What the Data for This Category Looks Like
Carbon steel industry research reports used for financial investment analysis draw data primarily from authoritative domestic steel industry statistical institutions, publicly disclosed information from listed steel enterprises, and public research results from industry research institutions. Updates follow a weekly regular release schedule, with ad-hoc reports added alongside industry policy adjustments or raw material price fluctuations. Each document follows a fixed structure with three sections: core data summary, supply and demand analysis, and future market outlook. Core fields include spot/ex-factory prices (unit: yuan/ton) for categories such as rebar and hot-rolled coil, monthly production volume (unit: 10,000 tons), and total social inventory (unit: 10,000 tons). No additional percentage-based statistical items are included.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The multi-source and scattered nature of carbon steel research reports requires prompts to clearly specify data retrieval priority and source scope, avoiding cross-category data confusion that could skew financial analysis conclusions. The high-frequency update attribute requires multi-turn dialogue to automatically retain the most recently retrieved research report nodes, preventing outdated data from interfering with investment decisions. The clear unit field requirements mean prompts must mandate that output results include corresponding units, avoiding analysis errors caused by mixed units for prices, production volumes, and other metrics. The fixed document structure and multi-category fields require multi-turn dialogue to gradually guide users to specify specific carbon steel sub-categories, narrowing the retrieval scope to improve analysis accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single carbon steel research reports have a relatively long average length, requiring sufficient context to support multi-turn follow-up questions and complete analysis |
recallTopK | Top 6–8 entries | Carbon steel research reports include multi-dimensional data such as prices, production volumes, and inventories, requiring a sufficient number of retrieved entries to cover core analysis dimensions |
similarityThreshold | 0.75–0.85 | Carbon steel industry terminology is highly specialized, requiring a high matching threshold to filter out research report content from non-target categories |
promptTemplate | Must explicitly specify carbon steel sub-categories, output units, and time range for retrieved research reports | Significant differences exist between carbon steel sub-categories; templates must constrain the boundaries of queries and outputs to meet financial analysis requirements |
autoRefreshRecall | Enabled | Carbon steel research reports are updated at a high frequency, requiring real-time retrieval of newly released industry analysis content to ensure data timeliness |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The AI dialogue node returns the
chat:LLM_model_response_emptystatus code with no output content. Cause: No reasonablesimilarityThresholdparameter is configured, and the retrieved carbon steel research reports have insufficient matching with the query requirements, preventing the large language model from generating valid analysis content. - Symptom: Non-standard quotation marks appear at the end of AI dialogue output, which are automatically converted to standard quotation marks afterward. Cause: The prompt template does not explicitly specify punctuation specifications, leading the large language model to use output formats not common in the industry.
- Symptom: Binding global variables in the workflow without limiting them to carbon steel-specific statistical fields, resulting in returned data that does not match the target category. Cause: The prompt does not explicitly define the call scope of global variables, leading to confusion between statistical indicators of different steel categories.
How to Verify Proper Configuration
- Trigger a single-turn query for a carbon steel category, check whether the retrieved research report content matches the target sub-category, and adjust
recallTopKandsimilarityThresholduntil the results meet expectations. - Initiate consecutive multi-round follow-up questions, check whether the context retains the previous round's query conditions and retrieved data, and confirm that the
maxContextsetting supports the complete dialogue flow. - Review the large language model call logs, check whether the output content includes the units of corresponding fields, and adjust the
promptTemplateuntil the format meets requirements. - Test the global variable binding operation, check whether the returned carbon steel data matches the preset statistical fields, and adjust the variable binding scope until it matches the target category.
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.