What This Category of Data Looks Like
Ledger data for non-ferrous metal smelting comes primarily from three data sources: on-site PLC control systems, laboratory quality inspection systems, and warehouse management systems. Systems store data in structured formats. A single batch ledger entry includes fields such as smelting batch number, raw material input quantity, intermediate product output quantity, final product quality parameters, equipment operating duration, and energy consumption data. Units include kilograms, hours, kilograms of standard coal, and others. Production teams update data per shift: on-shift ledgers sync every 8 hours, and monthly summary ledgers update and archive according to the natural month.
Constraints on Model Access and Configuration Workflows
Multi-source, heterogeneous ledger data formats require adapting to output structures from different systems during model access. Configure sufficient context length to carry complete single-batch entries and avoid truncating critical production parameters. High-frequency shift-level updates create peak data synchronization requests. Limit concurrent model calls to match cloud service provider interface limits and prevent request rejection. Professional smelting terminology and numerical values require lower model output randomness to ensure summary report accuracy. Limit token count per single output for long-text ledger summaries to avoid generated content exceeding interface capacity.
Configuration Settings
This configuration is formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers complete single-batch ledger entries and associated historical data, avoids truncation of critical production parameters |
maxTokens | 2000–3000 token | Matches typical output length of ledger summary reports, prevents generated content from exceeding interface limits |
temperature | 0.2–0.4 | Reduces output randomness, ensures accuracy of professional terminology and numerical values, and meets the strict data requirements of smelting scenarios |
AIPROXY_API_ENDPOINT | Corresponding cloud service provider DeepSeek interface address | Supports professional question-and-answer needs for non-ferrous metal smelting scenarios, leverages corresponding large model capabilities |
AIPROXY_API_TOKEN | Exclusive key generated via the platform | Completes interface identity verification, ensures security of data calls |
toolChoice | auto | Allows models to independently choose whether to call tools to assist ledger summary, adapts to integration needs for multi-field data |
Three Common Configuration Errors
- Symptom: A large language model interface call returns a 429 status code, and the request is directly rejected. Cause: No concurrent number limit is configured, exceeding the concurrent limit of the cloud service provider's interface.
- Symptom: A custom large model cannot be called normally, while the system default large model operates normally. Cause: The
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters are not configured correctly, with incorrect identity verification or interface address settings. - Symptom: The speech recognition function cannot select a target model. Cause: No speech recognition-related plugins are enabled on the platform, or access parameters for the corresponding model are not configured.
How to Verify Successful Configuration
- Check the model access configuration page, confirm that identity verification parameters have no errors, and the interface connection status shows normal.
- Import a single batch of non-ferrous metal smelting ledger data, trigger a summary task, and verify the field completeness and accuracy of the generated report.
- Send multiple concurrent test requests to confirm that 429-type abnormal errors do not occur.
- Enable the speech recognition plugin, confirm that the target model appears in the configuration list and can be selected.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.