Data Characteristics in this Category
Contract Research Organizations (CROs) generate extensive specialized documents during the biopharmaceutical R&D process. Data sources include clinical trial protocols, investigator brochures, case report forms (CRFs), statistical analysis plans (SAPs), and clinical study reports (CSRs). These documents have varying update frequencies, from monthly revisions for trial protocols to daily entries for CRF data. Document structures are highly standardized, adhering to international guidelines like ICH GCP. Fields and units have strong biostatistical and medical backgrounds, such as dosage units (mg/kg), time points (hours, days), biomarker concentrations (ng/mL), and include numerous specific identifiers like subject numbers and visit dates.
Constraints on "Knowledge Base Retrieval and Recall" from these Characteristics
The standardized structure of CRO documents requires the knowledge base to identify and preserve key logical sections during chunking, such as protocol chapters, adverse event (AE) records, and concomitant medication lists. High-frequency data updates, particularly clinical data, challenge the knowledge base's real-time and incremental update capabilities. The specialized nature of fields and units means that during vectorization and retrieval, the model must accurately understand medical terminology and context. This avoids recall bias caused by synonyms or abbreviations. Critical information dispersed across long documents, such as safety data for a specific drug at different trial stages, requires the retrieval mechanism to link multiple sections for comprehensive context. Inter-document citation relationships, such as a CSR referencing an SAP, also require efficient system handling.
Configuration Settings
| Configuration Item | Recommended Value | Basis for Recommendation |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness with recall efficiency; avoids diluting key information in long chunks |
Chunk Overlap Length (Chunk Overlap Length) | 100–200 characters | Maintains contextual coherence; handles semantic links across chunks |
Recall count (Recall Count) | Top 5–8 items | Balances recall breadth with computational cost of subsequent re-ranking |
Similarity threshold (Similarity Threshold) | Determined by measurement | Experimentally set based on specific embedding model and dataset characteristics |
Rerank result count (Re-ranked Return Count) | 3–5 items | Focuses on the most relevant results; improves final answer accuracy |
Embedding Model | bge-large-zh | Possesses good understanding capabilities in the Chinese biomedical domain |
Three Common Pitfalls
- Knowledge base query response time is too long, resulting in timeout errors or freezes. This occurs when chunk length is too long or recall count is too high, leading to extensive retrieval and re-ranking computations.
- Key medical terms or abbreviations are missing from retrieval results. This happens when the knowledge base fails to fully understand the contextual semantics of specialized terms during chunking or embedding, or when the embedding model lacks generalization ability in the biomedical field.
- After uploading a PDF file, the system displays "File parsing failed" or the content is empty. This is due to low-quality PDF scans, complex tables, or images, preventing text extraction tools from correctly identifying text content.
How to Verify Configuration
- Select representative CRO R&D documents. Execute multiple complex queries. Observe if recall results include all expected associated information.
- Compare retrieval results under different chunk length and overlap length configurations. Evaluate semantic completeness and redundancy.
- Use queries containing specialized terms and abbreviations. Check for accurate matching and contextual relevance of these terms in the recalled content.
- Through system logs or monitoring, check the average response time of knowledge base queries. Ensure it remains within an acceptable range.
The values provided are common starting points. They should be measured against your 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.