contain large amounts of tabular data. A longer timeout ensures complete parsing of structured content |
max_tokens | 2000–3000 token | Carbon steel due diligence reports require detailed indicator explanations and comparisons. Sufficient generation length covers complete analysis 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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Residual
thinktag content appears in generated due diligence reports after running the workflow. Cause: No rule to remove thinking content is configured inprompt_template, or the logic of the code running node does not take effect across the entire workflow. - Phenomenon: Insufficient number of recalled carbon steel data entries from the knowledge base, or returned content does not match the user-specified category. Cause:
similarity_thresholdis set too high or too low, or the value ofrecall_top_kdoes not cover indicator data for subcategories. - Phenomenon: Generated due diligence reports are truncated and cannot output complete indicator analysis. Cause:
max_tokensis set too small, or the model triggers content truncation restrictions during generation.
How to verify correct configuration
- Launch a multi-turn dialogue that includes a specific steel category and time range, and verify whether the indicators returned by the model match the specified category and period.
- Upload a carbon steel industry monthly report, trigger the parsing process, and verify whether the parsed fields fully cover core indicators such as category, price, and output.
- Run a test workflow, check whether the generated report removes
thinktag content and that the output length meets expectations. - Adjust recall parameters, compare recall results under different thresholds, and confirm that the accuracy of returned content meets due diligence requirements.
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.