What data looks like for this category
Inspection and process record audit for active pharmaceutical ingredient and pharmaceutical intermediate production covers data sources including registered process documents, raw material batch ledgers, DCS production control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Data updates are triggered upon completion of each production batch and each process step, with a frequency of once per step per batch. Daily documents include batch production records, material balance ledgers, reaction and purification process records, cleaning validation records, impurity profile reports, and deviation and change application documents. Data fields include batch identifiers, process parameter values, test result values, document numbers and change order numbers. Some documents use a mixed format of structured tables and unstructured text.
What constraints these characteristics impose on model access and configuration
Multiple heterogeneous data sources require unified mapping of different system data formats during configuration, to avoid audit deviations caused by field misalignment. The high-frequency batch-level update feature requires a limited model context window, as a larger window will increase processing delay for single-batch audits. The mixed structured and unstructured document structure requires configuring parsing rules adapted to mixed formats, to ensure the model can accurately extract core audit information. Compliance-related audit requirements require that the model call link has traceable permission configuration, to prevent unauthorized leakage of sensitive process and quality data.
How to set the configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of single-batch production records and associated quality documents, avoiding truncation of core audit data |
maxTokens | 2000–3000 token | Meets the detail level required for audit result output, covering multi-dimensional conclusions such as impurity analysis and yield calculation |
quoteMaxToken | 1500 token | Limits the length of referenced source data, ensuring the accuracy and readability of audit basis |
temperature | 0.1–0.3 | Reduces random output, ensures consistency of audit conclusions, and complies with compliance requirements |
AIPROXY_API_ENDPOINT | External access address of the deployed FastGPT proxy service | Enables unified routing and permission control for model calls |
AIPROXY_API_TOKEN | Exclusive access key bound to the application | Restricts unauthorized model calls, meeting data security requirements |
Three common mistakes
- Directly passing the String-type result output from code blocks into large models triggers a "Question is empty" error. The cause is failure to handle special characters such as line breaks and escape symbols in the result, leading to model interface parsing failure.
- Filling in the container internal port address when configuring
AIPROXY_API_ENDPOINT. Even if the container port is accessible locally on the host, the proxy service still cannot connect, eventually triggering a timeout error. - Failing to associate the corresponding language model configuration after creating the index model, causing audit tasks to fail to call large models for inference, resulting in an abnormal state of missing model.
How to confirm the configuration is complete
- Call the model test interface, pass in single-batch batch production record text, and check whether the returned result includes conclusions corresponding to audit dimensions such as impurity analysis and yield calculation, with no obvious content truncation or missing.
- View the model call log, confirm that
AIPROXY_API_TOKENverification is passed, and there are no permission-related error messages. - Adjust the
temperatureparameter to different ranges, compare the consistency of the two output results, and verify that the parameter configuration has taken effect. - Test the multi-source data access scenario, pass in mixed data from LIMS and DCS, and check whether the model can correctly identify and associate corresponding fields.
The values provided are common starting points and should be measured against the reader's own samples.