Data Characteristics
Quality documents for high-value consumables include product registration certificates, production licenses, inspection reports, clinical evaluation reports, instructions for use, labels, and UDI (Unique Device Identification) data. These documents originate from regulatory bodies, manufacturers, and third-party testing agencies.
Update frequencies vary:
- Registration certificates and production licenses have fixed validity periods, typically 5 years, requiring re-submission and review upon expiration.
- Inspection reports and clinical evaluation reports may update irregularly due to product upgrades or changing regulatory requirements.
- Instructions for use and labels update with product batches or versions.
Document structure is primarily PDF. Content includes both structured data (e.g., registration numbers, batch numbers, expiration dates, model specifications, key performance indicators) and unstructured descriptions (e.g., clinical use risks, precautions). Fields and units are highly standardized. For example, registration numbers follow unified NMPA (National Medical Products Administration) encoding rules, and physicochemical indicators in inspection reports explicitly use SI units.
Constraints from "Reference and Traceability"
Regulatory strictness for high-value consumable quality documents mandates accurate source referencing and clear traceability paths. Regulatory data sources (e.g., NMPA databases) should be prioritized for authoritative content.
The periodic updates of documents, especially the validity management of registration certificates and production licenses, require the knowledge base to have version control capabilities and synchronize the latest valid documents promptly. Risk warnings and precautions in unstructured descriptions require precise extraction of original text to avoid semantic deviations during referencing.
Standardized fields and units allow direct matching and extraction of specific field information during referencing. This includes identifying registration number or production batch number via regular expressions or specific tags, ensuring traceability to the exact location within the document. References to this data must clearly trace back to the original file's page number, paragraph, or even specific table row.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 tokens | Balances long document processing with response speed, preventing critical information loss due to context truncation. |
Chunk size (Segment Length) | 500 characters (characters) | High-value consumable documents have strong paragraph logic; this length preserves complete paragraph semantics. |
Recall count (Recall Count) | Top 8 entries (top 8) | Increases recall scope to cover more potentially relevant regulatory clauses or technical indicators. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures recalled results are highly relevant to the query, filtering out low-quality matches. |
Rerank result count (Rerank Return Count) | 3 entries (3 items) | Selects the three most relevant items as final reference sources from the high-quality recall set. |
Citation Format | File Name[page number] (File Name[Page Number]) | Clearly indicates the original document and its specific location within the document, meeting regulatory traceability requirements. |
Common Pitfalls
- Knowledge base search results include expired registration certificate information. This occurs when the knowledge base lacks document validity settings or fails to update expired documents promptly.
- AI responses cite technical parameters inconsistent with original inspection reports. This happens due to improper document segmentation strategies, leading to incorrect splitting of key values and units or missing context.
- Reference sources cannot link to specific original PDF files or point to incorrect files. This results from incorrect file path associations during document upload or corrupted file indexes.
Verification Steps
- Select 5 random high-value consumable quality documents. Query key information (e.g., registration number, batch number, expiration date, main performance indicators). Verify that the AI response's cited sources accurately point to the corresponding original document text.
- Simulate queries about product risk warnings or precautions. Check if the AI response accurately extracts the original description and if the citation format includes the file name and page number.
- Query an expired registration number. Confirm if the system correctly identifies and prompts that the information is expired, or only returns documents with valid information.
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.