What the data for this category looks like
Technical documents for active pharmaceutical ingredient and pharmaceutical intermediate production include batch production records, material balance sheets, reaction and purification records, cleaning validation records, impurity profile reports, deviation and change documents, and more. Data sources cover registered process documents, raw material batch ledgers, DCS systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Documents are updated per production batch and process step. Fields include batch number, reaction temperature, yield data, impurity content, solvent residue levels, and more. Most formats are structured tables and semi-structured text.
What constraints do these characteristics impose on model access and configuration
Documents for this category originate from dispersed sources, have high update frequencies, and include numerous specialized chemical terms and compliance-related fields. This imposes multiple constraints on model access and configuration. Heterogeneous data from multiple systems requires compatible input format configurations to adapt to outputs from different data sources. Frequently updated batch documents demand sufficient model context length to cover multiple associated process and quality records. Accurate understanding of specialized terms requires setting model parameters to be rigorous, to avoid generating review results that do not meet compliance requirements. Additionally, review errors may lead to batch scrap or compliance risks, requiring stable model calls and accurate identity verification.
How to set configurations
The following configurations are based on FastGPT v4.17.0, and are adapted for the cross-review of technical documents for active pharmaceutical ingredient and pharmaceutical intermediate production:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the total length of multi-step reaction records, impurity profile reports, and associated batch documents |
maxTokens | 2000–3000 token | Limits the output length of single-batch review results to avoid exceeding model limits |
quoteMaxToken | 1500 token | Restricts the maximum truncated length of a single referenced document to ensure complete review evidence |
| Workflow/API tool selection | auto | Automatically triggers document cross-referencing and compliance check tools to improve review accuracy |
temperature | 0.1–0.3 | Reduces generation randomness to ensure rigor and consistency of professional review results |
AIPROXY_API_ENDPOINT | Deployed OneAPI interface address | Points to the self-built model proxy interface to enable local model access |
AIPROXY_API_TOKEN | Valid key obtained from OneAPI | Completes identity verification for model calls to ensure valid interface access permissions |
Three common configuration mistakes
- Setting
toolChoicetofalse. This prevents review results from automatically associating referenced documents. The symptom is that impurity profile reports or batch ledger content are not cited in the output. The cause is that tool call permissions are not enabled, so cross-document verification cannot be completed. - Configuring
AIPROXY_API_TOKENto not match the actual deployed OneAPI key. The symptom is a 401 status code in responses. The cause is failed identity verification. This occurs even if the key works in Postman tests, but the correct key was not entered in FastGPT's configuration. - Setting
maxContextbelow4000 token. This truncates multi-step reaction records. The symptom is that review results omit compliance assessments of intermediate reaction steps. The cause is insufficient context length to cover complete process documents.
How to confirm the configuration is valid
- Call the configured
AIPROXY_API_ENDPOINTand use the configuredAIPROXY_API_TOKENto send a basic text generation request. Confirm a 200 status code and normal response content are returned. - Upload a single batch's production record and impurity profile report to trigger a technical document cross-review task. Confirm the output includes explicit document citation snippets.
- Adjust the
temperatureparameter to0.5and observe changes to the review result wording. Confirm that model output adjusts accordingly after the parameter modification. - Check the FastGPT backend model call logs. Confirm there are no error records related to 401 status codes, timeouts, or context truncation.
The values provided are common starting points, and should be measured against relevant operational samples.