What the data for this category looks like
The ledger data for active pharmaceutical ingredient and pharmaceutical intermediate manufacturing comes from registered process documents, raw material batch records, DCS systems, weighing systems, LIMS, chromatography data systems, and QMS. Data updates follow each production batch and process step cycle, and include batch production records, material balance documents, reaction and purification records, cleaning validation records, impurity profile reports, deviation and change documents, and more. Core fields include batch identifier, feeding quantity, reaction duration, purification parameters, impurity peak data, deviation descriptions, and more. Units use professional production measurement standards such as kilograms, hours, and milliliters.
Constraints on model access and configuration
Heterogeneous data sources across multiple systems require models to support ledger data in different formats during access, to prevent format parsing errors. Single-batch data volumes are large and include specialized chemical terminology, so model output randomness must be limited to ensure accuracy for key fields. High-frequency batch-level updates require configuration items to quickly adapt to the context scale of a single batch, while also ensuring data transmission privacy to avoid leakage of sensitive production information. Additionally, extraction accuracy for core business fields such as impurity profiles and yield directly impacts report compliance, so targeted configuration of the model’s generation logic is needed.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-document splicing for single-batch production ledgers, covering all fully associated data including batch records, impurity profiles, and deviation documents |
maxTokens | 4000–6000 token | Meets output length requirements for long report generation, accommodating core business information such as summarized yield, impurities, and process transfers |
temperature | 0.1–0.3 | Reduces model output randomness, ensuring consistent extraction of key professional fields such as batch numbers and impurity content |
| Workflow/API tool selection | auto | Automatically invokes knowledge base retrieval, matches heterogeneous ledger data from multiple system sources, and improves information recall completeness |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the FastGPT v4.17.0 platform | Connects to compliant model call links, adapting to privacy protection requirements for pharmaceutical production data |
AIPROXY_API_TOKEN | Fill in the exclusive key generated by the FastGPT platform | Ensures identity verification for model calls and security of data transmission |
Three common configuration mistakes
- Phenomenon: The large model returns an empty response after being called, or the generated report omits fields related to impurity profiles. Cause:
maxTokensis set too low, which cannot accommodate the full content of the impurity profile report, resulting in truncated model output or failure to generate valid content. - Phenomenon: Multiple calls to the same query return inconsistent results from the private knowledge base. Cause:
temperatureis set too high, leading to random deviations in the model’s extraction of professional terms, andtoolChoiceis not restricted to a fixed retrieval mode, resulting in unstable call logic. - Phenomenon: Tasks cannot be triggered normally after configuring a speech model. Cause: The corresponding
AIPROXY_API_TOKENis not bound in the speech configuration module of FastGPT v4.17.0, or a speech model type not supported by the platform is selected.
How to verify successful configuration
- Upload complete production ledger documents for a single batch, trigger the ledger summary task, and check if the output report includes preset core business fields such as batch number, feeding quantity, and reaction duration.
- Trigger the same summary request multiple times, verify consistency of the output results, and confirm that the
temperatureparameter value meets the business’s accuracy requirements. - View model call logs to confirm there are no format errors in the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and that the call link returns a normal status code. - Test the speech access function, input a voice command to trigger ledger summary, and confirm that the command is correctly parsed and the corresponding report content is returned.
The values provided here are common starting points and should be evaluated against the reader's own samples.