Data Characteristics for this Category
IVD (In Vitro Diagnostic) diagnostic reagent R&D documents include project initiation reports, technical requirements, product specifications, registration inspection reports, clinical trial reports, risk management reports, production process regulations, quality standards, stability study reports, and change records. These documents typically exist in formats such as PDF, Word, and Excel, with some data embedded as images. Data update frequency is high during the R&D phase; project progress, experimental results, and version iterations trigger document revisions. Document structure is rigorous, often adhering to specifications from the National Medical Products Administration (NMPA) or international standards (e.g., ISO 13485), including clear chapter titles, figures, tables, and appendices. Fields frequently involve batch numbers, expiration dates, test methods, clinical performance indicators (e.g., sensitivity, specificity), reagent components, and calibrator information. Units strictly follow the International System of Units (SI) or industry conventions, such as concentration units mg/mL, IU/L, and time units min, h.
Constraints on "Reference and Traceability" from These Characteristics
The rigorous structure and high regulatory requirements of IVD diagnostic reagent R&D documents impose strict constraints on reference and traceability. First, documents contain extensive standardized terminology, experimental data, and regulatory clauses. This requires references to be precise down to the original text location, ensuring the authority and verifiability of answers. Second, documents update frequently, especially clinical trial and risk assessment sections. The system must identify differences between versions to avoid citing outdated information. Third, structured fields like batch numbers and expiration dates may be scattered across different documents. The referencing system must associate information across documents to provide a complete information chain. Finally, due to data in image format, OCR accuracy directly impacts the completeness of cited content. Any citation deviation can lead to R&D decision errors or non-compliance with regulations. Therefore, reference source accuracy and traceability are critical.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Ensures individual knowledge blocks contain sufficient context while avoiding excessive length that could lead to information redundancy or topic deviation. |
Recall Count | Top 5–8 | Given the rigor of IVD documents, increasing the recall count helps cover more potentially relevant and critical knowledge points. |
Similarity Threshold | 0.75–0.85 | Guarantees semantic relevance of recalled content, filtering out low-quality or inaccurate references, especially for specialized terminology. |
Rerank Return Count | 3 | After recall, reranking further filters for core references most directly related to the question, improving traceability efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Provides ample parsing time for large PDF documents or scanned files containing complex figures, preventing parsing failures due to timeouts. |
ENABLE_OCR | True | Ensures the system can process critical data embedded as images, such as experimental reports and spectrograms, increasing reference coverage. |
Three Common Mistakes
- Returned content does not match the reference source: This may occur if the
Similarity Thresholdis set too low, leading to the recall of many irrelevant knowledge blocks. The model then "freely invents" answers instead of strictly adhering to the original text. - Some documents fail to generate Q&A pairs or are directly inserted as original text: This could relate to an inappropriate
Chunk Lengthsetting. Overly long chunks can dilute key information, while overly short chunks may not provide enough context for the model to understand. - Reference sources lack critical field information: This typically happens if the
ENABLE_OCRparameter is not enabled or the OCR engine's recognition capability is insufficient. Key data like batch numbers and expiration dates in images are not extracted structurally, affecting traceability completeness.
How to Confirm Configuration
- Randomly select 10–20 complex queries. Check if the document page numbers and paragraphs cited in the returned answers fully correspond to the original document content.
- For documents containing figures, tables, or scanned images, verify that key fields identified by OCR (e.g., test results, batch numbers) appear accurately in the cited content.
- Simulate queries involving document version differences. Check if the system accurately cites information from the latest version and indicates potential differences in older versions.
- Evaluate whether knowledge blocks cited by the system effectively resolve ambiguity for queries involving vague or highly polysemous specialized terminology, pointing to the most authoritative source of explanation.
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.