What this category of data looks like
This category of data covers entities such as manufacturing enterprise equipment departments, workshop maintenance teams, and public utility departments. Daily documents include equipment ledgers, inspection checklists, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance history, spare parts inventory, and statutory inspection information. Update cycles cover shifts, daily, weekly, monthly periods, and immediate synchronization when faults occur. Document fields include structured content such as equipment numbers, inspection items, fault codes, and spare part models.
What constraints these characteristics impose on the model access and configuration link
Data sources are scattered and have large differences in update cycles. Model access must support multi-source data synchronization configuration, and adapt to access rules for data sources with different update frequencies. Most document fields are structured coding content related to equipment. Recall logic matching structured data must be configured to avoid invalid recall of unstructured text. Immediate data generated when faults occur requires low-latency access, which imposes clear constraints on the response time of model calls. Statutory inspection records require strict compliance verification. Validation rules for fixed fields must be configured. Maintenance work orders and spare parts lists are closely related. Cross-document associated recall configuration must be supported to ensure the accuracy of maintenance recommendations.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the API access address of the locally deployed large model, for example http://192.168.1.100:8000/v1 | Adapt to the access requirements of locally deployed models, and meet the private deployment compliance requirements of equipment inspection scenarios |
AIPROXY_API_TOKEN | Fill in the access key of the local model, leave blank if there is no key | Adapt to the access authentication rules of different deployed models, and meet the security requirements of internal enterprise access |
maxContext | 8000–12000 token | Adapt to the context recall requirements of multiple documents such as equipment ledgers and inspection records, and avoid exceeding the model context window |
maxTokens | 2000–3000 token | Match the content length of single documents such as maintenance work orders and fault analysis, and ensure the completeness of Q&A results |
temperature | 0.2–0.4 | Reduce the probability of random generation, ensure the accuracy of process specification and technical standard Q&A, and meet the rigor requirements of equipment maintenance |
| Workflow/API tool selection | auto | Automatically select whether to call tools, adapt to the real-time recall requirements of multi-source equipment data |
Three common misconfigurations
- Symptom: A connection timeout error is returned when calling a locally deployed large model. Cause: The access address of
AIPROXY_API_ENDPOINTis not configured correctly, or the local model service does not open the corresponding port. - Symptom: After adding a model in the FastGPT backend, the model cannot be selected in knowledge base Q&A. Cause:
AIPROXY_API_TOKENis not configured correctly, or the model permissions are not granted to the currently used knowledge base. - Symptom: Core inspection items and standard requirements in the equipment inspection checklist are not recalled in Q&A results. Cause: The value of
maxContextis too small, causing the recalled context to not cover the complete field content of the inspection checklist.
How to verify successful configuration
- Access the configured
AIPROXY_API_ENDPOINTaddress to verify whether the local model service responds normally. - Initiate a model test call in the FastGPT backend, and check whether the returned results conform to the Q&A logic related to equipment inspection and maintenance.
- Upload a standard equipment inspection checklist document, initiate associated Q&A, and confirm that the recalled content matches the inspection items and standard requirements in the document.
- View the model call logs in FastGPT v4.17.0 to confirm that the actual values of parameters such as
maxContextandmaxTokensmeet the configuration requirements.
The values given are common starting points and should be measured against the reader's own samples.