Data Characteristics
Clinical trial pre-screening data handled by Contract Sales Organizations (CSOs) primarily originates from various clinical research documents provided by sponsors. These documents include, but are not limited to, study protocols, investigator brochures (IBs), informed consent form (ICF) templates, case report form (CRF) designs, and standard operating procedures (SOPs). Data sources are typically exports from internal sponsor systems or email transfers. Update frequency varies from weekly to monthly, depending on trial progress and protocol revisions. Document structures are complex, often containing large amounts of unstructured text, tables, figures, and nested section headings. Fields and units involve medical terminology, dosage units (e.g., mg, μg/kg), time units (e.g., days, weeks), and various biomarker indicators.
Constraints from "Document Parsing and Chunking"
The highly specialized and complex structure of CSO documents places high demands on document parsing. Study protocols and IBs contain dense medical terminology. The parser must accurately identify and maintain contextual semantics to avoid splitting critical information during chunking. ICFs and CRF templates include extensive structured or semi-structured information, such as patient inclusion/exclusion criteria and adverse event recording fields. The parser must effectively process table and list structures, correctly extracting and associating their content. Periodic document updates mean the knowledge base needs to support incremental updates and version management, ensuring each pre-screen uses the latest documents. Diverse measurement units and medical abbreviations also challenge tokenization and entity recognition accuracy, requiring specific configurations to improve recognition rates.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances contextual completeness and retrieval efficiency. Avoids overly long chunks diluting key information and overly short chunks losing semantic meaning. |
Overlap Length | 100–150 characters | Ensures semantic continuity at chunk boundaries, especially for medical terminology and long sentences. |
Enhanced Parsing | Enable PDF, Word, Excel enhanced parsing | Handles complex layouts in clinical documents, such as tables, figures, and multi-column text. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the time required to parse large study protocols and IB files, preventing parsing failures due to timeouts. |
File Size Limit | 200 MB | Adapts to large clinical documents (e.g., PDFs with figures) provided by sponsors. |
Enable Table Recognition | Enable | Clinical documents contain numerous tables, such as dose adjustment tables and adverse event reporting templates. Enabling this extracts table data accurately. |
Common Pitfalls
- PDF or Word document parsing results in missing content or garbled text. This occurs when enhanced parsing is not enabled or configured, preventing correct recognition of complex layouts and embedded objects.
- When uploading files via the knowledge base API, a
milvusStandaloneout-of-memory error appears. This indicates inadequate memory allocation for the FastGPT service or Milvus instance, leading to resource exhaustion when processing large files or high-concurrency parsing. - After uploading an Excel file, vector indexing is incorrect or query results are imprecise. This happens when the content structure of the Excel file is not pre-processed or key columns are not specified, preventing the parser from effectively identifying data relationships.
Verification Steps
- Upload a clinical study protocol PDF containing tables and complex medical terminology. Check the parsed chunks to confirm that table data and key terminology are complete and semantically coherent.
- Perform an incremental update on a collection of investigator brochure documents with multiple versions. Check that the knowledge base contains only the latest version and that older versions are handled correctly.
- Use the API to upload a large Word document. Observe the parsing logs to confirm no timeout errors or file parsing failures, and check that corresponding knowledge base chunks are generated.
- Conduct multiple retrieval tests for inclusion/exclusion criteria in a specific disease area. Check that recalled chunks contain all relevant patient characteristics, diagnostic criteria, and exclusion conditions. Adjust the similarity threshold based on query results.
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.