What the Data for This Category Looks Like
Policy and regulatory basis data for iron and steel smelting mainly comes from official documents and industry standards released by the State-owned Assets Supervision and Administration Commission of the State Council, the Ministry of Industry and Information Technology, the Ministry of Ecology and Environment, and the China Iron and Steel Industry Association. Update frequency is irregular with policy revisions, while industry-wide standards are updated every six months. Document structure includes policy document number, issuing authority, effective date, core clauses, and applicable smelting stages (sintering, ironmaking, steelmaking, etc.). Fields include energy consumption limits, pollutant emission indicators, and production capacity control requirements. Most units use industry-specific metrics such as kgce/ton steel and mg/m³.
Constraints on Model Integration and Configuration
The professional units and stage-specific clause structure of iron and steel smelting policy data require model integration to support recognition of industry-specific terminology. Long-text policy clauses require sufficient context capacity to avoid truncation of core control requirements. The irregular update schedule of official data sources requires knowledge base synchronization to match the model context refresh mechanism, ensuring the timeliness of Q&A content. The multi-field policy structure requires the retrieval link to prioritize matching the relationship between smelting stages and policy clauses, avoiding irrelevant content that interferes with Q&A accuracy. Additionally, integration requirements for locally deployed models require configuration items to support non-public cloud API interfaces and identity verification rules.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text policy clauses for iron and steel smelting, avoiding truncation of core control requirements |
maxTokens | 2000–3000 token | Ensures complete reply length for policy Q&A, covering explanations of multiple clauses |
quoteMaxToken | 1500–2000 characters | Limits the length of a single cited policy text, ensuring accurate and non-redundant citations |
temperature | 0.1–0.3 | Reduces randomness, ensuring accuracy of policy interpretation |
AIPROXY_API_ENDPOINT | API address of locally deployed models | Adapts to local model integration requirements, complies with FastGPT v4.17.0 configuration specifications |
AIPROXY_API_TOKEN | Access key generated by the local model | Completes identity verification, preventing unauthorized access errors |
Three Common Misconfigurations
- Issue: A 401 unauthorized error is returned after testing integration with a locally deployed reranking model. Cause:
AIPROXY_API_TOKENis not configured correctly, and does not match the access key generated by the local model. - Issue: Long policy clauses are truncated in model replies, failing to cover complete control requirements. Cause: The
maxContextconfiguration value is too small, not adapted to the long-text characteristics of iron and steel smelting policies. - Issue: Retrieved policy clauses do not match the target smelting stage, and policy content from unrelated industries appears. Cause: Precise retrieval rules are not configured for the stage-specific fields of iron and steel smelting, leading to an overly broad retrieval scope.
How to Verify Successful Configuration
- Access the configured
AIPROXY_API_ENDPOINT, use the configuredAIPROXY_API_TOKENto send a test request, confirm that a normal response code is returned. - Import a single long policy document, trigger a policy Q&A test, check that the reply does not have text truncation, and confirm that the
maxContextandmaxTokensconfigurations are reasonable. - Initiate a policy Q&A for a specific smelting stage (such as ironmaking), check that all retrieved clauses correspond to the target smelting stage, and confirm that the retrieval rules are effective.
- Adjust the
temperatureparameter, initiate two tests of the same question, check the consistency of the two replies, and confirm that the parameter configuration is effective.
The values provided are common starting points and should be measured against samples relevant to the specific deployment context.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.