What the data for this category looks like
Energy metals data sources include public industry statistical bulletins, General Administration of Customs public import and export data, spot trading platform quotes, and publicly disclosed information from mining enterprises. Update cycles vary significantly: spot price data updates daily, monthly supply and demand data updates monthly, and annual capacity planning data updates quarterly. Documents use structured tables as their core carrier, with accompanying text analysis. Fields include metal category identifier, transaction price, inventory scale, and import and export quantity. Units include yuan/ton, ten thousand tons, and similar units.
What constraints these characteristics impose on model access and configuration
The multi-source and multi-update-frequency characteristics of energy metals data require the model access link to support configurable scheduled pull tasks, adapting to the update cycles of different data sources. The diversity of field units requires configuration support for unit normalization rules for structured data, to avoid numerical comparison errors during model processing. The mixed long and short text document structure requires flexible adjustment of the context window parameter, to avoid truncation of long-term planning data or real-time market data. Some offline files have large sizes, requiring configuration support for extended parsing timeout and larger file upload limits.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Energy metals due diligence reports include long-term supply and demand analysis and real-time spot market conditions, requiring adaptation to mixed long and short text inputs to avoid truncation of critical data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Some energy metals industry research report files have large sizes, requiring extended parsing timeout to complete full field extraction |
Recall count | Top 8–10 entries | Energy metals data has multiple dimensions, requiring sufficient recalled structured data to support due diligence analysis and avoid information gaps |
Similarity threshold | 0.72–0.78 | Balance accuracy and recall rate, adapting to the structured data field matching logic of the energy metals category |
model_api_timeout | 120 seconds | Some industry data source interfaces have slow response speeds, requiring extended model call timeout to avoid request interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Support uploading large industry annual statistical reports and batch data source files, adapting to full data import for due diligence reports |
The parameter values provided on this page are common 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 Configuration Mistakes
- Model testing returns "request error", while curl testing of the interface functions normally. Cause: Local offline deployed model access whitelist is not configured, or model access API key and endpoint address are not filled correctly.
- The due diligence report outputs price data with inconsistent units. Cause: Field normalization rules are not configured, and the model fails to identify and unify units such as yuan/ton and ten thousand tons, leading to numerical comparison errors.
- The model does not organize and output results after knowledge base query. Cause: The
maxContextparameter is not adjusted to adapt to long text input, resulting in truncated parsed knowledge base data, and the model cannot obtain complete information.
How to Confirm the Configuration Is Complete
- Execute the model test interface, verify that the returned result includes fields such as energy metal category price and inventory, and matches the configured recall count.
- Upload a small energy metals industry report, check that the parsed fields include preset category identifiers and numerical units.
- View the model call log, confirm that the timeout configuration matches the actual response duration of the data source interface.
- Adjust the similarity threshold, verify that the accuracy of recalled data meets the requirements of due diligence analysis.
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.