What this category of data looks like
This category of data covers content related to the full lifecycle of manufacturing enterprise equipment. Sources include EAM/CMMS systems, equipment sensor collection data, operator daily inspection records, maintenance work order history, spare parts inventory ledgers, statutory inspection records for special equipment, and more. Daily documents include equipment ledgers, inspection forms, graded maintenance plans, maintenance work orders, fault analysis reports, spare parts lists, and others. Fields involve equipment numbers, inspection points, maintenance cycles, spare part models, inspection validity periods, and additional relevant items. Update rhythms include shift-by-shift inspection submissions, weekly maintenance plan updates, monthly spare parts inventory checks, and real-time records created when faults occur.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
The data sources for this category are scattered, and update rhythms vary significantly. Multi-turn dialogue must first distinguish the different context weights of real-time sensor data, periodic archived documents, and temporary fault records, to avoid invalid information interfering with core judgments. Document fields cover multiple business dimensions such as equipment identification, inspection indicators, and inspection validity periods. Prompts must clearly specify the corresponding meanings of fields to prevent field confusion across documents. Additionally, the supplier and process quality audit link needs to associate spare parts supplier qualifications and maintenance service records. The context of multi-turn dialogue must include associated fields related to suppliers, to ensure that dialogue logic aligns with business scenarios.
How to set the configuration
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to equipment work order number | Distinguish multi-turn dialogue contexts for different equipment, avoid information confusion across work orders |
messages | Retain the first 15–20 dialogue history entries | Cover the complete communication process for a single maintenance audit, control total context length within 800–1200 characters |
variables | Bind core fields of equipment ledgers and spare parts lists | Automatically inject business-specific data, reduce manual input errors |
| Total length of multi-turn dialogue context | 800–1200 characters | Adapt to the average length of equipment inspection documents, avoid exceeding model processing limits |
| Prompt template variables | Calibrate based on actual testing | Adapt to differences in equipment document fields across enterprises, ensure prompts match business scenarios |
Three common mistakes
- Issue: When configuring multi-turn dialogue, failing to bind
chatIdleads to confusion of dialogue contexts across equipment, with inspection information from different work orders being mixed. Cause: ThechatIdis not bound to the equipment work order number, so the system cannot distinguish dialogue contexts for different business scenarios. - Issue: After enabling the file upload function, the dialogue interface cannot receive uploaded equipment inspection table PDF files, and returns an error with empty fields. Cause: The binding rules for file parsing are not correctly associated in the dialogue configuration, causing uploaded files to fail to be read by the system.
- Issue: The GLM model can be selected in the AI dialogue module, but only GPT series models are displayed in the text content extraction link. Cause: The model configuration for text content extraction is not synchronized to the externally connected model list, and the model is only configured separately for the dialogue module.
How to confirm the configuration is complete
- Initiate a simulated multi-turn dialogue for equipment inspection audit, check if the dialogue history is automatically bound to the
chatIdof the corresponding work order, and confirm that no information from other work orders is mixed into the context. - Upload an equipment inspection table file, check if the dialogue interface can normally read the file content and return parsing results.
- After configuring
variablesto bind equipment ledger fields, initiate a dialogue, check if the prompt automatically injects the bound field data. - Test dialogue selection after connecting different models, confirm that the text content extraction module displays all connected models.
The values provided are common starting points and should be measured against the reader's own samples.