What the data for this category looks like
Energy metals data is sourced from commodity market service platforms, public reports from industry associations, and production and sales information publicly disclosed by upstream manufacturing enterprises. The update schedule follows multiple tiers: spot trading prices are updated daily, weekly inventory and monthly production data are updated on a weekly or monthly basis, and quarterly industry supply and demand reports are released quarterly. Each individual data entry includes fields such as product name, origin specification, same-day spot quotation, weekly average price, monthly cumulative output, and total social inventory. Unified units include yuan/ton, ton, or ten thousand tons, and each data entry includes a data collection timestamp.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-tiered update schedule for energy metals data requires multi-turn dialogue to clearly define the time dimension of the data the user needs, to prevent calling outdated information. The fields cover multiple specification and statistical dimension types. Prompts must guide users to progressively clarify parameters including product, origin specification, and statistical cycle; failure to do so may lead to mismatches between quotations and user demand. Unit information attached to each data entry must be explicitly verified during dialogue to avoid confusion between units such as yuan/ton and ten thousand yuan/ton. Additionally, frequently updated spot price data consumes significant context space, so the length of historical data retained in the context window must be limited to avoid redundant information interfering with current queries.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6000–10000 token | A single energy metals market data entry occupies approximately 200-300 tokens. Retaining the last 5 rounds of dialogue context covers consecutive query needs and avoids interference from redundant old data |
systemPrompt | Must explicitly ask the user for the required energy metals product, specification, statistical cycle, and unit requirements. Supplement questions when information is unclear | Energy metals have many fields and significant specification differences. Guiding users to supplement key parameters avoids returning incorrect information |
toolCallTimeout | 30 seconds | Commodity data source interface responses typically take 10-20 seconds. Setting a 30-second timeout covers network fluctuation scenarios and avoids dialogue timeout interruptions |
maxToolCalls | 2–3 times | Routine queries for energy metals only require 1-2 tool calls to obtain market and inventory data. Excessive calls increase user waiting time |
responseFormat | Fixed field template including product, specification, quotation, statistical cycle, unit | A standardized return format allows users to quickly obtain key information and avoids format confusion |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: AI dialogue responses time out, and the interface shows a "request timeout" status code. Cause: A reasonable
toolCallTimeoutparameter is not configured, or interface request logic is not optimized when calling commodity data sources, leading to waiting times that exceed expected thresholds. - Phenomenon: After calling the chart tool to generate an energy metals price trend chart, the interface displays a blank screen. Cause: The system prompt does not explicitly require the chart tool to include complete time range and product parameters when called, resulting in missing necessary data source filter conditions during tool invocation and failure to generate valid charts.
- Phenomenon: The AI dialogue node returns empty content or a "tool call failed" error message. Cause: No exception handling logic is included in the system prompt. When the data source interface returns empty data, the system fails to guide the user to adjust query conditions or supplement information, and directly returns empty results.
How to confirm the configuration is complete
- Initiate a query with multiple dimensional parameters, verify that the system actively asks follow-up questions for unclear information, and confirm that the prompt configuration is active.
- Initiate consecutive multi-round related queries, verify that the context retains previously mentioned key parameters such as product and specification, and that no parameter confusion occurs.
- Trigger a tool call scenario, verify that the response duration meets business requirements, and confirm that the timeout configuration is appropriate.
- Simulate a scenario where the data source returns empty data, verify that the system returns clear guiding prompts, and does not return empty content or unhandled errors.
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.