What data for this category consists of
This category of data comes from raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily documents include batching records, coating and calendering parameter tables, winding/stacking operation records, liquid injection ledgers, formation and grading data, OCV and safety test reports. Data updates synchronize with each batch of cell production. Key monitoring parameters refresh in real time. Document fields include slurry ratio parameters, calendering gap values, winding tension parameters, dew point values, and more. Corresponding production batch identifiers are attached.
What constraints these characteristics impose on model access and configuration
The real-time refresh and batch-binding characteristics of this category of data require model access configuration to match production data update rhythms. Multi-source heterogeneous production documents and testing data require configuration to support unified parsing and adaptation of different document formats. Electrochemical production has high parameter accuracy requirements. Model call response delays must align with workshop operation waiting windows. Data involves production safety-related content. Configuration must fix trusted model channels to avoid unauthorized call risks. Single-batch data volume fluctuates with production scale. A reasonable context length threshold must be configured to adapt to data carrying needs for different batches.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Total token count of documents related to a single batch of cell production usually falls within this range, covering full-process data including batching, coating, formation and grading |
maxTokens | 2000–3000 token | Process Q&A typically requires detailed parameter adjustment suggestions, so reply length must cover key information adequately |
quoteMaxToken | 1500 token | Sufficient original document fragments must be retained as Q&A basis, while avoiding context overload |
temperature | 0.1–0.3 | Process parameter Q&A must ensure result accuracy and consistency, avoiding random fluctuations that compromise production parameter reliability |
AIPROXY_API_ENDPOINT | Enter legal endpoint address of enterprise-built proxy | Production data transmission security and stability must be guaranteed, avoiding uncontrollable risks of public network calls |
AIPROXY_API_TOKEN | Enter exclusive token bound to proxy endpoint | Authorization scope of model calls must be restricted to prevent unauthorized access to production-related data |
Three common configuration mistakes
- Phenomenon: Interface shows channel unavailable, or error returns no available channel during call. Cause: Model channel not bound to corresponding process Q&A application group, or proxy endpoint configuration error causes connection failure.
- Phenomenon: Context overflow error returns during model call. Cause:
maxContextparameter not adjusted per actual token count of single-batch production data, exceeding model-supported context length limit. - Phenomenon: Reply result deviates significantly from actual process parameters. Cause:
temperatureparameter set too high, causing random fluctuations in model-generated parameter suggestions that do not meet production accuracy requirements.
How to confirm configuration is complete
- Access the model management page of FastGPT v4.17.0. Confirm configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENpass test connection. - Upload typical production documents for a single batch of cells. Initiate a process parameter query. Confirm model call succeeds with no context overflow error.
- Initiate the same process Q&A three or more times. Confirm consistency of reply results, verifying
temperatureparameter setting meets accuracy requirements. - Review application call logs. Confirm model call response delay aligns with workshop operation waiting window requirements.
The values provided are common starting points and should be measured against the reader's own samples.