Data Characteristics for This Category
Data for academic promotion registration and declaration document preparation primarily comes from clinical trial reports, drug monographs, pharmaceutical research reports, safety evaluation reports, and relevant regulatory documents. Update frequency varies based on drug lifecycle stages and regulatory requirements. For example, clinical trial data might update every few years, while drug monographs or regulatory changes could update annually or quarterly. Document structures are mainly structured and semi-structured, including PDF reports, Word documents, Excel spreadsheets, and some database records. Fields and units are highly specialized, such as dosage units (mg/kg), concentration units (μg/mL), time units (hours or days), and statistical P-values and confidence intervals. The data often contains numerous charts, tables, and specialized terminology, requiring advanced text extraction and semantic understanding.
Constraints Imposed by These Characteristics on "Reference and Traceability"
The specialized nature of academic promotion documents requires references to be precise, down to specific paragraphs or figures in the original text, to support arguments for efficacy, safety, or regulatory compliance. The high update frequency of regulatory documents and monographs necessitates a system that can quickly identify and index the latest versions, ensuring timely references. Document diversity (PDF, Word, Excel) and complex structures (charts, tables) demand robust document parsing capabilities to accurately extract text and maintain contextual relationships, preventing references from being taken out of context. Accurate identification of specialized fields and units is fundamental for traceability; any misinterpretation of units or values could lead to serious compliance issues. Additionally, since these documents are typically lengthy, effective context management mechanisms are needed to support cross-chapter or cross-document referencing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 300-500 characters | Balances semantic completeness with recall efficiency, avoiding excessive fragmentation or overly long contexts. |
Recall count | Top 8 entries | Covers more potentially relevant information, improving the comprehensiveness of responses. |
Similarity threshold | 0.75-0.85 | Ensures strong relevance of recalled content, reducing interference from irrelevant information. |
Rerank result count | Top 3 entries | Prioritizes the most relevant and re-ranked content for display. |
maxContext | 4000 characters | Balances model processing capabilities with the amount of contextual information, ensuring critical information is not lost. |
reference_template | [${index}] ${text} (Source: ${source_name}) | Standardizes reference format, clearly displaying cited content and source path. |
Three Common Mistakes
- Symptom: System responses only show the reference source, without specific content. Reason: The
Similarity threshold(similarity threshold) is set too high, leading to insufficient matching between recalled text segments and the user's query, preventing the model from generating an effective answer. - Symptom: Reference sources point to old regulations or outdated clinical data. Reason: The data source update mechanism is not synchronized in time, or the knowledge base does not correctly index the latest document versions, causing the model to retrieve outdated information.
- Symptom: Data units or values in the response's references are incorrect. Reason: The document parsing module inaccurately identifies specialized fields in complex tables or charts, or the
Chunk size(segment length) is improperly set, leading to data truncation.
How to Confirm Correct Configuration
- Select different types of registration and declaration documents (e.g., clinical reports, monographs), input typical queries, and check if reference sources accurately point to the corresponding paragraphs in the original text.
- Upload the latest version of a regulatory document, then ask relevant compliance questions, and verify that the reference source is from the latest version of the document.
- For queries containing specialized units and values, check if the referenced content in the response accurately retains the original units and values, and manually compare with the original text for confirmation.
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.