Data Characteristics in this Category
Contract Sales Organizations (CSOs) prepare registration and declaration documents. This process involves data from various stages of drug development. This includes clinical trial reports, pharmaceutical research data, non-clinical study reports, and manufacturing quality management documents. This data typically exists in a mix of structured (e.g., database records, tabular data) and unstructured (e.g., PDF documents, Word documents, scanned images) formats.
Update frequencies vary. Clinical trial data may update regularly as trials progress. Pharmaceutical and non-clinical research data are relatively stable but may be revised during the declaration process based on review comments. Document structures are complex. They contain extensive specialized terminology, abbreviations, and specific formatting requirements, such as the CTD (Common Technical Document) format mandated by ICH guidelines. Field and unit standardization is high. For example, dose units (mg, g), concentration units (μg/mL, %), and time units (days, weeks, months) must strictly comply with regulatory requirements.
Constraints Imposed by these Characteristics on "Model Access and Configuration"
The mixed structure of CSO data challenges model access. It requires models capable of processing both structured and unstructured information simultaneously. The large volume of unstructured documents, especially in PDF format, demands that models have efficient text extraction and parsing capabilities, as well as the ability to recognize tabular and image content.
The irregular data update frequency, particularly the dynamic changes in clinical trial data, requires model access solutions that support incremental updates and version management. This ensures the timeliness of the knowledge base. The specialized terminology and abbreviations in documents, along with specific formatting requirements like CTD, mean that models need pre-training or fine-tuning with domain knowledge. This enables accurate content understanding and the generation of compliant declaration documents. Additionally, strict field and unit specifications require models to precisely identify and maintain the accuracy of this critical information during data extraction and generation. This avoids compliance risks due to unit confusion.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Balances contextual coherence with retrieval efficiency, adapting to the long-text characteristics of declaration documents. |
chunk_overlap | 100 characters | Ensures contextual continuity at chunk boundaries, preventing critical information from being cut off. |
top_k | 5 | Balances relevance with retrieval speed, meeting the demand for quickly locating information. |
similarity_threshold | 0.75 | Improves the relevance of retrieval results, reducing interference from irrelevant information. |
max_tokens | 4096 | Accommodates potentially lengthy descriptions in declaration documents, ensuring complete model output. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time-consuming parsing of large PDF documents, preventing processing failures due to timeouts. |
Common Mistakes
- Model responses contain factual errors or hallucinations. This happens because the model has not been fine-tuned with specialized biomedical domain knowledge, leading to misunderstandings of professional terms and concepts.
- After uploading PDF files, the knowledge base fails to correctly extract tabular data or chart descriptions. This manifests as
document_parse_erroror empty content fields. The default document parser's inability to handle complex PDF layouts causes this. - The model provides inconsistent answers when addressing questions involving different versions of regulations or guidelines. This occurs because the knowledge base lacks effective version management, leading the model to retrieve outdated or conflicting information.
Verification Steps
- Upload a series of CTD-format PDF documents containing complex tables and charts. Check if the knowledge base correctly parses and extracts all key information.
- Use query statements containing specific biomedical professional terms and abbreviations. Verify if the model accurately understands and retrieves relevant document snippets from the knowledge base. Evaluate if the
similarity_scoreof the retrieved results is above the expected threshold. - Query for common fields in registration and declaration documents, such as time, dosage, and units. Cross-reference the accuracy and standardization of this information in the model's generated responses.
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.