What data for this category looks like
Data for this category comes primarily from ERP orders and inventory, BOMs and process routes, equipment capacity, staff shifts, work-in-progress, and supplier deliveries. Daily documents include sales orders, master production schedules, material requirements plans, weekly and daily scheduling tables, work orders, material shortage lists, and delivery change notices. Data updates occur daily, weekly, or when triggered by orders or abnormal events. Fields include order numbers, material codes, process capacity, planned start and completion times, available material quantities, and more. Units use common business metrics such as hours, pieces, and days.
What constraints these characteristics impose on multi-turn dialogue and prompt words
Data sources for this category are scattered, and update triggers are inconsistent. Multi-turn dialogue must filter expired scheduling and inspection data to avoid interfering with current requests. Document structures vary, and fields are closely linked. Prompt words must adapt to the field formats of different documents to ensure Q&A accurately extracts order and equipment-related information. Business operations span all processes and resource constraints. Multi-turn dialogue must associate multi-dimensional data, rather than focusing solely on single-process inspections. Scheduling calculations rely on real-time data. Dialogue context must synchronize the latest capacity and material status in a timely manner.
This configuration is based on FastGPT v4.17.0
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Context window maximum token count | 8000–12000 token | This category's documents contain multi-dimensional scheduling and inspection data, requiring coverage of complete business context |
| Multi-turn historical message retention | Retain the most recent 20 messages | Scheduling data updates frequently; overly long history dilutes valid information for current requests |
| Session variable binding | Bind order number and equipment ID fields in variables | This category's business relies on associated data for specific orders and equipment |
| Session timeout duration | 600 seconds | Scheduling calculations need to load multi-source ERP and equipment data, which takes significant time |
| Prompt template matching | Match by work order type associated with chatId | Inspection and scheduling logic differs across work orders, requiring precise adaptation to the current scenario |
Three common mistakes
- Symptom: The error
fail to create post presigned urlappears when uploading attachments or text datasets, while the template import function works normally for file uploads. Cause: Storage bucket permissions associated with the session are not configured correctly, or the storage path parameter is not bound invariables. - Symptom: Scheduling results in multi-turn dialogue do not match actual work orders. Cause: Retained
messageshistory is too long, and expired scheduling data is included, leading to incorrect context association. - Symptom: Sessions disconnect mid-use and cannot be restored. Cause: A reasonable session timeout duration is not set, or session context is not persisted via
chatId, resulting in lost session state.
How to confirm the configuration is complete
- Test uploading a small attachment, confirm the
fail to create post presigned urlerror does not appear, and verify storage configuration takes effect. - Initiate a multi-turn dialogue containing an order number and equipment ID, check that
variablesare correctly bound to the corresponding fields, and that the dialogue context associates the correct business data. - Initiate a time-consuming scheduling calculation request, confirm the session is not forcibly disconnected within 600 seconds, and that the timeout configuration matches expectations.
- Switch sessions for different work order types, confirm the prompt template correctly matches by
chatId, and that Q&A logic adapts to the current work order type.
The values provided are common starting points and should be measured against the reader's own samples.