Citation and Traceability for Molecular Diagnostics

Data Characteristics in Molecular Diagnostics

Molecular diagnostics product registration documents involve various data types. These include clinical trial reports, performance validation reports, risk management reports, instructions for use, and technical requirements. Documents are often in PDF, Word, or scanned image formats. They have complex structures and may contain numerous charts, formulas, and biological sequence information.

Data update frequency is relatively low. Updates primarily occur during product development, clinical trial batch updates, or regulatory policy adjustments. Field names in these documents typically follow medical device industry standards, such as batch number, production date, expiration date, clinical concordance rate, and specificity. Units include ng/μL, copies/mL, and percentage. These fields require high precision and traceability. Some critical data may be embedded as attachments or images.

Constraints from Data Characteristics on Citation and Traceability

The data characteristics of molecular diagnostics registration documents impose specific constraints on citation and traceability.

First, complex document structures and multi-format storage require the knowledge base to effectively process non-text content. This includes extracting key information from images and indexing it.

Second, data updates are infrequent but large in volume. The knowledge base needs version management and incremental update capabilities. This ensures that citations always refer to the latest, approved versions.

Precise fields and units require the ability to identify and differentiate these specialized terms during retrieval and citation. This avoids ambiguous matching that leads to incorrect citations. For example, when searching for specificity, the system should distinguish specificity data from different batches or detection methods.

Strict traceability requirements mean that each citation must link back to the specific section, page number, or even chart location in the original document. This requires the knowledge base to retain fine-grained metadata during chunking and indexing.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Balances complex document structures with information completeness, preventing critical information from being cut off.
`Recall count` (Recall Count)	`10 entries` (items)	Improves retrieval recall rate, covering more relevant information. Addresses the polysemy of technical terms.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures high semantic relevance of recalled results to molecular diagnostics professional queries, reducing noise.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	Selects the most relevant content for AI model citation while maintaining accuracy.
`maxContext`	`3000 Tokens`	Accommodates detailed descriptions in molecular diagnostics registration documents, ensuring the AI model understands the complete context.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles parsing time for large PDFs or documents with complex charts, preventing timeout failures.

Common Pitfalls

The knowledge base retrieves relevant chunks, but the AI does not return a citation. This occurs because maxContext is set too low, preventing the model from receiving all retrieved results.
After uploading a large scanned clinical report, some key data are not indexed. Queries fail to recall this data. This occurs due to OCR recognition failure or document parsing timeout.
Knowledge base retrieval results are passed to an HTTP request in a workflow, but the AI conversation still cannot cite them. This occurs because the data format after HTTP request processing does not match the input format expected by the AI module.

How to Verify Configuration

Upload a molecular diagnostics performance validation report containing complex tables and charts. Verify that key fields (e.g., detection limit, intra-batch difference) are accurately indexed and recalled.
Conduct multi-round simulated Q&A. Ask questions about specific regulatory clauses, technical parameters, or risk assessment points in the declaration documents. Check if the AI's response includes correct and traceable citation links.
Check the knowledge base retrieval logs. Confirm that Recall count (Recall Count) and Similarity threshold (Similarity Threshold) are working as expected. Verify that the recalled results cover multiple dimensions of the query intent.

The values provided are common starting points and should be measured against specific 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.