Data Characteristics in this Category
Site Management Organization (SMO) R&D documents originate primarily from clinical trial sites, sponsors, and Contract Research Organizations (CROs). Data update frequency correlates closely with clinical trial progress. Batch updates typically occur at key milestones such as project initiation, protocol amendments, ethics approvals, subject recruitment and follow-up, and data lock. Daily updates include scattered safety reports, visit records, and quality control documents. Document structures are complex, encompassing various types like Standard Operating Procedures (SOPs), Informed Consent Forms (ICFs), Investigator Brochures (IBs), Clinical Trial Protocols, Case Report Forms (CRFs), ethics approvals, training records, and qualification certificates. Fields and units are highly specialized, for example, dosage units (mg/kg), time points (D+N days), and laboratory indicators (U/L, ng/mL). These often involve medical terminology, abbreviations, and coding systems (e.g., MedDRA, WHO Drug).
Constraints on "Reference and Traceability" from these Characteristics
The specialized nature and complex structure of SMO documents necessitate precise reference traceability down to the smallest semantic unit. A specific visit point description in a clinical trial protocol or an operational step in an SOP may be cited in subsequent questions. The phased nature of data updates requires the knowledge base to handle version control, ensuring that the cited document version aligns with the context of the user's query. For example, citing data from an old protocol version could lead to serious discrepancies. The strictness of fields and units means their original form must be preserved during structured parsing to avoid information distortion due to format conversion or truncation. For instance, a dose of "10 mg" cannot be parsed as "10" and lose its unit. Furthermore, frequent cross-referencing between documents, such as CRFs referencing Protocols, demands that the traceability system identifies and displays these associations, helping engineers understand the complete path of information sources.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size | 400–600 characters | Balances contextual completeness with retrieval efficiency, preventing individual segments from being too long (diluting core information) or too short (losing context). |
Recall count | Top 8–12 entries | SMO documents are highly specialized, requiring more relevant segments for comprehensive judgment and reducing the risk of missing critical information. |
Similarity threshold | 0.75–0.85 | Ensures high semantic relevance of retrieved content, excluding interference from medical terms that are similar but have different actual meanings. |
Rerank result count | 3–5 entries | Building on a high recall volume, a reranking model focuses on the most relevant and authoritative references, improving answer accuracy. |
maxContext | 32000 token | Accommodates complex medical contexts and multi-document cross-references, ensuring the model has sufficient information for reasoning and traceability. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | SMO documents are often large and complex, requiring longer parsing times for deep structured extraction. |
Three Common Mistakes
- The knowledge base response shows "No permission to operate this conversation record" or clickable reference links are broken. This happens when the RAG service's reference source address is incorrectly configured or permission validation fails.
- Reference sources in the answer are incomplete or lack critical information. This occurs when the document segmentation strategy is unreasonable, causing individual segments to fail in expressing complete semantics independently, or when document metadata is not extracted during parsing.
- The reply outputs
\nand other newline characters literally, failing to render as newlines. This happens when the AI model's output format is not converted to the target system's rich text or Markdown rendering rules.
How to Verify Correct Configuration
- For typical questions, check if the cited document sources in the answer accurately point to specific paragraphs in the original document and verify the consistency of the cited content with the original text.
- Test whether the system correctly references the latest information after different document versions are updated and if it can trace back to specific version numbers or revision dates.
- Randomly select questions containing medical terminology, dosage units, and abbreviations, and verify that these specialized details are fully preserved and accurate in the references.
Note: 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.