Data overview for this category
Core data for this category comes from the formula and process library, raw material batches, reactor DCS, weighing systems, LIMS, and warehouse systems. Updates trigger per production batch or each critical step. Daily retained documents include batch production instructions, feeding review slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Each document links to a unique batch number, and contains fields such as feeding volume, reaction temperature, and cleaning residue detection values. No unified cross-system format exists, so data is associated and integrated by batch dimension.
Constraints for model access and configuration
The multi-batch switching production mode, heterogeneous multi-source data structure, and high-risk production scenario of this category create multiple constraints for model access and configuration. Exclusive documents updated per batch must link strictly to the corresponding batch context to prevent cross-batch data from interfering with equipment inspection logic. Documents from heterogeneous sources require unified formatting rules to ensure the model accurately parses field information from different systems. High-risk production requirements limit model output randomness, while controlling context length to avoid truncation of critical information and ensure inspection result accuracy.
How to set configurations
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the context length of a complete single-batch production process, avoiding truncation of critical information such as feeding and reaction steps |
temperature | 0.2–0.4 | Reduces randomness in model output, ensures consistency of equipment inspection logic, and meets strict chemical production scenario requirements |
| Workflow/API tool selection | auto | Automatically triggers tool calls to adapt to complex needs of multi-dimensional equipment parameter verification and historical document retrieval |
quoteMaxToken | 1500 token | Prioritizes retrieval of associated documents within a single batch, avoiding interference from cross-batch unrelated data on inspection results |
AIPROXY_API_TOKEN | Valid token for the corresponding model service | Used for secure connection to compliant model interfaces, ensuring legitimacy of data transmission |
AIPROXY_API_ENDPOINT | Exclusive address of the deployed proxy service | Adapts to unified call paths for multi-system data access, simplifying configuration complexity |
Three common mistakes
- Phenomenon: Tool call results deviate from equipment inspection logic, output does not match actual production processes, and even provides incorrect maintenance suggestions. Cause: The
temperaturevalue is set too high, leading to excessive randomness in model output that does not align with strict chemical production requirements. - Phenomenon: Rerank model video memory usage continues to rise, eventually triggering an out-of-memory error and failing to process subsequent requests. Cause: No automatic context truncation rule is configured, and multi-batch production data is continuously loaded without automatic release of video memory resources.
- Phenomenon: No available channel list appears on the OneAPI page, making it impossible to select the target model. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the proxy service is not running normally.
How to confirm correct configuration
- Input single-batch feeding review-related questions to verify whether the model recalls associated documents such as feeding review slips and reaction process records for the corresponding batch.
- Trigger a tool call test to confirm that
toolChoiceautomatically selects appropriate tool logic that meets equipment inspection verification needs. - Monitor model service video memory usage to confirm no abnormal continuous increase occurs after multi-batch testing.
- Check the OneAPI channel list to confirm that the corresponding model channel is displayed normally and can be called normally.
The values provided are common starting points and should be measured against the reader's own samples.