What Data for This Category Looks Like
This category of data is primarily sourced from order and inventory records, bill of materials (BOM) lists, process routes, equipment capacity ledgers, staff shift schedules, work-in-progress status, and supplier delivery logs within manufacturing enterprise ERP systems. Daily processed documents include sales orders, master production plans, material requirements plans, weekly and daily scheduling tables, production work orders, material shortage lists, and delivery change notices. Data update frequency is adjusted based on business rhythms: capacity and inventory data are typically synced daily, scheduling baselines are updated weekly, and corresponding records are refreshed immediately when order changes or exceptions occur. Document fields include order numbers, material codes, process working hours, equipment numbers, delivery requirements, and more, with numeric codes and timestamps serving as core identifiers.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered, and update rhythms vary significantly. Multi-turn dialogue must integrate the latest scheduling, inventory, and capacity data according to different update cycles. Prompt engineering must clearly distinguish the priority between baseline data and temporary exception data. Most document fields use coded or numeric identifiers, so prompt engineering must require the model to return audit results that correspond to specific codes, avoiding vague descriptions. A single dialogue must process a large number of fields, so context length must be limited to ensure response efficiency. At the same time, key information about historical work orders and scheduling changes must be retained during multi-turn interactions to avoid repeated requests for original business data.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the last 10 rounds of user and assistant conversation content | This category of single audit needs to cover multi-process data; 10 rounds can fully carry the interaction logic while avoiding context overload |
| Context Window Limit | 800–1200 characters | The average length of a single core scheduling document for this category falls within this range; exceeding it easily causes the model to confuse data associations |
variables | Pre-fill the current work order number, equipment capacity threshold, and delivery requirements | The core data of this category are coded fields; pre-filling variables reduces repeated input and improves audit accuracy |
chatId | Bind the unique identifier of the current production work order | Multi-turn dialogue must be bound to a specific work order to avoid cross-work order data confusion, ensure consistency of audit scenarios, and comply with FastGPT v4.17.0 session management rules |
| Multi-turn Session Timeout | 600 seconds | Scheduling audit requires multiple checks of exceptions and rescheduling plans; 600 seconds covers the complete interaction process |
| Prompt Template Loading Rule | Prioritize loading the dedicated audit template for the current process | This category covers multi-process scenarios; dedicated templates can accurately match the business needs of the current audit |
Three Common Mistakes
- Phenomenon: The Human field is empty in the response record preview when calling the API. Cause: Historical conversation content is not passed correctly in the
messagesparameter format, causing the system to fail to recognize the user input node. - Phenomenon: Custom dialogue interface interaction is stuck, and response exceeds a reasonable duration. Cause: The context window limit is not set, and excessive historical scheduling and work order data are loaded, exceeding the model's processing threshold.
- Phenomenon: The audit results returned by the model do not meet scheduling requirements. Cause: The prompt engineering does not bind the work order number and capacity parameters in
variables, causing the model to fail to obtain exclusive data for the current scenario.
How to Confirm Proper Configuration
- When the API is called, check whether the
messagesparameter includes complete user and assistant conversation nodes, and confirm that the Human field of each user input node is not empty. - When multi-turn dialogue is tested, verify that the context window only retains scheduling and work order data required for the current audit, and adjust the window limit to a range matching the length of business data.
- After the model response is triggered, confirm that the returned results are associated with the preset variables of the current work order, with no content deviating from the business scenario.
- Verify that the session timeout setting aligns with the reasonable duration of a single audit, ensuring that interactions are not interrupted due to timeout.
The values provided are common starting points and should be measured against the reader's own samples.