Data Characteristics for this Category
GMP compliance registration documents originate from regulatory bodies such as the National Medical Products Administration (NMPA), the U.S. Food and Drug Administration (FDA), and the European Medicines Agency (EMA). These include regulations, guidelines, announcements, and technical review principles. Internal company documents like quality management system files, manufacturing process specifications, testing methods, and validation reports also contribute. Data update frequency is relatively stable, typically revised annually, but may see temporary updates during emergencies or new drug approval needs. Documents are primarily in PDF, Word, and XML formats, containing numerous tables, figures, and specialized terminology. Fields and units are highly standardized, for example, batch numbers, expiry dates, production dates, test results (e.g., content, purity, microbial limits), and units of measurement (mg, g, mL, IU). Strict requirements exist for numerical precision and compliance descriptions.
Constraints from these Characteristics on "Knowledge Base Retrieval and Recall"
The stable update cycle of GMP compliance documents means knowledge base content requires regular, but not high-frequency, bulk updates and version management. Complex tables and figures in documents demand a knowledge base chunking strategy that effectively handles cross-chunk table structures or image-text associations, preventing important information from being fragmented during chunking. The presence of specialized terminology and standardized fields makes identifying synonyms and near-synonyms, as well as querying numerical ranges, critical. For example, a query for "batch" should also recall information related to "batch number." Furthermore, the rigor of registration documents requires retrieval results to be highly accurate and traceable. Any imprecise recall could lead to compliance risks, necessitating higher recall precision and completeness. The ability to recognize and convert units of measurement is also an implicit constraint, ensuring a query for "100 milligrams" accurately matches records for "0.1 grams."
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size | 500-800 characters | Balances paragraph completeness and recall efficiency. Avoids long paragraphs diluting key information and short paragraphs fragmenting context. |
Chunk overlap | 100-200 characters | Ensures contextual continuity across paragraphs, especially when processing tables or definitional descriptions. |
Embedding Model | bge-large-zh-v1.5 | Provides more precise semantic understanding for specialized terminology and regulatory texts in the Chinese biomedical field. |
Recall count | Top 5-8 entries | Increases recall rate, especially during the initial retrieval phase, providing a richer candidate set for subsequent re-ranking. |
Similarity threshold | Calibrate by actual measurement | Adjusts through a test set based on specific datasets and business requirements to balance recall precision and completeness. |
Rerank result count | Top 3 entries | Focuses on the most relevant results, reducing user screening burden and improving information acquisition efficiency. |
Three Common Mistakes
- Incomplete knowledge base query results or irrelevant information appearing: This occurs due to improper chunking strategies, leading to key information being fragmented or mixed with unrelated content, affecting the semantic accuracy of embedding vectors.
- No search results after importing PDF files, or an
invalid configuration parameter nameerror: This may be due to improper file parser configuration, failing to correctly extract text content, or incorrect index model configuration parameters during knowledge base initialization. - Knowledge base search results fail to accurately match queries for specific batch numbers or date ranges: This happens when the knowledge base does not effectively identify and index structured data, or the query model is not optimized for understanding entity information like numbers and dates.
How to Confirm Proper Configuration
- Prepare a test set containing typical regulatory clauses, quality standards, and production processes. Execute queries and manually evaluate the completeness and relevance of the recalled content, ensuring no critical information is missed.
- Use a question set with clear answers to test the knowledge base's question-answering accuracy. Pay special attention to the matching of specialized terminology, numerical units, and regulatory clauses, comparing them against expected results.
- Simulate query scenarios of varying complexity, such as queries involving multiple keywords, time ranges, or specific batch information, to check if the recall results accurately capture these limiting conditions.
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.