This page is written based on FastGPT v4.17.0.
What the data for this category looks like
Production ledger data for complete home appliance manufacturing comes from ERP and MES systems, component batch records, tightening and refrigerant charging equipment logs, electrical safety test and performance spot check reports, and after-sales repair records. Data updates follow a per-unit product workflow ledger schedule. Teams daily summarize spot check and repair data, and generate performance test reports on a fixed schedule. Daily documents include configured BOMs, assembly work instructions, process parameter records, and more. Fields include component batch numbers, tightening torque values, refrigerant charging amounts, safety test voltages, repair reasons, and other relevant items. Each field is bound to units and validation rules for its corresponding business scenario.
Constraints on Model Access and Configuration
This category’s ledger data has high update frequency and diverse types. Model access configurations must support fast parsing of multi-format text. This prevents summary reports from being delayed due to data synchronization lags. Each individual product has many associated documents. The context window must be set to a reasonable range. This prevents cross-interference between data from different product models in mixed-line production. Professional fields such as refrigerant charging amounts and safety test parameters require accurate recognition by the model. Configurations must ensure the model’s understanding accuracy of industrial professional terms. They must also adapt to the enterprise’s internal network isolation requirements. This prevents core production data from being leaked.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to associated analysis of multi-process ledgers for a single batch, prevents data truncation caused by exceeding the model’s context window |
maxTokens | 2000–3000 token | Matches the output length of a single production report, covers multi-dimensional summary content including safety regulations, performance, and repairs |
quoteMaxToken | 1500–2000 characters | Retains core fragments of the original ledger text, ensures key business fields such as component batches and charging amounts are not truncated |
temperature | 0.1–0.3 | Ensures generation accuracy of professional terms and business fields, reduces random generation of irrelevant content |
AIPROXY_API_ENDPOINT | Fill in the local or dedicated cloud interface address according to the enterprise’s deployment environment | Adapts to internal network isolation requirements, prevents production data from being transmitted through public interfaces |
AIPROXY_API_TOKEN | Exclusive access key assigned according to permissions | Limits interface access scope, prevents data security risks caused by unauthorized calls |
Three Common Mistakes
- The
{"object":"error","message":"Only allowed now"}error occurs. The cause is failure to configureAIPROXY_API_TOKENor interface address that meets the enterprise’s internal permission requirements. This prevents passing the model access validation. - Local deployed vllm services cannot be added in oneapi. A connection refused error is reported. The cause is failure to open the listening port of the vllm service, or the enterprise’s internal network policy blocks cross-service access links.
- Generated ledger reports missing component batch numbers or refrigerant charging amount fields. The cause is that
quoteMaxTokenis set too small. This truncates core business fields in the original ledger text, preventing the model from extracting key information.
How to Verify Successful Configuration
- Call the test interface with the assembly work instructions and electrical safety test records for a single air conditioner. Check if the summary content returned by the model includes all required business fields.
- View the model call logs. Confirm that the configurations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENdo not trigger permission verification failure prompts. - Adjust the
temperatureparameter to the 0.1–0.3 range. Compare the content of two generated reports. Confirm the consistency of professional terms and business fields. - Submit a summary request for batch ledgers. Check if context overflow related error prompts appear. Verify the rationality of the
maxContextconfiguration.
These values are common starting points. They should be validated against the target deployment’s own samples.