Data Characteristics
CSO (Contract Sales Organization) clinical trial pre-screening data originates from multiple channels. These include core clinical trial documents like sponsor-provided agreements, Investigator's Brochures (IB), protocols, and Informed Consent Forms (ICF). Internal resources like accumulated medical literature, regulatory documents, and past project experience summaries also contribute. Document update frequency depends on trial phase and regulatory requirements; protocol amendments, for example, may lead to multiple updates, while medical literature sees continuous new publications. Document structures vary, often being unstructured or semi-structured text such as detailed reports in PDF, agreement texts in Word, or patient inclusion/exclusion criteria lists in Excel. Common fields include disease diagnostic criteria, contraindications for concomitant medications, specific biomarker levels, and patient baseline characteristics. Units cover various medical and biological dimensions such as dosage (mg, µg), time (hours, days, weeks), and concentration (ng/mL, µmol/L).
Constraints on Deployment and Upgrade
The multi-source, unstructured nature of CSO clinical trial pre-screening data places high demands on FastGPT's document parsing capabilities. Large volumes of PDF and Word documents require stable and accurate text extraction, along with preservation of critical table structure information. Frequent document updates and revisions necessitate an efficient incremental update mechanism for the knowledge base, avoiding full rebuilds each time and handling version differences. The prevalence of medical terminology and specialized acronyms challenges the model's semantic understanding, requiring pre-trained models or fine-tuning to improve domain-specific comprehension. Furthermore, sensitive information within the data, such as patient privacy or confidential trial content, imposes strict constraints on the deployment environment's security, access control, and data anonymization capabilities to ensure compliance.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial documents, especially PDFs with charts, can be large. Sufficient upload space is needed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF files can be time-consuming. Increasing the timeout prevents parsing interruptions. |
Chunk size | 800 characters | Clinical protocols are detail-dense. Longer segments help maintain contextual integrity and reduce semantic fragmentation. |
Recall count | 10 entries | Pre-screening often requires synthesizing information from multiple sources. Increasing recall items improves relevant information coverage. |
Similarity threshold | Calibrated by measurement | This value needs experimental determination based on actual data and recall effectiveness to balance recall and precision. |
embeddingModel | text-embedding-ada-002 or privately deployed model | Prioritize models with strong semantic understanding to handle medical terminology and complex sentence structures. |
Common Pitfalls
- When uploading large PDF files, an "upload failed" or "parsing timeout" error may occur. This can be due to the
PARSE_FILE_TIMEOUT_SECONDSparameter being set too short, causing the file to exceed the processing time limit. - Query results may show misunderstandings or omissions of key medical terms. This indicates the
embeddingModelmay not adequately comprehend specialized biomedical vocabulary, suggesting insufficient model selection or fine-tuning. - After a knowledge base update, newly uploaded document content may not be immediately reflected in search results. This likely means the knowledge base index did not trigger an incremental update, preventing new data from being included in the search scope.
How to Verify Configuration
- Upload a PDF document containing complex tables and medical terminology. Check if the parsed text content is complete and if table structures are correctly identified.
- Construct queries targeting specific inclusion/exclusion criteria from clinical trial protocols. Verify that the recalled results include all relevant document snippets.
- Perform an incremental knowledge base update. Then, query for differences before and after the update to confirm new data has been successfully indexed and is retrievable.
The values provided are common starting points. Measure them 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.