Model Access and Configuration for SMO Registration and

Data Characteristics for This Category

SMO (Site Management Organization) registration and declaration documents come from diverse sources. These include clinical trial protocols, investigator brochures, informed consent forms, ethics approval documents, case report forms (CRFs), and safety reports. Documents are typically in PDF, Word, or scanned image formats, with varying degrees of structure. Update frequency is dynamic; protocol amendments, ethics approval updates, and safety reports change frequently, especially during ongoing clinical trials. Document content involves extensive medical terminology and regulatory clauses. Fields are diverse and complex, covering drug generic names, indications, dosage and administration, adverse event codes (e.g., MedDRA), laboratory test indicators (with units, such as mmol/L, mg/dL), and common regulatory document elements like section numbers, version numbers, and revision dates.

Constraints from These Characteristics on Model Access and Configuration

Diverse data sources require model access to support various file formats, especially OCR capabilities for scanned documents. Dynamic updates mean the knowledge base needs incremental updates and version management to ensure the model always responds based on the latest information. The coexistence of unstructured and semi-structured data challenges text segmentation strategies, requiring a balance between semantic completeness and information density. The specialized nature of medical and regulatory terms demands that the model accurately captures contextual semantics during vectorization and effectively matches information during retrieval. Numerical fields with units, such as laboratory indicators, require particular attention to accurate identification and extraction to avoid misinterpretation due to unit confusion. Furthermore, the hierarchical structure and cross-referencing in regulatory documents impose higher demands on RAG (Retrieval Augmented Generation) retrieval strategies and answer generation logic, requiring the model to understand and integrate relationships between different sections.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	SMO documents, especially PDFs or scanned images, often contain many images, leading to large file sizes.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances semantic completeness of medical texts with retrieval efficiency, avoiding excessive truncation of critical information.
`Recall count` (Number of Retrieved Chunks)	`Top 5–7 entries` (top 5–7)	The complexity of registration and declaration documents requires more contextual information for accurate answers.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Balances the breadth and precision of retrieval, reducing interference from irrelevant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	OCR and content parsing for large PDFs and scanned documents require significant time.
`Rerank result count` (Number of Reranked Chunks)	`Top 3 entries` (top 3)	Ensures the model's final generated answer is based on the most relevant and high-quality information.

Three Common Mistakes

An error occurs when the model's inference content is wrapped in a think tag and placed in the content field. This happens because the tool call parser fails to correctly identify or process this non-standard output format.
Long error codes appear when connecting to a model via an API key. This usually results from improper proxy server configuration, unstable network connection, or insufficient API key permissions.
The model reports a "message":"chat:llm-model-response-empty" error. This can occur during flowchart invocation, indicating the model failed to generate a valid response. Possible causes include excessively long input, internal model errors, or context window overflow.

How to Confirm Proper Configuration

Upload and parse a typical large PDF clinical trial protocol. Check if the file content is fully imported and if the segmentation logic meets expectations.
For queries involving specialized content like MedDRA codes or laboratory indicators, verify that the model retrieves accurate relevant paragraphs and generates reasonable answers.
Simulate actual questions from SMO personnel, such as regulatory requirements for specific drug adverse events. Check the model's response timeliness and accuracy. Compare with expert opinions and adjust the similarity threshold based on feedback.

Note: 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.