Data Characteristics in this Category
Site Management Organization (SMO) product data primarily originates from clinical trial protocols, investigator brochures, subject informed consent forms, ethics committee approvals, contract documents, project management documents, and various Standard Operating Procedures (SOPs). This data typically exists as PDFs, Word documents, and structured database records. Update frequency varies: project protocols and contracts are relatively stable, but investigator brochures, subject recruitment materials, and operational SOPs update periodically based on trial progress and regulatory requirements, usually monthly or quarterly. Document structure often includes clear chapter headings and paragraphs, interspersed with numerous tables, charts, and abbreviations. Key fields include trial ID, protocol version number, drug name, indication, inclusion/exclusion criteria, adverse event grading, visit procedures, and cost terms. Units involve dosage (mg, g), time (days, weeks, months), and numerical values (e.g., blood routine indicator ranges).
Constraints Imposed by these Characteristics on Vector Models and Indexing
The diversity and update frequency of SMO data impose specific requirements on vector model and indexing construction. Large volumes of unstructured documents demand efficient text extraction and preprocessing. Information within tables and charts requires special handling to prevent semantic loss. Monthly or quarterly update cycles necessitate incremental indexing capabilities to avoid frequent full rebuilds. Specialized terminology, abbreviations, and specific units within documents require vector models to accurately understand contextual meanings, preventing incorrect recalls. For example, the same drug might have different dosage units or administration methods at different trial stages; the vector index must differentiate these subtle semantic differences. Furthermore, legal and compliance requirements in contract clauses and ethics approvals demand high accuracy and completeness in recall; any omission or misunderstanding could lead to severe consequences.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Balances context retention for long texts and recall granularity for short texts, suiting SMO document chapter structures. |
Chunk Overlap | 50 characters | Ensures semantic continuity at chunk boundaries, preventing critical information from being cut off. |
Recall Count | 8–12 items | Given the complexity of SMO consultations and the need for information completeness, increasing recall count covers more potentially relevant segments. |
Similarity Threshold | 0.78–0.85 | Balances recall precision and completeness, reducing missed recalls due to insufficient similarity of specialized terms. |
Rerank Return Count | 5 items | Further refines the most relevant segments using a reranking model based on initial recall, improving final answer quality. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | SMO documents are often large and complex, requiring longer file parsing times to ensure complete content extraction. |
Three Common Pitfalls
- Missing key clauses or numerical values in query results: This occurs when chunk length is too short or the chunking strategy is inappropriate, leading to important information being truncated or semantics being dispersed.
- New SOP content not appearing in queries after an index update: This happens if the indexing system lacks an incremental update mechanism, or if the file monitoring service fails to trigger the re-indexing process correctly.
- Queries for drug names or trial stages returning many irrelevant results: This indicates that the vector model's embedding understanding of specialized terminology is insufficient, failing to effectively differentiate context or subtle differences in synonyms, leading to low-quality recall.
How to Confirm Correct Configuration
- Select several typical queries (e.g., "Inclusion/exclusion criteria for Drug A," "Adverse event reporting timeline in visit procedures"). Compare FastGPT's results with the original document content to confirm accurate recall of key information.
- Upload an investigator brochure containing the latest revisions. After the index update completes, immediately query for newly added key information to confirm that updated content is retrievable.
- For queries containing abbreviations or specialized vocabulary, check if recalled segments correctly link to their full definitions or related explanations. This assesses the vector model's understanding of industry-specific terminology.
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.