Knowledge Base Retrieval and Recall for Bioequivalence

Data Characteristics

Bioequivalence regulation data originates from guidelines, technical review requirements, regulatory documents, industry consensus, and internal Standard Operating Procedures (SOPs) published by various national drug regulatory agencies. These documents typically exist in PDF, Word, and Excel formats. They contain extensive specialized terminology, formulas, charts, and case studies. Update frequency is relatively low, primarily occurring during regulatory revisions or the release of new guidelines. Document structures are complex and hierarchical, encompassing both macroscopic policy interpretations and microscopic, specific experimental methods and data processing specifications. Fields include drug names, dosage forms, specifications, reference preparation information, subject selection criteria, statistical analysis methods, biological sample testing methods, and judgment criteria. Units commonly include concentration (e.g., ng/mL), time (e.g., h), area (e.g., AUC), and statistical indicators (e.g., confidence interval percentage).

Constraints on Knowledge Base Retrieval and Recall

The specialized nature and complex structure of bioequivalence documents demand high precision in knowledge base retrieval. Documents contain extensive specialized terminology, requiring precise matching or contextual understanding to avoid generalized recall. The low update frequency of documents means knowledge base content is relatively stable, but each update requires accurate incremental or full synchronization. Diverse document formats and complex internal structures, such as tabular data and nested lists, require the knowledge base to have robust file parsing capabilities to accurately extract text and structured information. Excel files containing bioequivalence data, such as pharmacokinetic parameters, may include a large amount of fine-grained data within a single file. This necessitates meticulous chunking to prevent information loss or incomplete recall. Furthermore, much critical information appears in charts, and simple text retrieval is insufficient.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk Length`	`800–1200 characters`	Accommodates the longer professional paragraphs and detailed descriptions common in bioequivalence documents, ensuring contextual completeness.
`Overlap Length`	`100 characters`	Helps the model understand logical connections across paragraphs, especially when defining terms or describing processes.
`Recall Count`	`8–12 items`	Balances coverage while avoiding retrieval of too many irrelevant segments, which would increase the model's processing burden.
`Similarity Threshold`	Calibrate by measurement	Bioequivalence terminology is highly specialized. Test with actual data to determine a threshold that distinguishes professional relevance from general mentions.
`Rerank Return Count`	`5 items`	Further refines recall results, focusing on core information most relevant to the query.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses large PDF or Word files common in bioequivalence reports, providing ample parsing time.

Common Pitfalls

Missing data from Excel files in query results: This occurs when Excel file parsing fails to fully recognize all data regions or rows, leading to unindexed content.
Inability to cite image content in answers after uploading images: This happens because the knowledge base's default text parsing process does not include Optical Character Recognition (OCR) or image content understanding, preventing image information from being indexed.
API calls returning Error: write EPROT: This indicates SSL certificate validation failure or proxy issues in the network connection when a third-party system calls the knowledge base API, preventing a secure communication link from being established.

Verification Steps

Upload representative bioequivalence PDF and Excel files. Verify that file content, especially tabular data and nested lists, is fully parsed.
Execute queries containing specialized terminology and regulatory clauses. Check if recall results include corresponding original text segments. Observe changes in recall precision by adjusting Similarity Threshold and Recall Count.
For uploaded documents with charts, attempt to ask questions related to chart content. Confirm if the model can make associations based on text descriptions or metadata.
Use the API interface to upload test files and perform queries. Check if the returned status codes and data formats meet expectations, without connection or parsing errors.

The values provided are common starting points. Measure against your own samples to determine optimal settings.

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.