What the data for this category looks like
Daily documents for this category include grouping rules, BOM lists, welding and torque records, BMS firmware flashing records, insulation and voltage withstand reports, EOL test data, and system configuration lists. Data sources include cell capacity grading data, MES systems, welding equipment, torque tools, BMS software version records, and EOL test benches. Data is updated once per produced PACK or energy storage cabinet, and synchronized each time the BMS software version changes. A single process record includes professional fields such as cell capacity grading parameters, welding torque values, insulation test voltage, and BMS configuration items. Most formats are structured tables or raw logs exported from equipment.
What constraints these characteristics impose on the model access and configuration workflow
Data sources for this category are scattered and have varying formats. Models must support reading and parsing multi-source heterogeneous data during access. Single-piece data has a high update frequency and fits the scenario of individual PACK or cabinet operations. This requires model context configuration to match the token count of single-piece data, to avoid truncation of critical professional information. There are many professional fields with high precision requirements. Models must strictly match grouping rules and test standards to reduce output randomness. At the same time, inspection and audit results are directly linked to system safety. This requires the stability and accuracy of model calls to meet production-level requirements. Tool calls must trigger automatically to pull original records for verification.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total token count of all process records and documents for a single PACK or cabinet in this category usually falls within this range, which can avoid truncating critical inspection data |
maxTokens | 2000–3000 token | Inspection and audit responses need to cover multiple sets of test results and compliance items. This length balances content completeness and response speed |
quoteMaxToken | 1500 token | The token count of original records such as welding and insulation that need to be quoted is controlled within this range, ensuring quoted content is complete without interfering with the main response |
| Workflow/API tool selection | auto | Tool calls must trigger automatically to pull original inspection records, avoiding the model directly outputting unverified audit conclusions |
temperature | 0.1–0.3 | Inspection and audit must strictly match grouping rules and test standards. This parameter range reduces output randomness and ensures audit consistency |
AIPROXY_API_TOKEN | Fill in the exclusive key provided by the service provider, and must comply with the interface format requirements of FastGPT v4.17.0 | Used to verify model call permissions, ensuring the legality and security of interface calls |
Three common misconfigurations
- Symptom: Model calls return a
401 Unauthorizederror, or the interface prompts that the API key is invalid. Cause:AIPROXY_API_TOKENis not configured correctly, or the key has not been granted call permissions for the corresponding model. - Symptom: The tool call step is unresponsive, and the model directly outputs an audit conclusion. Cause:
toolChoiceis set tononeor the tool call switch is not enabled, which does not match the business requirement of calling tools to pull original records for this category. - Symptom: Critical grouping parameters or insulation test data are truncated in the audit results. Cause: The
maxContextconfiguration value is too small, and does not cover all process record data for a single PACK or cabinet, resulting in critical information being unreadable by the model.
How to confirm the configuration is correct
- Fill in
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT, then initiate a single test call to confirm the interface returns normally without errors. - Set
toolChoicetoauto, import a complete process record sample for a single PACK, and confirm the model triggers a tool call to pull relevant original data. - Import all process records for a single PACK, and check whether the audit content output by the model covers all required fields without truncation.
- Adjust
temperatureto 0.2, run three consecutive tests on the same sample, and confirm that the output results are consistent to meet audit requirements.
The values provided here are common starting points, and should be evaluated against the reader's own samples.