What data for this category looks like
Data for mining and mineral processing declaration material and case review mainly comes from mining enterprise applications for mining right renewal, mineral processing flow test reports, and on-site inspection case records retained by safety supervision departments. Data update rhythm follows project process progress: complete materials are updated intensively during the project declaration stage, and partial content is updated as needed during annual inspections or rectification stages. Document structures include structured application forms, approval opinion ledgers, and unstructured exploration drawings and ore grade test reports. Fields include mining right number, mining volume, mineral processing recovery rate, and others. Units mostly use professional mining units such as tons, grams per ton.
What constraints these characteristics impose on model access and configuration
The mixed structured and unstructured document feature requires models to support both text parsing and visual content recognition. Corresponding visual configuration items must be enabled. Fixed-format declaration materials and professional field units require models to adapt to fixed extraction rules and professional term recognition. Generation randomness must be limited to ensure review rigor. Batch data scenarios with concentrated updates require sufficient context window and output length limits to avoid exceeding model capabilities during single processing. Daily review workflow requirements require models to automatically call tools for format verification and data extraction. This adapts to multi-stage review tasks.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Case files include visual materials such as exploration drawings and on-site photos. Model needs to parse visual content to complete review |
maxContext | 8000–12000 token | Single declaration material often includes multi-page reports and complete case files. Adapts to long-context requirements for full document parsing |
maxTokens | 2000–3000 token | Structured approval opinions must be output in review results. Control output length to avoid redundant content |
quoteMaxToken | 1500–2000 token | Need to reference test data and license clauses in case files as review basis. Limit the length range of referenced content |
temperature | 0.1–0.3 | Reviews must remain consistent and rigorous. Reduce generation randomness to avoid subjective judgment bias |
AIPROXY_API_ENDPOINT | Fill in the official service endpoint of the accessed model | Complete model identity verification and request routing. Ensure normal call link operation |
Three common mistakes
- Phenomenon: Model returns 405 status code after call. Cause: Request format for
AIPROXY_API_ENDPOINTis not configured correctly, or the endpoint does not grant call permissions for the corresponding interface. - Phenomenon: Time taken for MCP-assisted model execution exceeds expectations. Cause:
maxTokensvalue is not limited, ortemperatureis set too high. This causes the model to generate redundant content and extend thinking time. - Phenomenon: File submission remains in "indexing" status for a long time. Cause: Context window limit of the model is not matched, or
maxContextconfiguration exceeds the maximum length supported by the model. This causes the indexing process to block.
How to confirm configuration is complete
- Initiate a test request for a single standard declaration material. Check that the returned result includes preset approval fields and corresponding reference content.
- View model call logs in FastGPT v4.17.0. Confirm there are no
AIPROXY_API_TOKENverification failures or 405-level errors. - Submit a test case file containing exploration drawings. Check that the model can correctly identify annotation information in the drawings and output corresponding review opinions.
- Adjust the
temperatureparameter to different ranges. Compare two review results. Confirm that result stability meets review requirements.
The values provided are common starting points and should be measured against the reader's own samples.
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.