Deployment and Upgrade for GMP-Compliant Clinical Trial

Data Characteristics

Data for GMP-compliant clinical trial pre-screening primarily originates from regulatory documents, guidelines published by drug administration authorities, Investigational New Drug (IND) applications and approval records submitted by pharmaceutical companies, clinical trial protocols, Investigator's Brochures (IB), Informed Consent Forms (ICF), and trial reports. This data largely consists of unstructured documents in formats such as PDF, DOCX, and XML. Some approval results or trial progress may exist as structured database records. Data update frequencies vary; regulatory documents typically undergo annual revisions or new releases, while clinical trial documents update in real-time according to trial phases. Document structures are complex, containing extensive specialized terminology, abbreviations, tables, and figures. Examples include drug batch numbers, manufacturing dates, expiration dates, and various physical and chemical indicators and microbial limits in quality inspection reports (e.g., cfu/g, ng/mL).

Constraints on Deployment and Upgrade from Data Characteristics

The data characteristics of GMP-compliant clinical trial pre-screening impose specific requirements on FastGPT's deployment and upgrade. The large volume and diverse formats of unstructured documents necessitate robust document parsing capabilities, especially for PDF files containing embedded tables and figures. High-frequency updates of clinical trial data require the system to have efficient incremental indexing and real-time update mechanisms to ensure the accuracy of pre-screening results. Dense specialized terminology and abbreviations demand more refined text segmentation strategies and domain-specific dictionary support to prevent semantic loss due to improper segmentation. Key information like batch numbers and manufacturing dates exists across different documents, requiring entity recognition technology for correlation. Additionally, units of measurement within the data (e.g., mg/kg, % w/w) require precise matching during recall and comparison, which demands that the vector model understands numbers and units.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial documents, especially protocols and reports with numerous attachments, can have large individual file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF documents is time-consuming and requires sufficient timeout duration.
`Chunk size`	`800–1200 characters`	Retains sufficient contextual information while preventing excessively long segments from impacting recall quality.
`Recall count`	`Top 8 entries`	Increases relevant information coverage to handle complex query scenarios.
`Similarity threshold`	Calibrate by measurement	Adjust based on actual document similarity distribution and pre-screening accuracy requirements; an initial value of `0.75` can be set.
`Rerank result count`	`Top 5 entries`	Selects the most relevant results, reducing manual screening effort.

Three Common Pitfalls

Knowledge base query returns empty results because document parsing failed, leading to critical information not being correctly indexed, or segment length being too short, resulting in incomplete semantics.
Model answers do not meet expectations; for instance, queries for specific batch numbers or manufacturing units fail to recall relevant content because of inaccurate recognition of numbers and units in documents, or ineffective entity correlation.
The system encounters an HTTP 504 Gateway Timeout error when processing large batches of file uploads because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, failing to accommodate the parsing time for large documents.

How to Confirm Correct Configuration

Upload typical clinical trial protocols, IBs, and regulatory documents. Check if the knowledge base can retrieve batch numbers, manufacturing dates, key trial indicators (e.g., AUC, Cmax), and their corresponding units from the documents.
Conduct multiple rounds of questioning for specific drug names and trial phases. Evaluate whether the model's answers accurately cite key information from source documents, especially expressions involving dosage units mg/kg or concentration units µg/mL.
Simulate simultaneous upload of 100 PDF documents, each averaging 50 MB. Observe if the system completes parsing and indexing within a reasonable time and check logs for parsing timeouts or memory overflow errors.

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.