What the data for this category looks like
Data for this category comes from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, workshop dew point monitoring systems, formation and grading cabinets, and laboratory testing equipment. Daily generated documents include batching records, coating and rolling parameter sheets, winding or lamination process records, liquid injection ledgers, formation and grading data reports, and OCV and safety test reports. Data updates are synchronized per production batch and per individual cell. Some critical parameters such as workshop dew point and coating areal density are updated in real time. Document fields include material batch number, process parameter values, test results, operator ID, test time, and more. Some parameters have dedicated units: coating areal density uses g/㎡, and formation and grading voltage uses V.
Constraints on model access and configuration
Scattered data sources and varying update frequencies require model access to support batch pulling of multi-source heterogeneous data and format alignment. This prevents ledger summary errors caused by mismatched data timelines. Daily documents include a large number of process parameters and test results with dedicated units. Original fields and unit information must be retained during configuration, to avoid loss of parameter semantics that reduces report readability. Electrochemical production has strict requirements for data consistency. Model calls must limit context length, to avoid introducing irrelevant historical data that disrupts summary logic. Real-time updated workshop parameters require a synchronization trigger mechanism with short intervals. This ensures the latest process and test data is used during report generation.
Configuration Settings
This configuration is based on FastGPT v4.17.0, and is tailored to meet the ledger summary and report generation needs of lithium-ion cell manufacturing scenarios.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Lithium-ion cell manufacturing ledger data has many fields and close associations, so sufficient context must be retained to link cross-process data |
maxTokens | 4000 token | Single report generation must control output length, to avoid content truncation caused by exceeding model processing limits |
temperature | 0.1–0.3 | Ledger summary and report generation require consistent parameter wording, to reduce the risk of random outputs |
quoteMaxToken | 1500 token | When extracting ledger-related documents, limit the length of a single reference, to avoid redundant content disrupting summary logic |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Ensure model requests are directed to the specified proxy service, to guarantee data transmission security |
AIPROXY_API_TOKEN | Dedicated key bound to the deployment environment | Verify request legitimacy, to prevent unauthorized access |
Three Common Configuration Mistakes
- Phenomenon: After uploading a Word document of batching records, the model recognition result is empty. Cause: The built-in Word document parsing plugin of FastGPT is not enabled, or non-standard format tables embedded in the document cannot be automatically identified.
- Phenomenon: When calling the model to generate a report, the output result contains a large number of punctuation marks such as # and *. Cause: No appropriate output format filtering rules are configured, causing the Markdown format generated by the model to not be correctly converted to a standard report format.
- Phenomenon: When accessing an external model API, the request returns an authentication failure. Cause: The
AIPROXY_API_TOKENconfiguration is incorrect, or the key does not match the deployed proxy service.
How to Confirm Configuration is Complete
- Upload a standard Word document of coating and rolling parameters, and verify that the parsed fields include preset fields such as coating areal density and rolling temperature.
- Initiate a ledger summary request, and verify that the unit information of the original parameters is retained in the output result, with no redundant format punctuation marks.
- After configuring
AIPROXY_API_TOKEN, initiate a model test request, and confirm that the returned response status code has no authentication-related errors. - Adjust the
maxContextparameter range, and verify that the association logic of different batch data is normal, with no information loss caused by context truncation.
The values provided are common starting points and should be measured against the reader's own samples.