Data Characteristics
Quality documents for clinical trial pre-screening in the biomedical field include Standard Operating Procedures (SOPs), Investigator's Brochures (IBs), Protocols, Informed Consent Forms (ICFs), and various record forms and reports. These documents typically exist as PDFs, DOCX files, or scanned images, with varying degrees of structure. SOPs and Protocols often have strict chapter and numbering systems. Their content updates infrequently, primarily during version iterations. Record forms and reports contain significant semi-structured data, such as patient demographics, test results, and medication records. These documents involve numerous medical terms, abbreviations, and specific units of measurement, such as mg/kg and mmol/L. Data sources are primarily internal document management systems or Electronic Data Capture (EDC) systems within clinical trial institutions.
Constraints Imposed by Data Characteristics on Vector Models and Indexing
Quality document characteristics impose specific requirements on vector models and indexing. First, documents contain many specialized terms and abbreviations. Vector models require strong domain knowledge understanding to accurately capture semantics. Second, document structures are complex. Examples include the hierarchical structure of SOPs and the multiple conditions within Protocols. Chunking strategies must preserve contextual integrity to avoid truncating critical information. For scanned documents, Optical Character Recognition (OCR) accuracy directly affects subsequent indexing quality. Low update frequency means index reconstruction costs are relatively manageable, but each update requires accurate incremental indexing. Additionally, common tabular data in documents requires special handling to ensure associated information within tables is not fragmented. For instance, table rows or columns can be vectorized as independent semantic units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Balances document semantic integrity and retrieval efficiency. Avoids noise from overly long text or loss of context from overly short text. |
Chunk overlap (Chunk Overlap) | 100–200 characters | Ensures semantic continuity between adjacent chunks, especially when processing long sentences or critical information spanning paragraphs. |
Recall count (Recall Count) | Top 8–15 items | Clinical documents require rigor. Increasing recall count improves coverage of relevant information for subsequent re-ranking. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Domain documents are highly specialized. A higher similarity threshold ensures precision of recalled content and reduces interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient file parsing time for large PDF or DOCX files, preventing parsing failures due to timeouts. |
UPLOAD_FILE_MAX_SIZE | 200 MB | Clinical documents may contain many images or charts. This value appropriately relaxes file upload size limits. |
Three Common Mistakes
- Knowledge base indexing remains stalled, showing an "incomplete" status for an extended period. This often results from file parsing timeouts or encountering abnormal data formats during processing, leading to index task interruption.
- After uploading a CSV file, content chunking does not meet expectations or appears garbled. This may occur if the CSV file's encoding format is inconsistent with the system's default encoding, or if special characters in the file are not handled correctly.
- Knowledge base citation quality significantly degrades after switching vector models. This may be because the new model's understanding of specific domain terms is inferior to the old model, or the new model's vector dimensions are incompatible with the existing index, requiring index reconstruction.
Verification of Configuration
- Upload a typical document (e.g., a 50-page Protocol). Observe indexing progress and status. Ensure all chunks are successfully indexed without errors.
- Perform keyword searches for specific professional terms and abbreviations within the document. Check if recall results accurately include relevant paragraphs and evaluate the contextual completeness of the recalled content.
- Select critical tabular content from the document. Construct queries containing tabular data. Verify the system's ability to correctly understand and recall relevant information within the tables.
- In actual pre-screening scenarios, simulate different types of queries (e.g., conditional filtering, side effect queries). Compare the knowledge base snippets cited in AI responses with the original document content to assess citation accuracy.
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.