Data Characteristics for This Category
Home medical quality documents include product manuals, registration certificates, production process files, quality standards, risk management reports, clinical evaluation reports, user feedback records, and regulatory compliance statements. Data sources are diverse, encompassing structured and unstructured data generated by internal R&D, production, and quality departments, as well as external public information such as guidelines, standards, and announcements from regulatory bodies. Document update frequencies vary; product manuals and registration certificates may update with product iterations, while risk management reports and user feedback records might update in real-time or periodically. Document structures are diverse, with PDF, Word, and Excel being common formats. These often contain numerous charts and images. Text content is typically highly specialized, involving medical terminology, technical parameters, safety indicators, and operating procedures. Fields and units have high standardization requirements, such as measurement precision, calibration cycles, and biocompatibility indicators.
Constraints Imposed by These Characteristics on "Citing Sources and Traceability"
The fragmented sources and multi-format nature of home medical quality documents require knowledge base support for ingesting various file types and effective extraction of text embedded within charts. Varying document update frequencies necessitate incremental updates and version management capabilities in the knowledge base to ensure the timeliness and accuracy of cited content. The presence of specialized terminology and standardized fields demands higher requirements for vectorization models and retrieval algorithms to ensure precise semantic understanding. Regulatory compliance requirements in documents mean that source citations must pinpoint specific paragraphs or even clauses in the original text to meet compliance review needs. Without effective processing, a large amount of image and table content can lead to incomplete or misinterpreted citation information. The unstructured nature of user feedback records requires the system to extract key information and link it to product documentation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances the professional density and contextual completeness of home medical documents, preventing semantic breaks during segmentation. |
Recall count | 8–12 entries | Ensures coverage of sufficient potentially relevant information to address the complexity and diversity of document content. |
Similarity threshold | 0.75–0.85 | Balances recall precision and recall rate, reducing irrelevant information interference and improving professional content matching. |
Rerank result count | 3–5 entries | Focuses on the most relevant core citations, reduces model processing burden, and improves response efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing time for large quality documents (e.g., clinical evaluation reports), preventing timeout failures. |
MAX_FILE_SIZE_MB | 100 MB | Allows uploading high-definition PDF documents containing numerous charts and images, ensuring complete data ingestion. |
Three Common Mistakes
- Cited content displayed does not match the original text in the knowledge base. This occurs when text in images or tables is not correctly extracted during file parsing, leading to an incomplete knowledge base index.
- Answers cite general knowledge, but specialized knowledge base content is not reflected. This indicates improper knowledge base weight settings or insufficient recall of specialized terms during vector retrieval.
- Some documents in the knowledge base (e.g., Excel format quality standards) are not cited. This might be because the file type is not configured for support, or the parser does not correctly handle its internal structure.
How to Verify Correct Configuration
- Upload typical documents (e.g., manuals with charts, multi-page risk management reports) and check if all text content is completely stored in the knowledge base.
- Construct queries strongly related to the document content. Check if the returned citation list accurately points to the original paragraphs and verify that the
contentfield of the citation matches the original text. - Construct queries containing specialized terms and parameters. Verify if the system can accurately recall corresponding quality standards or technical specifications and evaluate if the
similarityscore is within the expected range. - Simulate a product defect report scenario by inputting unstructured user feedback. Confirm if the system can link to relevant product manuals or risk management documents and check if the
sourcefield of the citation is correct.
Note: 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.