What data for this category looks like
This category of data covers the entire production process of air conditioners, refrigerators, washing machines and small household appliances. Daily documents include configured BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports and repair records. Data is sourced from ERP/MES systems, parts batch ledgers, tightening and charging equipment, safety compliance testers, performance laboratories and after-sales fault databases. Update cycles run per single product, per shift production batch or spot check plan. Document fields include part models, process parameters, test results, batch numbers and exception records. Non-standardized statistical analysis fields are not included.
What constraints these characteristics impose on the "model access and configuration" workflow
The mixed-line production of multiple product categories requires the model to quickly distinguish process parameters for different models. The context association scope must be limited to avoid interference from cross-model data. Frequently updated single-product or batch data requires the configuration to support real-time or near-real-time data access, preventing outdated data from affecting Q&A accuracy. Heterogeneous data sources from multiple types of equipment and systems require the configuration to support multi-format data import. The processing load per Q&A round must also be controlled to prevent timeouts caused by excessive data volume. Strict requirements for safety compliance and performance testing require the model to prioritize relevant test documents when called. Configuration parameters must lock the relevant data sources.
How to set the configurations
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the context length required for splicing multiple documents in household appliance manufacturing, avoiding truncation of key process parameters and test records |
maxTokens | 4000–6000 token | Covers the complete response requirements for single-round process Q&A, including assembly steps, parameter ranges and exception handling instructions |
quoteMaxToken | 2000–3000 token | Limits the total length of referenced documents, ensuring that irrelevant model process data is not mixed in during mixed-line production |
temperature | 0.1–0.3 | Process and standard Q&A requires strict matching with document content, reducing the probability of randomly generated content |
AIPROXY_API_ENDPOINT | Fill in the actual proxy address according to the deployment environment | Adapts to the enterprise internal network and model call path, ensuring compliance of data transmission |
AIPROXY_API_TOKEN | Calibrated through actual testing | Matches the model call key configured by the enterprise, avoiding call failures caused by invalid tokens |
Three common configuration mistakes
- Symptom: Reranking model test prompts indicate no available model is found. Cause: The call key and endpoint for the corresponding reranking model are not specified in the configuration, or the key permission does not cover the reranking model type.
- Symptom: Empty results are returned after entering the token configured for OneAPI. Cause: The model permission corresponding to the token is not correctly bound, or the token is not added to the FastGPT model call whitelist.
- Symptom: Single-round Q&A response results are truncated. Cause: The
maxTokensconfiguration value is too small, failing to cover complete process document references and response content.
How to confirm the configuration is properly set up
- Enter the model configuration page, verify that the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches the proxy configuration deployed by the enterprise. - Initiate a single household appliance process standard Q&A, check that the document types referenced in the returned result match assembly and test records.
- Adjust the
temperatureparameter and initiate a test, observe the consistency of the response content, confirm that randomly generated content does not exceed the scope of process documents. - Simulate a mixed-line production scenario Q&A, check whether the model can correctly distinguish process parameters for different household appliance models, with no cross-model data confusion.
The values provided are common starting points and should be measured against the reader's own samples.