Deployment and Upgrade for Phase II-III Clinical

Data Characteristics

Phase II-III clinical trial documentation, including regulations and Standard Operating Procedures (SOPs), originates from regulatory agencies, industry association guidelines, and sponsor-specific internal policies. These documents have a low update frequency, typically receiving annual or quarterly revisions due to policy changes or significant industry standard releases. Formats are primarily PDF, Word, and some Excel spreadsheets. Content is rigorously structured and hierarchical, containing specialized terminology, flowcharts, and approval checkpoints. Common fields include trial protocol numbers, version numbers, effective dates, revision histories, responsible persons, operational steps, and record-keeping requirements. Units primarily involve time (e.g., days, weeks), quantity (e.g., cases, copies), and percentages.

Constraints on Deployment and Upgrade

The stability of Phase II-III clinical documentation means initial deployment requires a high-quality, one-time parsing and vectorization of documents. Subsequent incremental updates will have less pressure. The rigorous structure and specialized terminology of the documents demand high-precision semantic understanding from the vector model to ensure accurate question answering. The presence of complex formats like PDF and Word requires robust file parsing, especially for extracting text from tables and images. Multi-level structures and flowcharts necessitate that FastGPT effectively identifies logical connections during knowledge base construction to avoid information silos. Standardized fields and units aid in precise matching and result validation during question answering, but also require standardized processing during the data pre-processing stage.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Phase II-III clinical documents are content-dense. Longer segments help maintain contextual completeness and reduce semantic fragmentation.
`Recall count` (Recall Count)	`Top 10`	Ensures coverage of multiple relevant clauses or steps within regulations or SOPs, improving the comprehensiveness of answers.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Regulatory question answering demands high accuracy. A threshold that is too low may introduce irrelevant content; one that is too high may lead to insufficient recall.
`Rerank result count` (Rerank Return Count)	`Top 5`	Building on a higher recall count, reranking further filters for the most relevant content, improving the quality of the final answer.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Phase II-III clinical documents, especially PDFs with many images and flowcharts, can be large, requiring sufficient upload capacity.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF or Word documents can be time-consuming. Increasing the timeout prevents failures due to incomplete parsing.

Common Mistakes

After knowledge base construction, question-answering results contain a large amount of irrelevant information. This occurs when the Similarity threshold (Similarity Threshold) is not finely tuned, leading to the recall of many low-relevance document snippets.
Uploading large PDF files results in an upload failure or parsing timeout, with 504 Gateway Time-out errors appearing in backend logs. This typically indicates that the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low.
When querying about a specific operational step, the answer fails to present the entire process. This may be because the Chunk size (Segment Length) is set too short, causing critical information to be split across different segments.

Validation Steps

Upload a Phase II-III clinical SOP document containing complex tables and flowcharts. Verify that its content is fully and correctly parsed. Use the knowledge base preview function to check segmentation effectiveness.
Query about a specific clause or process detail from a regulation. Check if FastGPT's answer accurately cites the original text and correctly identifies relevant version numbers and effective dates.
Simulate multiple users querying the knowledge base simultaneously. Monitor system response times and check mysql database connection status to ensure system stability under high concurrency.
When a new version of a regulation is released, perform a knowledge base update. Ensure the incremental document is correctly indexed and seamlessly integrates with existing content.

Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.

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.