Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data for mining and ore processing comes from internal production management systems, equipment IoT platforms, geological survey archives, and office systems. Data update frequencies fall into four categories: real-time (equipment operating parameters), daily (production daily reports), weekly (inspection archives), and project cycle (exploration documents). Document structures include structured equipment parameter tables, semi-structured operation logs and shift schedules, and unstructured technical specifications and training materials. Fields include equipment number, operating duration, processing capacity, inspection number, employee ID, job title, and others. Units include hours, tons, entries, and characters.
Constraints on Model Integration and Configuration
Structured equipment and employee data require clear field mapping rules to prevent the model from confusing data of different business types. Real-time equipment operating data requires that the latency of the model integration interface is controlled within a reasonable range, and short timeout configurations must be adapted. Long-form exploration and operation documents require adjustment of context window parameters to ensure complete recall of key information. When calling mixed multi-source data, configure rules for recall classified by business type to avoid irrelevant data interfering with output. Employee data for internal office scenarios requires strict access range restrictions, and secure proxy and token rules must be configured.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Mining documents include long-form exploration reports and operation logs, and need to accommodate multi-source context information |
maxTokens | 2000–3000 token | Responses for internal office and employee services need to cover specific content such as equipment status and shift adjustments to avoid truncation of critical information |
quoteMaxToken | 1500–2000 token | Need to prioritize recall of core reference content such as equipment operation records and employee training documents, and control the length of quoted segments |
temperature | 0.3–0.5 | Internal office scenarios require stable information output and avoid irrelevant divergent content |
toolChoice | auto | Need to automatically call knowledge base recall or tools based on user queries, adapting to multiple scenarios such as equipment data queries and employee attendance inquiries |
AIPROXY_API_ENDPOINT | Proxy address deployed on the enterprise intranet | Internal mining data requires secure transmission to prevent sensitive production and employee information from being leaked over the public internet |
AIPROXY_API_TOKEN | Token bound to an enterprise internal service account | Restrict access to only authorized internal office systems to call the model, ensuring access security |
Three Common Configuration Errors
- Symptom: Significant differences between online chat and API call results. Deviation occurs even when
streamis set tofalseanddetailis set totruefor API calls. Cause:maxContextandtemperatureparameters for online and API calls are not configured uniformly, leading to inconsistent context processing logic across different call chains. - Symptom: Model output only includes thought processes, with no main body content. Cause: The
maxTokensconfiguration value is too low, resulting in truncation of main body content, or thetemperatureconfiguration is outside the reasonable range, leading to abnormal output logic. - Symptom: The workspace edit page prompts that no language model is configured, and the AI model option in workflow problem classification is empty. Cause:
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTare not configured globally on the platform, or the configured token does not have corresponding model call permissions.
How to Confirm Successful Configuration
- Submit test queries related to equipment status or employee attendance, and verify whether the returned content includes corresponding business fields and unit information.
- Call the API interface to submit the same query, compare the returned results with the online chat to confirm that the context recall range and output format are consistent across both.
- Check the workflow configuration page to confirm that the AI model option displays an available model list with no error prompts.
- View the model call logs to confirm that the actual values of configuration parameters such as
maxContextandmaxTokensmatch the preset values.
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and requirements.
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.