Data Profile for This Category
Data for this category typically originates from ERP order and inventory systems, BOM lists, process route documents, equipment capacity ledgers, staff scheduling tables, work-in-progress ledgers, and supplier delivery records. Update frequencies include daily, weekly, or trigger-based updates tied to orders or exceptions. Commonly processed documents include sales orders, master production plans, material requirement plans, weekly and daily scheduling tables, work orders, material shortage lists, and delivery change notices. Fields include order numbers, material codes, capacity man-hours, shift durations, work-in-progress quantities, and more. Common units are pieces, hours, days, and batches.
Constraints Imposed on Multi-Turn Dialogues and Prompt Words
Dispersed data sources and inconsistent update rhythms for this category require multi-turn dialogues to stably pass context parameters. variables must bind core real-time updated fields such as scheduling and inventory data. The wide variety of commonly processed document types requires prompt words to clearly distinguish field rules for different documents, to avoid confusing material codes and work order numbers. Data updates trigger based on exceptions; overly long dialogue histories consume model processing resources, so the historical round count for messages must be limited to prevent context overflow. Additionally, cross-process and cross-resource constraints require prompt words to pre-sort logic, to ensure that device capacity and material requirements can be linked during dialogues.
This page is written based on FastGPT v4.17.0.
Configuration Guidelines
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Fixed binding of session unique identifier | Ensures context is not lost across multi-turn dialogues, matching the coherent review requirements for cross-document reviews |
messages | Retain the first 3–5 turns of dialogue content | Adapts to the update rhythm of production data, avoids redundant historical content interfering with current reviews |
variables | Bind real-time scheduling and inventory fields | Ensures the latest equipment capacity and material requirement data is called during dialogues |
| Maximum context length | 800–1200 characters | Adapts to the single-turn content scale of production planning documents |
| Single-turn message limit | 2 messages | Adapts to the step-by-step confirmation logic of cross reviews, prevents single-turn information overload |
| Prompt word template matching rules | Automatically switch based on document type | Distinguishes field verification standards for different documents such as sales orders and work orders |
Three Common Misconfigurations
- Attempting to send two production planning-related messages in a single turn results in only the first message being received by the system. Cause: The array structure of
messagesis not configured correctly, causing subsequent messages to not be parsed by the system. - After completing multi-turn cross reviews, the model’s accuracy in judging scheduling constraints decreases, and normal performance resumes after restarting the dialogue. Cause: The historical round count for
messagesis not limited, and overly long context overflow covers core scheduling parameters. - After calling a knowledge base to query material or capacity data, the results cannot be synchronized to the dialogue window. Cause: Query results are not correctly passed through
variables, meaning returned content is not loaded into the dialogue context.
How to Verify Proper Configuration
- Initiate a dialogue containing multiple production documents, check that
chatIdremains consistent to ensure coherent context. - Input a query that includes scheduling parameters, verify that returned results call the latest data bound by
variables. - Send multiple related messages, confirm that the system retains historical dialogue content according to the configured round limit.
- Switch between different types of production documents, check that prompt words automatically adapt to the field verification rules for each document type.
The values provided are common starting points and should be measured against the reader's own samples.