What the data for this category looks like
Data mainly comes from internal coal mine OA systems, underground equipment monitoring ledgers, employee attendance and scheduling systems, on-site inspection logs, and safety training documents. Structured data includes fields such as equipment number, inspection time, and anomaly level, with units corresponding to units, hours, and 1-5 levels. Unstructured data mostly consists of inspection reports and training materials, with 500 to 2000 characters per document. Real-time monitoring data updates every second. Attendance and scheduling data updates daily. Inspection logs sync every 8 hours per production shift.
What constraints these characteristics impose on model access and configuration
The data includes content with multiple update cycles: second-level real-time monitoring, daily updated scheduling, and shift-level inspection logs. When connecting the data, adapt to the synchronization cycles of different data to avoid system overload. Mixed structured and unstructured data requires model configurations to balance accurate matching of structured fields and semantic understanding of unstructured text. Underground data involves sensitive scenarios such as safety and equipment status. Configure reasonable output restriction rules for these scenarios. Also adapt to the large variation in single document length to avoid exceeding the model context limit.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed context length of underground monitoring and inspection reports, avoiding truncation of core safety data |
maxTokens | 2000–3000 token | Matches single-round output length requirements for employee inquiries and ticket replies, avoiding resource waste |
quoteMaxToken | 3000–4000 characters | Matches single-segment extraction length for unstructured inspection reports, retaining complete scene information |
temperature | 0.3–0.5 | Adapts to rigor requirements for safety and operation and maintenance scenarios, reducing random output |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise intranet deployment address | Adapts to coal mine intranet security isolation deployment requirements |
AIPROXY_API_TOKEN | Fill in according to the assigned exclusive key | Ensures permission security for intranet interface calls |
Three common mistakes
- Cannot find the custom protocol option on the new model channel interface, only preset types are displayed. Cause: Custom model access permission is not enabled in system settings, or the used FastGPT version does not support this configuration.
- Model-generated replies do not match the effect of the native platform. Cause: The
temperatureparameter value is outside the range adapted to the scenario, or themaxContextsetting is too small, causing key context to be truncated. - Cannot accurately match structured equipment ledger data during the knowledge base indexing process. Cause: The context extraction range is not adjusted for structured fields, resulting in embedded vectors failing to cover field-related information.
How to confirm the configuration is complete
- Initiate a model call request, verify that the system returns expected response status and content.
- Import a segment of underground equipment inspection log, confirm that the model extracts complete context information without truncation.
- Modify the
temperatureparameter value, observe changes in the model's reply rigor, and confirm the parameter takes effect. - Check the FastGPT v4.17.0 system operation log, confirm that model call token consumption does not exceed the preset upper limit.
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.