What the Data for This Category Looks Like
The data for this category covers the entire production process of complete units of air conditioners, refrigerators, washing machines and small home appliances. Daily documents include configuration bill of materials (BOM), assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports and rework records. Data sources cover ERP/MES systems, component batch ledgers, tightening and charging equipment logs, safety compliance tester data, performance laboratory test results and after-sales fault archives. Data updates follow per-unit product, shift and spot check schedules. Fields include equipment number, process parameters, test results, batch information and fault type.
What Constraints Do These Characteristics Impose on the Model Access and Configuration Link
The mixed-line assembly of multiple product models requires the model to quickly distinguish process parameters and safety compliance requirements for different products, and accurately match corresponding document fragments. Multiple heterogeneous data sources mean the access process must support logs and reports in different formats, so reading rules for multiple data source types need to be configured. The update schedule based on per-unit, shift and spot check requirements means the model context window must load the latest batch of process and test data in a timely manner, to avoid calling outdated information. The direct consumer-facing attribute requires the maintenance guidance output by the model to comply with safety compliance rules, so it must strictly link to safety clauses in the corresponding documents, to avoid generating content that does not meet compliance requirements.
How to Configure Settings
The following configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the document splicing needs of mixed-line assembly of multiple product models, and accommodates process and test data from multiple batches |
maxTokens | 4000 token | Controls the output length of single-round Q&A, ensuring maintenance guidance information is concise and covers core key points |
temperature | 0.2–0.4 | Reduces output randomness, ensuring accurate and consistent expression of safety compliance and process parameters, in line with enterprise document requirements |
quoteMaxToken | 2000–3000 token | Accurately extracts key information from a single process document, avoiding redundant content interfering with fault judgment |
AIPROXY_API_ENDPOINT | Internal enterprise large model deployment address | Adapts to data isolation requirements, preventing leakage of sensitive process and after-sales data |
AIPROXY_API_TOKEN | Enterprise-specific access token | Ensures the security of internal large model calls, preventing unauthorized access |
Three Common Misconfigurations
- Phenomenon: The model call returns a 500 status code, prompting an invalid address. Cause:
AIPROXY_API_ENDPOINTis not correctly configured as the actual deployment address of the local large model, and is mistakenly filled with the default public address or third-party platform address. - Phenomenon: The maintenance guidance output by the model contains incorrect safety compliance parameters or process requirements. Cause: The
temperaturevalue is set too high, causing the model to generate random parameters that do not match enterprise documents, deviating from actual production requirements. - Phenomenon: The model configured via OpenAPI cannot be selected in the system interface. Cause:
AIPROXY_API_TOKENis not correctly configured, causing the platform to fail to pull the list of connected models, or the token permissions are insufficient.
How to Confirm the Configuration Is Complete
- Call the model test interface, pass in a fault description of a home appliance device, and check whether the returned result is linked to the locally configured process document fragments.
- View the model access log, confirm that the values of parameters such as
maxContextandmaxTokensmatch the configuration, and that no system default values have overwritten them. - On the system model selection interface, confirm that the large model configured via OpenAPI is normally displayed and can be selected and called normally.
- Simulate the mixed-line inspection scenario of multiple models, pass in BOM numbers of different models, and check whether the model can match the assembly and maintenance documents corresponding to each model.
The values provided here are common starting points, and should be evaluated against the reader's own samples.