What the Data for This Category Looks Like
Data for this category is sourced from registration process documents, raw material batch records, DCS systems, LIMS systems, chromatography data systems, and QMS maintained by the synthesis, purification, quality, and registration support departments of active pharmaceutical ingredient enterprises. Daily documents include batch production records, material balance sheets, reaction and purification records, cleaning records, impurity profile reports, deviation and change documents. Data updates follow the rhythm of each production batch and process step. Fields include reaction parameters, impurity content, solvent residue, batch number, and more. Units use common chemical detection units such as molar ratio and ppm.
Constraints Imposed by Data Characteristics on Model Connection and Configuration
Multi-source heterogeneous data sources require model connections to adapt to output formats of different systems, to avoid data parsing errors. High-frequency batch-level updates require configuration to support batch data processing context capacity, to prevent task interruptions caused by context overflow. The structure and terminology of professional chemical documents require model configuration to support accurate understanding of domain text, and to adapt to parsing requirements for exclusive business logic such as impurity profiles and process routes.
How to Configure Settings
These configurations are developed based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to full context loading of long documents such as batch production records and impurity profile reports, preventing truncation of professional chemical content |
temperature | 0.1–0.3 | Reduces random output, ensuring consistent results for tasks including impurity evaluation and process compliance judgment |
quoteMaxToken | 2000 token | Matches the core fragment length of a single professional document in this category, ensuring complete recall of content such as impurity profiles and reaction records |
| Workflow/API tool selection | auto | Automatically selects tools to adapt to retrieval and parsing requirements for multi-source data, supporting cross-document verification in supplier audits |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Adapts to the dedicated interface path for large model calls, ensuring connectivity for model access |
AIPROXY_API_TOKEN | Fill in according to authorization credentials | Completes identity verification for model access, ensuring compliance of call permissions |
Three Common Configuration Mistakes
- Setting the
maxContextparameter to too small a value will return a "context overflow" error during search testing. This occurs because the configuration fails to adapt to the long document loading requirements of this category, leading to truncated professional chemical content that prevents successful retrieval. - Failing to correctly fill in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwill return 401 or 404 status codes when calling the large model. This is caused by incorrect identity verification or interface path configuration, preventing connection to the target large model. - Enabling the
visionparameter without configuring corresponding visual input processing rules will return empty results when uploading impurity profile scans. This occurs because the configuration fails to adapt to visual parsing requirements for chromatograms and scanned documents in this category.
How to Verify Successful Configuration
- Upload a single batch production record document, and check if the model can correctly extract core fields such as reaction parameters and batch information. This verifies if the context loading configuration takes effect.
- Submit a retrieval request related to supplier qualifications, and check the number of recalled results and their matching degree. This verifies if
quoteMaxTokenand retrieval configuration adapt to business requirements. - Call the large model to complete impurity profile compliance judgment, and check the consistency of output results. This verifies if the
temperatureparameter setting meets business requirements. - View the model call log, and confirm that the call statuses for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare both successful. This verifies if the access permission configuration is correct.
The values provided are common starting points and should be measured against the reader's own samples.