Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The document data for mineral mining and beneficiation mainly comes from mining plan ledgers, beneficiation process logs, safety inspection records, monthly reserve inspection reports, and compliance declaration documents. Data update cycles cover daily, weekly, and monthly frequencies. Daily data includes on-shift beneficiation processing volume and equipment operating parameters. Weekly data covers beneficiation recovery rate and grade fluctuations. Monthly data includes full-mine reserve inventory and compliance reports. Each document includes standard fields such as mining batch number, ore grade, processing volume, recovery rate, and safety hazard level. Most field units are industry standard units such as tons, percentage, and cubic meters.
What constraints do these characteristics impose on tool calling and plugin configuration
The multi-frequency, multi-field data characteristics of mineral mining and beneficiation impose multiple constraints on tool calling and plugin configuration. First, daily updated equipment and beneficiation parameter data volumes are large. Tool calling must limit the size of datasets returned in a single call to avoid exceeding context limits. Second, industry standardization of document fields requires that the functionCall of tool calling strictly matches fixed fields such as mining batch, grade, and processing volume. Otherwise, the generated documents will have missing fields or incorrect units. Third, generating monthly compliance reports requires calling full historical data. Plugins must support pulling data by time range, and must verify that the data time interval matches the report cycle to avoid generating invalid reports.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
functionCall field definition | Include mining batch, ore grade, processing volume, recovery rate, safety hazard level | Match the standard field structure of mineral mining and beneficiation documents to ensure generated content complies with industry norms |
toolChoice | auto | Allow the AI to autonomously identify the current document type and select the corresponding tool calling logic |
| Tool calling trigger timing | Trigger daily at 00:00, or trigger manually on demand | Adapt to different update cycles of daily beneficiation data and monthly compliance reports |
| Plugin data pull limit | Top 1000 entries | Control the size of datasets returned in a single call to avoid context overflow |
| Plugin data pull timeout | 600 seconds | Adapt to the pull delay of bulk mineral mining and beneficiation data to avoid call interruptions |
| Single tool calling context limit | 800–1200 characters | Adapt to the conventional generation length of mineral mining and beneficiation documents |
Three common mistakes
- Phenomenon: Fields are missing or units are incorrect in tool returns, and generated documents contain incorrect descriptions such as "grade 100 tons". Cause: The
functionCallis not configured according to the standard fields of mineral mining and beneficiation, so the AI cannot correctly match data fields and units. - Phenomenon: Context overflow errors are returned in the conversation interface, and the dataset returned by tool calling exceeds the system's carrying capacity. Cause: A reasonable plugin data pull limit is not set, and the volume of data pulled in a single call is too large.
- Phenomenon: The AI does not call the data pull tool, and directly generates generic documents without specific values. Cause:
toolChoiceis configured to fixedly call a specified tool, and the AI is not allowed to autonomously identify document types and data needs.
How to confirm the configuration is correct
- Manually trigger a tool call, and check whether the fields of the returned dataset match the fields defined in
functionCall. - Review tool calling logs to confirm that the trigger timing complies with the preset scheduled or manual rules.
- Verify that the generated documents contain correct units and fields, and adjust the result verification rules until requirements are met.
- Test bulk data pull scenarios to confirm that tool calling does not experience timeout or interruption issues.
The values provided are common starting points and should be evaluated 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.