What the Data for This Category Looks Like
Data sources cover sensor records from mining sites, manual ledgers from beneficiation workshop teams, and material test records from quality inspection departments. Updates run after each shift or at fixed daily times. Most documents use structured table formats. Each single record includes fields such as operation time, equipment ID, throughput, raw material quality test values, and concentrate output. Throughput uses tons as its unit, operation duration uses hours, and equipment operating parameters use kilopascals.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and produced by different teams. Multi-turn dialogue requires clear distinction between the attribution scenarios of different uploaded files. Prompt engineering must predefine file classification rules. Update frequency is high, so each summary must limit the time range. The messages context must carry the time interval parameters confirmed in the previous round to avoid mixing data across time periods. Many structured fields have inconsistent units. Prompt engineering must explicitly specify extraction formats and corresponding units for each field to prevent unit errors in results. Single record volume is large. Multi-turn dialogue must limit the number of recalled records per request to avoid context overload.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 token | Mine mining and beneficiation ledgers have large batch data volumes. Sufficient context must be retained to associate multi-turn questions with historical data, and this aligns with the context limit requirements of FastGPT v4.17.0 |
| Single Search Limit | Top 20 entries | Prevents context overload from excessive historical records, while covering query needs for main shift operation data |
| Variable Configuration | Add time range variables in variables | Dynamically limit the time period for aggregated data, adapt to query needs for different shifts or dates, and enable accurate data recall |
| Question Optimization | Enabled | Converts colloquial ledger query requests into structured field extraction instructions, improving recognition accuracy for category-specific fields |
| Single Upload File Count | Fewer than 5 | Mine mining and beneficiation ledger files mostly contain single-category data for a single shift or workshop. Excessive files will confuse classification logic and reduce extraction efficiency |
Three Common Misconfigurations
- Phenomenon: Sensor data and manual ledgers uploaded during multi-turn dialogue cannot be correctly distinguished, and confusion occurs during field extraction. Cause: No predefined classification rules for different uploaded files in the prompt engineering, and no file type identifier configured via
variables. - Phenomenon: The error
Cannot read properties of null (reading 'q)occurs during dialogue. Cause: The format of themessagescontext was not configured correctly, and null value fields were not preprocessed, leading to parsing failure during model invocation. - Phenomenon: Workflow question classification results do not match actual ledger types, with low recognition accuracy. Cause: The prompt engineering did not use category-specific fields of mine mining and beneficiation ledgers (such as equipment ID, throughput unit) for restriction, and general classification logic cannot adapt to category characteristics.
How to Confirm Proper Configuration
- Upload two different types of mine mining and beneficiation ledger files, check whether the prompt engineering can extract corresponding fields separately, and confirm that file classification takes effect.
- Configure a test time range in
variables, initiate a multi-turn dialogue, and check whether the returned results only include data within that time period. - Initiate three consecutive different ledger query requests, check whether the context correctly retains historical questions and results, with no context loss.
- Trigger the error scenario, check whether errors of the type
Cannot read properties of null (reading 'q)occur, and confirm that themessagesformat is configured correctly.
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.