What this category of data looks like
Business data for chemical pharmaceutical preparations manufacturing comes from approved formulation processes, material batch ledgers, MES systems, electronic batch records, equipment and environmental monitoring data, LIMS laboratory management systems, and quality management systems. Daily associated documents include master production documents, batch production records, batch packaging records, cleaning records, inspection records, deviation and CAPA documents, change records, and batch release review forms. Data is updated once per production batch, triggered by key process steps. Equipment environmental monitoring data is updated per a fixed monitoring plan. Data fields include material batch numbers, process parameter values, test results, deviation descriptions, release status, and no additional non-production supplementary fields.
What constraints these characteristics impose on model integration and configuration
The batch-associated data structure for chemical pharmaceutical preparations manufacturing is fixed and covers multiple document types. Sufficient context length must be configured during model integration to accommodate complete batch-level documents. Data from multiple system sources has varying formats. Configured models must support unified parsing of multi-source data. Data updated per batch and key steps requires fast response to production operation windows. Model call response times must align with business rhythms. Data under GMP compliance requirements must be traceable. Relevant parameters supporting traceable references must be enabled during configuration.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total token length of complete batch-level documents, avoiding truncation of critical batch record content |
temperature | 0.1–0.3 | Ensures rigor of analysis results and meets data consistency requirements |
quoteMaxToken | 2000 characters | Limits the length of a single referenced document to avoid context overload |
vision | Enabled | Supports parsing non-text data such as equipment parameter screenshots and weighing record images in batch records |
AIPROXY_API_ENDPOINT | Fill in per deployed large model service | Adapts to the access address of domestic large models to meet compliance requirements |
AIPROXY_API_TOKEN | Fill in per authorized key | Completes identity verification for model calls and ensures data access security |
Three common configuration errors
- No options for domestic large models such as Tongyi Qianwen appear in the interface dropdown list. The cause is that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured, or the access mapping for the corresponding large model has not been added in the platform. - Model calls return parameter incompatibility or format error prompts. The cause is that the value of
maxContexthas not been adjusted based on the text length of batch records, or thevisionparameter has not been enabled to adapt to image-based production data. - Importing the OneAPI address and key does not trigger model calls. The cause is that the OneAPI access address has not been correctly entered in the
AIPROXY_API_ENDPOINTconfiguration item, or the key is not bound to the call permissions of the target model.
How to confirm successful configuration
This verification process applies to FastGPT v4.17.0.
- The values entered for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare verified to match the deployed large model service. Connectivity is verified using the platform’s built-in test interface. - A text snippet from a single batch record is input as a test. The model’s returned analysis result is confirmed to include parsed content for the corresponding fields.
- The
visionparameter is enabled, then an equipment parameter screenshot is uploaded. The model’s ability to correctly recognize text information in the image is verified. maxContextis set to 8000 token, then text exceeding this length is input. The model’s behavior is verified: either normal truncation of content or a context overflow prompt is displayed.
The values provided here are common starting points and should be validated against samples specific to the actual deployment.