What Data for This Category Looks Like
This category of data primarily comes from ERP order and inventory systems, BOM lists, process routes, equipment capacity ledgers, staff shift schedules, work-in-progress ledgers, and supplier arrival records. Daily associated documents include sales orders, master production plans, material requirements plans, weekly and daily scheduling sheets, work orders, material shortage lists, and delivery change notices. Data update frequency varies by scenario. Capacity and inventory data are synced daily. Master schedules are updated weekly. Updates trigger immediately when order changes or exceptions occur. Data fields include process number, equipment ID, planned start/end time, material code, work-in-progress quantity, and other core analysis dimensions for production scheduling.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data for this category is scattered and has flexible update rhythms. Multi-turn dialogue must support dynamic retrieval of real-time data from different sources. A static knowledge base alone cannot cover real-time changed production information. Differences in the structure of multiple document types require prompts to clearly distinguish calling rules for sales orders, work orders, and scheduling sheets. This avoids confusion over field meanings. The immediate data update when exceptions occur means dialogue context must retain scheduling adjustment records from after the exception trigger. This ensures traceability can link to the latest change information. Quality traceability requires linking process, capacity, and material information. Prompts must limit dialogue to only retrieve production data bound to the current scheduling work order. This prevents irrelevant dimensions from interfering with analysis.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind work order ID, equipment ID, current scheduling date | Precisely limit the range of production data retrieved by the dialogue, only associating the production task currently being traced |
messages | Retain the first 20 rounds of dialogue content | Cover the complete process from initial scheduling to exception triggering, ensuring coherent context for multi-turn traceability |
chatId | Bind the unique identifier of the current work order | Bind the dialogue to a specific production work order, facilitating subsequent backtracking of the association between scheduling adjustments and quality problems |
| Context refresh interval | Automatically refresh every 600 seconds | Adapt to the update rhythm of daily data and on-demand updates during exceptions for this category, ensuring that scheduling and inventory data retrieved in the dialogue are the latest versions |
| Number of associated documents per retrieval | Retrieve the first 8 entries | Cover core associated information such as processes, materials, and capacity for the current work order, avoiding information overload |
Three Common Misconfigurations
- Symptom: Published dialogue only displays raw LaTeX code and cannot render formatting. Cause: No format rendering rules are configured in the prompt template, or the format rendering switch for the dialogue interface is not enabled.
- Symptom: Dialogue remains stuck in the AI reply phase for a long time with no valid output. Cause: No reasonable context refresh interval is set, leading to blocking when frequently retrieving scattered ERP and equipment ledger data.
- Symptom: The final workflow output includes redundant content from intermediate AI dialogues. Cause: The intermediate result output switch is not turned off in the AI dialogue node of the workflow, causing intermediate steps of multi-turn dialogue to be included in the final output.
How to Verify Correct Configuration
- Manually enter a known work order ID, and check whether the associated data returned by the dialogue only includes scheduling, material, and process information for that work order.
- Trigger a scheduling exception change, wait for the context to refresh, then initiate a traceability dialogue, and check whether the returned data includes the latest change records.
- Test input of LaTeX-formatted formulas, and confirm that the published dialogue interface can normally render the formatting.
- Run a workflow that includes an intermediate AI dialogue node, and check whether the final output only includes the preset target results with no intermediate step content.
The values provided here are common starting points and should be measured against the reader's own samples.