What This Category of Data Looks Like
Industrial metals investment research data primarily comes from domestic and overseas futures exchanges, industry associations, spot trading platforms, and professional research report institutions. Futures contract quotes update in real time during trading days. Spot quotes update daily. Industry supply-demand and policy data is released weekly or monthly.
Documents are divided into three categories: structured quote tables, unstructured industry research reports, and policy documents. Fields include product name, origin, transaction benchmark price, trading volume, and more. Units are uniformly set as yuan/ton, USD/ton, ten thousand tons, and number of trading lots.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The multi-source heterogeneous, high-frequency updated, and unit-diverse characteristics of industrial metals data impose multiple constraints on multi-turn dialogue and prompt engineering workflows.
Multi-source data includes structured quotes and unstructured research reports. Prompts must clearly distinguish the call priority and format requirements for the two types of data. The high-frequency update feature requires the dialogue system to support real-time knowledge base synchronization. During multi-turn dialogue, users must be guided to clearly specify the data time range and update node.
Domestic and overseas data have unit differences such as yuan/ton and USD/ton. Prompts must mandate the model to uniformly convert output units to avoid confusion. Parameter differences vary widely across different products. Multi-turn dialogue must gradually guide users to clarify key information such as product name and specifications to narrow the query scope.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
ragRecallNum | 8–12 results | Industrial metals data is mostly a mix of structured and unstructured content. A range of 8–12 results can cover core supply-demand and quote information while avoiding context overload |
ragSimilarityThreshold | 0.75–0.85 | Industrial metals data has high requirements for field accuracy. This range filters low-relevance general industry content while retaining core quote and research report materials |
maxContext | 8000–12000 characters | Industrial metals investment research dialogues often involve multi-dimensional data comparisons. This range can carry historical interaction information and recalled data from multiple rounds of exchanges |
parseMaxSize | 500 MB | Industry research reports and supply-demand statement files are often large in size. This value covers most standard industrial metals documents |
systemPrompt | Preset template tailored for industrial metals investment research scenarios | Must clearly require the model to prioritize calling recalled data, unify output units, and guide users to supplement product and time information |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Misconfiguration Issues
- Symptom: The model's output language does not match the requirements specified in the preset prompt. For example, an English preset prompt requires English output, but the final reply only uses Chinese. Cause: The output language rule is not clearly specified in the
systemPrompt, and language consistency constraints are not maintained across multi-turn dialogue sessions. - Symptom: After uploading industrial metals supply-demand statements or quote tables, the parsed results show missing fields or mixed units. Cause: Structured file field mapping configuration is not enabled, or the parsing template is not adapted to the fixed column structure of industrial metals documents.
- Symptom: The quote data returned by the model during multi-turn dialogue does not match the user-specified time range. For example, a query for the same day's copper price returns a weekly average price. Cause: The
ragSimilarityThresholdis set too low, resulting in recalled expired data, or the prompt does not require the model to verify that the data timestamp matches the user's request.
How to Verify Proper Configuration
- Upload an industrial metals spot quote table, check the parsed results for field completeness and unit consistency, and adjust the corresponding configuration until the expected outcome is achieved.
- Initiate a query that includes product name and time range, verify that the data returned by the model matches the user-specified parameters, and adjust the recall and similarity configuration until the requirements are met.
- Conduct three consecutive progressive queries, such as first querying copper prices, then querying aluminum prices for the same period, and finally comparing the two, confirm that the context can carry historical interaction information, and adjust the context window configuration until the requirements are met.
- Review the application call logs to confirm that the system prompt is correctly loaded, and each reply follows the preset data filtering and format rules.
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.