Data Characteristics
Bioequivalence (BE) study regulations and standard documents primarily originate from regulatory bodies such as the National Medical Products Administration (NMPA), European Medicines Agency (EMA), and U.S. Food and Drug Administration (FDA). These documents include guidelines, technical review requirements, legal provisions, and Q&A compilations. They are typically in PDF, DOCX, or HTML formats. These documents are highly structured, containing extensive specialized terminology, dosage units (e.g., mg/mL, Cmax, AUC), statistical indicators (e.g., 90% confidence interval), and trial design specifications. Update frequency depends on regulatory policy adjustments or new scientific understanding, potentially ranging from several times a year to once every few years. Content usually focuses on drug absorption, distribution, metabolism, and excretion (ADME) characteristics, statistical analysis methods, and exemption conditions.
Constraints on Vector Models and Indexing
The specialized and structured nature of bioequivalence regulatory documents imposes specific requirements on vector models and indexing. First, documents contain specialized terms and abbreviations like AUC, Cmax, Tmax, and CV%. Vector models must accurately understand their contextual meaning, avoiding over-generalization that leads to semantic drift. Second, documents often include tables, charts, and complex formulas. This non-textual information can lose its relevance during chunking, impacting retrieval accuracy. Third, regulatory updates often involve localized revisions or additions. The indexing mechanism must support incremental updates and accurately identify differences between new and old versions to ensure timely and accurate retrieval results. Finally, the need for precise matching of dosage units and statistical indicators requires vector models to have some semantic recognition capability at the numerical and unit levels.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Ensures each chunk contains complete regulatory provisions or concepts, preventing semantic fragmentation. |
Chunk Overlap Length (Chunk Overlap Length) | 100–150 characters | Connects the context of adjacent chunks, improving recall for cross-paragraph queries. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances retrieval precision and recall, reducing irrelevant results. |
Recall count (Recall Count) | 10–15 items | Covers more potentially relevant information, providing sufficient candidates for subsequent re-ranking. |
Rerank result count (Re-ranked Return Count) | 5–8 items | Focuses on the most relevant results, improving the quality of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Handles the parsing time of large PDF or DOCX files, preventing timeout errors. |
Common Pitfalls
- No search results or irrelevant results after index creation: This often occurs due to an improper chunking strategy, where regulatory provisions are fragmented into meaningless pieces, or the vector model fails to accurately capture the semantics of specialized terms.
- New and old information confusion or failure to retrieve the latest provisions after document updates: This happens when an incremental indexing mechanism is not used, or the update strategy fails to correctly identify and replace outdated content.
- Chunking errors or parsing failures when processing CSV files: This may relate to changes in the parser's logic for specific encoding or formats of CSV files after a FastGPT version upgrade, leading to incorrect extraction of file content.
Verification of Configuration
- Select test questions containing key regulatory provisions and specialized terms. Check if retrieval results accurately hit the original passages.
- Upload a new version of a regulatory document with minor revisions. Confirm the system identifies and prioritizes relevant information from the new version.
- Ask questions about tabular data or numerical values with units in the document. Verify that the Q&A results correctly extract and present this information.
The values provided 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.