What the Data for This Category Looks Like
Data for general mechanical equipment assembly scenarios typically comes from PLM, ERP systems, part inspection records, assembly tool collection data, test benches, MES systems, and customer technical agreements. Daily documents include assembly process cards, multi-level BOMs, torque and clearance adjustment records, commissioning reports, factory inspection certificates, packing lists, and more. Data is updated per single device or order. Fields cover part numbers, torque values, clearance parameters, coaxiality indicators, test results, and some have clear units such as newton meters, millimeters, degrees Celsius.
What Constraints Do These Characteristics Impose on the "Model Integration and Configuration" Link?
The multi-level BOM and per-device update characteristics of this category require limiting context length during model integration to avoid logical confusion caused by data redundancy. Dispersed multi-source data (assembly tools, test benches, MES) requires unified format adaptation. Without this, field parsing errors will occur. Torque, clearance and other parameters with clear units require support for unit recognition and verification in the configuration to prevent calculation deviations in the large model. The audit link requires precise matching of physical objects and document data. Therefore, data integration must prioritize the integrity of core fields to avoid irrelevant information interfering with retrieval results.
How to Set the Configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the context requirements of multi-level BOMs and multiple assembly documents, avoid truncation of key information |
maxTokens | 2000–3000 token | Control single-round output length, ensure audit results are clear and complete, prevent overflow of overlong content |
quoteMaxToken | 1500 token | Limit the total length of referenced documents, focus on core assembly parameters and inspection records, improve retrieval accuracy |
temperature | 0.2–0.4 | Reduce randomness, ensure consistency of audit logic, avoid arbitrary parameter calculation errors in the large model |
AIPROXY_API_ENDPOINT | Fill in the corresponding address according to the deployment environment | Adapt to proxy addresses for different deployment scenarios, ensure normal model calls, support access to various compliant large models |
| Question Optimization | Enabled | Trigger reference resolution and question expansion before data retrieval, solve the problem of unclear reference objects, improve retrieval accuracy |
Three Common Mistakes
- Large model output parameter total results have deviations. The manifestation is that torque values or clearance parameter calculation results do not match the original records. The cause is failure to limit the value range of the
temperatureparameter, leading to excessive randomness in the large model, with errors in unit field parsing and calculation. - Retrieval results lack BOM hierarchy information. The manifestation is that returned documents contain only scattered part data, without displaying a complete assembly hierarchy. The cause is that the
maxContextparameter value is too small, leading to truncation of multi-level BOM context and failure to fully load associated data. - Model call timeout. The manifestation is that a request timeout error is displayed on the interface. The cause is failure to reasonably set the
maxTokensparameter, leading to excessive single-round data processing volume exceeding the response time limit of the proxy interface.
How to Confirm the Configuration Is Correct
- Upload a complete single-device assembly document and inspection record, perform a BOM completeness check test, and check whether the returned results include all core fields and hierarchy association information.
- Simulate multi-round audit requests, observe whether the parameter calculation results output by the large model are consistent with the original records, and adjust the
temperatureparameter to the range that meets business consistency requirements. - Verify the model call authentication process, confirm that normal requests can be initiated after using the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations, with no error returns. - Test fuzzy reference audit requests, check whether the large model can correctly identify reference objects and return corresponding results after the question optimization function is enabled.
The values provided are common starting points and should be measured against the reader's own samples.