Data Characteristics in This Category
Biopharmaceutical registration and declaration involve diverse data sources. These include clinical trial reports, non-clinical study reports, manufacturing process documents, quality standards, and pharmaceutical research data. Documents are typically in PDF, Word, or scanned image formats. They have complex structures and contain extensive specialized terminology, charts, and tables. Data update frequency is relatively low, primarily concentrated during declaration preparation and review feedback phases. Documents often include international units (e.g., mg/kg, mol/L) and professional abbreviations. Field naming conventions vary by source institution but generally follow international guidelines such as ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use).
Constraints Imposed by These Characteristics on "Reference Source and Traceability"
The complexity and specialized nature of registration and declaration documents demand high accuracy in reference sourcing and traceability. Key data and conclusions within documents must be precisely traceable to their original sources. This supports the scientific validity and compliance of the declaration. Diverse document formats require parsing systems with robust text extraction and structuring capabilities. Low update frequency means historical version management and reference consistency are critical. The prevalence of specialized terminology and measurement units makes semantic understanding and entity recognition essential for ensuring reference quality. Any reference deviation can lead to review risks and potentially affect drug approval processes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances semantic completeness with recall accuracy. Avoids overly long paragraphs diluting key information or overly short paragraphs losing context. |
Chunk Overlap Length | 50 characters | Ensures key information correlation across segments. Enhances recall robustness. |
Recall count | Top 8–12 entries | Covers a sufficient number of potentially relevant knowledge points. Balances recall rate and computational overhead. |
Similarity threshold | 0.75 | Filters out low-relevance content. Improves reference quality and reduces interference from irrelevant information. |
Rerank result count | Top 5 entries | Focuses on the most relevant references. Avoids displaying excessive unnecessary details in complex declaration scenarios. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses complex parsing needs for large or scanned documents. Ensures file processing completion. |
Three Common Pitfalls
- Queries fail to return local knowledge base content, or references appear correct in the display but are not in the answer. This might be because the
Similarity thresholdis set too high, filtering out relevant segments. Alternatively, the model might not fully utilize context information when generating answers. - Document parsing takes too long or fails, preventing reference establishment. This might be because
PARSE_FILE_TIMEOUT_SECONDSis set too short. It cannot handle large PDFs or scanned documents with complex charts. - Reference sources point to inaccurate document page numbers or paragraphs. This might be because document structured parsing is not precise enough. It fails to correctly identify sub-sections or logical hierarchies within tables.
How to Confirm Correct Configuration
- For core declaration documents, build a test set with key questions and expected answers. Verify the accuracy of references in the answers and their locations in the original text.
- Upload typical large declaration documents (e.g., pharmaceutical research reports, clinical trial summary reports). Check if file parsing status is successful. Verify if the segmentation effect meets expectations.
- Randomly select multiple question-answer pairs. Cross-check if each reference source's page number and paragraph precisely correspond to the original document content. Pay special attention to references involving measurement units and specialized terminology.
The values given are common starting points and should be measured against specific 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.