Deployment and Upgrade for Bioequivalence Regulations

Data Characteristics for This Category

Bioequivalence (BE) study regulations and standards primarily originate from guidelines and directives published by the National Medical Products Administration (NMPA), along with technical documents from international organizations like ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use). These documents are typically in PDF, Word, or plain text formats. They often contain extensive specialized terminology, formulas, charts, and tables. Update frequency is relatively stable, with new versions released when new drug approval policies or international guidelines are revised, usually quarterly or annually. Document structures are rigorous and well-sectioned, often covering pharmacokinetic parameters (e.g., Cmax, AUC), statistical evaluation criteria, and bioanalytical methods. Fields and units are highly standardized; for example, Cmax units are ng/mL, AUC units are ng·h/mL, and confidence intervals frequently appear as 90% CI.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The data characteristics of bioequivalence regulatory documents impose specific requirements on AI Agent deployment and upgrades. The specialized terminology and statistical symbols in these documents necessitate that the RAG model effectively identifies and preserves semantic integrity during text chunking and embedding, preventing critical information loss due to improper segmentation. Although the update frequency is not high, each update may involve revisions to core judgment criteria. Therefore, a flexible knowledge base update mechanism is required to support rapid import of new document versions, incremental updates, or version management. Furthermore, chart and table information within documents, especially tables containing statistical results, requires enhanced OCR or table parsing capabilities to ensure these structured data are correctly extracted and used for question answering. The deployment environment must support rendering specific fonts and symbols to avoid issues like image loading failures.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`Chunk size` (Chunk Size)	`500–800 characters`	Ensures the completeness of specialized terms and statistical descriptions, preventing semantic breakage during segmentation.
`Recall count` (Recall Count)	`Top 5–8 chunks`	Ensures coverage of multiple relevant regulations or guidelines, increasing information comprehensiveness.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Improves recall accuracy for highly specialized texts with precise vocabulary requirements.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample parsing time when processing complex documents like PDFs and Word files.
`maxContext`	`32000`	Accommodates the context requirements of lengthy regulations or guidelines, reducing information truncation.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Handles regulatory documents containing numerous charts or high-resolution images.

Three Common Pitfalls

Inaccurate knowledge base query results, where the large model fails to effectively summarize, may be due to a similarity threshold set too low. This can lead to the recall of irrelevant text snippets, diluting effective information.
Images or table content in uploaded documents may not be recognized, leading to missing information in question answering. This typically occurs due to an unconfigured or improperly configured OCR service, or if the FastGPT document parser version does not support embedded parsing of specific image formats.
After offline deployment, model testing may show a "request error." This could be related to model service configuration in the offline environment, such as incorrect model_url or authentication token settings, preventing FastGPT from accessing the locally deployed large model.

How to Verify Correct Configuration

Upload typical regulatory documents containing key bioequivalence parameters (e.g., Cmax, AUC) and statistical requirements (e.g., 90% CI). Verify that the Q&A system can accurately extract and interpret these values and concepts.
For a specific regulatory revision, import the updated document. Test if the system can correctly cite the latest provisions and compare differences with older provisions to confirm the knowledge base update mechanism is functioning correctly.
Check document parsing logs to ensure the PARSE_STATUS for all uploaded files is success, especially for complex files containing charts and tables. This confirms no information is missed due to timeouts or parsing errors.

The values given are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.