Data Characteristics
Quality document management in clinical trial pre-screening primarily uses data from pharmaceutical companies' Quality Management Systems (QMS), Contract Research Organizations (CROs)' SOP documents, and regulatory guidelines. Update frequencies vary; SOPs and guidelines typically update quarterly or semi-annually, while deviation reports and CAPAs (Corrective and Preventive Actions) may generate in real-time. Most documents are PDF text, containing numerous tables, charts, and nested structures. Fields and units are highly specialized, including "batch number," "production date," "expiration date," "test method ID," "deviation level," and "risk score." Units include milligrams (mg), milliliters (ml), percentages (%), and International Units (IU), often with strict formatting requirements, such as batch numbers following a "YYMMDD-XXX" pattern.
Constraints on Deployment and Upgrades
Diverse sources and high update frequency of quality documents require deployment solutions to support multi-source data ingestion and incremental updates, avoiding full re-indexing. Complex tables and nested structures in documents challenge text parsing and vectorization, necessitating more refined preprocessing steps to ensure accurate information extraction and semantic correlation. Specialized fields, units, and strict formatting mean models need strong domain knowledge for training and inference. Deployment may require custom entity recognition or regular expression matching rules. Since these documents often contain sensitive data, the deployment environment must meet strict data security and compliance requirements, including data encryption, access control, and audit logs. Version upgrades require careful attention to model compatibility and migration strategies to maintain existing knowledge base effectiveness and manage mappings between old and new data models.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Quality documents often contain many images and tables, resulting in larger file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF document parsing can be time-consuming; this prevents parsing failures due to timeouts. |
Chunk size | 800–1200 characters | Ensures individual document segments contain sufficient context while avoiding excessive length that could impact retrieval efficiency. |
Recall count | Top 10 entries | Clinical trial pre-screening decisions are complex, requiring more relevant document snippets for judgment. |
Similarity threshold | 0.75 | Improves the accuracy of retrieval results and reduces interference from irrelevant information. |
Rerank result count | Top 5 entries | Ensures that the most relevant and core document snippets are presented to the user. |
Common Mistakes
- Symptom: After a system upgrade, the relevance of some historical query results significantly decreases. Reason: Incompatibility between the old model version and the new vectorization algorithm, leading to a mismatch between historical vector data and the new query vector space.
- Symptom: After uploading large PDF documents, file parsing status remains stuck for a long time or reports "parsing timeout." Reason: The
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, not adequately accounting for the parsing time of complex quality documents. - Symptom: Queries about specific batch numbers do not include all relevant files, or include irrelevant content. Reason: Document segmentation did not sufficiently consider the integrity of key fields like batch numbers, or preprocessing failed to effectively identify and preserve the context of these specialized terms.
Verification Steps
- Upload typical quality documents containing charts and tables. Check if the parsed text content is complete and structurally correct, especially the extraction of tables and specialized terminology.
- Query key questions from specific clinical trial protocols using different phrasings. Verify that the retrieved document snippets are accurate, comprehensively cover relevant information, and that the number of retrieved items matches expectations.
- Simulate a version upgrade. Index and query the same batch of documents before and after the upgrade. Ensure consistency and accuracy of query results post-upgrade, verifying compatibility with the old data model.
Note: The values provided are common starting points. Measure them against your own samples for optimal configuration.
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.