What data for this category looks like
Inspection and process record data for general mechanical equipment assembly typically comes from PLM/ERP, part inspection data, assembly tool collection systems, test benches, MES, and customer technical agreements. The data is updated per device and per order, and covers documents including assembly process cards, BOMs, torque records, gap adjustment records, commissioning reports, factory inspection forms, packing lists, and more. Fields include material codes, assembly torque values (unit N·m), gap dimensions (unit mm), coaxiality tolerances, and others. Different documents have varying field formats and update nodes.
What constraints these characteristics impose on model integration and configuration
The multi-variety assembly per order characteristic of this category requires model integration to adapt to small-batch, multi-type data per single device or order. Multi-source heterogeneous data sources require the configuration process to support document formats and field rules exported by different systems. Data update frequency is tightly tied to assembly progress, so the context used for model calls must cover all valid data for the current assembly stage. Additionally, the inspection and audit links have strict requirements for numerical accuracy. Model parameter configuration must balance rigor and processing efficiency to avoid missed or false judgments caused by improper parameter settings.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the total token count of full-process assembly documents for a single device, preventing context truncation that causes loss of critical information |
temperature | 0.2–0.4 | Meets the rigor requirements for inspection and audit, reduces random output deviation, and ensures accuracy of numerical verification and rule matching |
| Workflow/API tool selection | auto | Automatically calls tools to compare numerical fields such as torque and gap, reducing errors from manual intervention |
AIPROXY_API_ENDPOINT | Fill in the actual deployed proxy address | Access path for connecting to third-party models, ensuring normal model call connectivity |
AIPROXY_API_TOKEN | Use the access key corresponding to the model platform | Completes identity verification, ensuring security and compliance of model integration |
Three common errors
- The interface displays "No available channel for model gpt-4o-mini under current group default". The cause is that
AIPROXY_API_TOKENis not configured correctly or the proxy address is invalid, resulting in failed model call authentication. - Setting
maxContextbelow 5000 tokens causes the BOM and assembly records for a single device to fail to be fully passed to the model, leading to missed audits for missing or incorrect assembly. - Setting
temperatureabove 0.7 causes random deviations in the model's judgment of values such as torque and gap, leading to inconsistent inspection results.
How to confirm configuration is complete
- On the model management page of FastGPT v4.17.0, test the call of the specified model, and check whether the returned results include correct inspection and audit logic.
- Import the assembly documents and inspection records of a single device, trigger the audit process, and verify whether the output results cover all key fields of the input documents.
- Check the status indicator of the model channel, confirm there are no "unavailable" error messages, and verify the validity of the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations. - Simulate input of assembly data for different orders, confirm that the model can adapt to differences in BOM structures and inspection record formats.
The values given are common starting points and should be measured against the reader's own samples.