What the data for this category looks like
Data for this category typically comes from ERP order and inventory systems, BOM lists, process route files, equipment capacity ledgers, staff shift schedules, work-in-progress statistics, and supplier arrival records. Daily processed documents include sales orders, master production plans, material requirement plans, weekly scheduling tables, production work orders, material shortage lists, and delivery change notices. Data updates follow order changes, exception events, or daily or weekly cycles. Fields include order numbers, material codes, process working hours, equipment numbers, shifts, work-in-progress quantities, and more. Common units are pieces, hours, and days.
Constraints imposed on model access and configuration
Multi-source heterogeneous data sources require model access configurations to support API connections to different systems and adapt to raw data in varying formats. Diverse document types and inconsistent update cycles require configurations that allow dynamic loading of different document fragments, and support real-time or periodic data synchronization. Fields include production-related parameters with specific units. This requires the model to accurately recognize field meanings and unit association rules, to avoid unit confusion or field matching errors during ledger summary. Additionally, requirements for cross-order, cross-process, and cross-equipment correlation analysis mean context configurations must carry sufficient volumes of correlated data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the volume of correlated data from multi-source production ledgers, prevents loss of process and order correlation information due to context truncation |
maxTokens | 4000 tokens | Controls output length per call, adapts to segmented generation requirements for production reports, prevents overly long results that cannot be fully parsed |
quoteMaxToken | 1500 characters | Limits the length of referenced raw ledger data, ensures the model focuses on core scheduling, work order, and order correlation information |
temperature | 0.3–0.5 | Reduces generation randomness, ensures field accuracy in production ledger summaries and consistency in report formatting |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Adapts to the network path for model calls, ensures successful connection to the selected large model service |
AIPROXY_API_TOKEN | Fill in the key generated for the account | Completes identity verification for model calls, ensures the security of interface calls |
Three common configuration errors
- Partial process information is missing from single-round generated production reports, and the interface prompts token call limit exceeded. The cause is failure to properly adjust
maxTokensandmaxContextparameters based on the total data volume of the summarized ledger, leading to context or output length exceeding model limits. - Calls fail when configuring the deepseek-R1 model, and the interface returns identity verification failure. The cause is incorrect filling of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters, or incorrect key permission configuration. - Uploaded image-format production scheduling tables cannot be parsed, and the ledger summary result is empty. The cause is failure to enable the
visionconfiguration and convert images to a text format processable by the model, preventing the model from recognizing image content.
How to confirm the configuration is correct
- Manually import a standard sales order and weekly scheduling table document, trigger a model call, and check if the returned ledger summary result includes all core fields.
- View model call logs in FastGPT v4.17.0, confirm that the actual call values of
maxTokensandmaxContextmatch the configured settings, with no limit exceeded prompts. - Verify the validity of
AIPROXY_API_TOKEN, confirm normal return of model responses via test interface calls. - Try passing production data with units, check that units in the summary report match the original data with no confusion.
The values provided are common starting points and should be validated against local test samples.