Knowledge Base Retrieval and Recall for IVD Diagnostic

Data Characteristics

IVD diagnostic reagent regulations and SOP documents originate from NMPA regulations, guidelines, technical review requirements, and internal quality management system documents, production process specifications, and inspection SOPs. Data update frequency is stable. National regulations typically update annually; internal documents update based on production and quality management needs. Document structures are hierarchical, including introductions, scopes, definitions, responsibilities, operating procedures, record requirements, and appendices. Fields often include product names, registration numbers, batch numbers, expiry dates, storage conditions, testing methods, and quality control indicators. Units cover precise values like concentration (mol/L, mg/dL), volume (μL, mL), time (min, h), and temperature (℃).

Constraints on Knowledge Base Retrieval and Recall

The hierarchical structure and specialized terminology of IVD diagnostic reagent documents require the knowledge base to maintain logical integrity during chunking. Avoid splitting critical information. Regulations and SOPs contain precise numerical values and units. Retrieval must accurately match or identify approximate values. For example, a query for 2-8℃ storage conditions should recall text containing 2℃To8℃ or 2℃~8℃. Stable update frequency means the knowledge base needs regular incremental updates, but not overly frequent ones. Unique identifiers like registration numbers and batch numbers demand precise matching and filtering capabilities. Complex specialized terms and abbreviations, such as ELISA and PCR, require the semantic model to possess strong domain understanding to effectively identify user intent and recall relevant content.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters`	Ensures individual chunks contain sufficient context while avoiding excessive length that could lead to semantic drift.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–150 characters`	Enhances continuity between chunks, reducing information loss due to splitting.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	IVD requires high retrieval precision; a lower threshold introduces too much irrelevant information.
`Recall count` (Recall Count)	`10–15 items`	Provides enough candidate documents for subsequent re-ranking and generation, covering potentially relevant content.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Allows sufficient time for parsing large regulatory or SOP documents.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Accommodates uploading single regulatory files or SOP documents with attachments (e.g., images, charts).

Common Pitfalls

A query for "how to increase knowledge base capacity" returns "Connection error." This typically indicates incorrect database connection configuration or resource exhaustion, preventing the knowledge base service from communicating with underlying storage.
A user asks about storage conditions for a specific batch number, but the recall results lack critical temperature range information. This happens when knowledge base chunking separates crucial numerical and unit information from descriptive text, leading to semantically incomplete recalled chunks.
After a regulation update, a user queries new terms, but the system still returns old version information. This indicates the knowledge base's incremental update mechanism is not triggered or configured incorrectly, failing to synchronize the latest document version.

Verification Steps

Upload the latest versions of IVD regulatory files and enterprise SOP documents. Check document parsing status for errors or failures.
Perform test queries using core IVD domain terms, registration numbers, and precise numerical values. Verify that recalled results include relevant key information.
Simulate user questions about specific product batch numbers, testing methods, or quality control indicators. Evaluate the accuracy and completeness of recalled content. Check if the similarity metric falls within the expected range.
After a knowledge base update, query updated terms. Confirm the system correctly recalls the new version information and the old version is no longer a primary result.

These values 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.