What this category’s data looks like
Core data comes from daily business documents of electroplating plants and hardware surface treatment workshops. These include bath analysis records, process parameter cards, fixture usage logs, film thickness and adhesion test reports, hazardous waste management ledgers, and online water quality monitoring records. Data sources include customer surface treatment specifications, bath test results, rectifier operating parameters, online water quality monitoring data, and hazardous waste management ledgers. Updates follow individual bath cycles, shift schedules, and fixed monitoring cycles. Each document includes fields such as bath composition, temperature and current, fixture position, film thickness values, and discharge indicators. Some documents require labeling of test time, batch number, and corresponding workpiece type.
What constraints these characteristics impose on model access and configuration
Data for this category is multi-source and has inconsistent update frequencies. Single-round drafting requires integrating multiple document types including bath test results, rectifier parameters, and water quality monitoring data. Therefore, model access must support context loading for multi-format input. Some documents contain sensitive content such as hazardous waste management and discharge indicators, so the scope of model output must be restricted. Exception report drafting requires linking process parameters and test results from historical bath cycles. When calling tools, field units and batch information must be aligned to avoid generating content that does not comply with process specifications. Frequently updated bath cycle data requires configuration that flexibly adjusts the context loading cycle to prevent redundant or missing key associated information.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate process parameters, test records, and fixture data for at least 3 consecutive bath cycles, covering the associated information required for anomaly analysis |
maxTokens | 2000–3000 token | The output length of a single process document or exception report must fit the standard length of workshop documents to avoid truncation of critical content |
quoteMaxToken | 500 token | Limits the direct output scope of sensitive content such as hazardous waste discharge data and bath heavy metal components, to comply with data security requirements |
| Workflow/API tool selection | auto | Automatically trigger tool calls to extract structured fields such as bath parameters and film thickness test values, ensuring that drafted content aligns with actual business data |
temperature | 0.2–0.4 | Process documents and exception reports must maintain rigor, avoiding subjective content that does not comply with process specifications |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapts to the access path of locally or cloud-deployed large language models, ensuring that requests can be sent normally |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Completes identity verification for model access, ensuring the legitimacy of requests |
Three common mistakes
- Phenomenon: When calling an MCP tool to draft a process document, a 400 status code is returned, and the model responds normally when the tool is not called. Cause: The adaptation parameters for
toolChoiceare not configured, or the parameter format for tool calls does not align with the field units of bath parameters and film thickness test values, resulting in an incorrect model request format. - Phenomenon: An error prompt appears after saving or publishing the configured node, and the error disappears when switching the AI model. Cause: The context length configuration for initializing the model does not adapt to the loading requirements of multi-bath cycle data for this category, and switching the model automatically loads context parameters that meet the requirements.
- Phenomenon: The generated exception report does not include test data such as film thickness and adhesion, only outputting text descriptions. Cause: The automatic call configuration for
toolChoiceis not enabled, so the model cannot automatically extract structured test record fields, resulting in missing content.
How to confirm the configuration is complete
This verification process is based on the functional logic of FastGPT v4.17.0.
- Initiate a simulated process document drafting request, verify that the request context includes at least 3 consecutive bath cycles of process parameters and test records, and confirm that the
maxContextconfiguration meets requirements. - Input test text containing hazardous waste discharge data, verify the scope of sensitive content in the model output, and confirm that the
quoteMaxTokenconfiguration complies with security requirements. - Trigger a tool call test, verify whether the model automatically extracts structured fields such as bath composition, current, and temperature, and confirm that the
toolChoiceconfiguration takes effect. - Check the output length of the model response, confirm that no content truncation occurs, and verify that the
maxTokensconfiguration fits the document length requirements.
The values provided are common starting points and should be measured against the reader's own samples.