What the data for this category looks like
Data for mineral mining and processing policy and regulatory basis comes from official public documents of national and local natural resources and emergency management departments, as well as normative documents released by industry associations. Documents take the form of official correspondence, and include fields such as document number, issuing unit, effective date, clause level (article/paragraph/subparagraph), applicable mineral types and mining methods. Content is categorized by regulatory link: mining, mineral processing, environmental protection, and safety. Data updates align with national and local regulatory policy changes, with no fixed cycle. New or revised policies will fully cover all relevant clauses at the time of update.
What constraints these characteristics impose on the model access and configuration link
The hierarchical structure and sub-scenario tags of policy documents require retrieval to match metadata such as mineral type and mining method. This prevents incorrect cross-scenario references. Single policy documents contain relatively large amounts of content. The effective range of context loading must be controlled to avoid exceeding the model’s context limit. Policy updates have no fixed cycle. Fast incremental synchronization configuration must be supported, while ensuring retrieval logic aligns with the latest policy fields. The rigor of policy clauses requires outputs to be associated with original text fragments. Content without supporting basis cannot be generated. Relevant parameters must be configured strictly.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Mining and processing policy documents often contain dozens of clauses per document. A full context load of a single policy document is required to avoid truncating key regulatory requirements |
maxTokens | 2000–3000 token | Responses must cover details of policy clauses, while controlling output length to fit Q&A scenarios. Thresholds are calibrated based on actual testing |
quoteMaxToken | 1500 token | Precise citation of original policy text fragments is required. Limiting the length of cited fragments ensures context coherence while staying within model support limits |
temperature | 0.1–0.3 | Policy Q&A requires strict accuracy. Reducing randomness avoids generating content that does not meet regulatory requirements |
AIPROXY_API_ENDPOINT | Exclusive access address for the corresponding platform | Must match the proxy address provided by FastGPT to ensure stable model call links |
AIPROXY_API_TOKEN | Bound exclusive secret key | Ensures unique call permissions, prevents unauthorized access, and meets data security requirements for public sector and state-owned enterprise environments |
Three Common Configuration Errors
- Phenomenon: Abnormal model call latency display. Online scenarios only show first token latency. Offline scenarios show total runtime. Unified viewing of first token latency is not possible. Cause: Unified latency statistics configuration was not adjusted. FastGPT’s default latency display logic differentiates by deployment mode. No unified statistics dimension was set for the policy Q&A scenario.
- Phenomenon: Policy clauses cited in responses do not match the target mine’s mining method. Cause: No metadata filtering rules were configured. Retrieval did not filter policy content applicable to open-pit or underground mines, leading to incorrect cross-scenario references.
- Phenomenon: Model-generated policy interpretation exceeds the scope of the original text. Cause:
temperaturewas set too high. The model generated extended content without basis in the original text, which does not meet the rigor requirements of policy Q&A.
How to Verify Successful Configuration
- Initiate a policy Q&A targeting a specific mineral type and mining method. Check whether the policy clauses cited in the response match the target scenario.
- View model call logs to confirm that the number of loaded context tokens does not exceed the configured
maxContextthreshold. - Check the latency statistics panel to confirm that latency display dimensions are consistent across all scenarios.
- Verify that generated content includes original text citation fragments, and confirm that the
quoteMaxTokenconfiguration is effective.
The configuration values listed here are common starting points. They should be evaluated against the relevant samples and use cases of the deployment.
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.