Data Characteristics for this Category
IVD diagnostic reagent quality document data originates primarily from a manufacturer's internal quality management system. This includes product technical requirements, registration inspection reports, clinical trial reports, production process specifications, quality standards, batch production records, batch inspection records, non-conforming product handling records, deviation handling records, and change control records. Document update frequency is typically low, occurring mainly during product registration, registration changes, production process adjustments, regulatory updates, or annual quality reviews. This usually happens quarterly or annually. Documents are primarily in PDF, Word, and Excel formats. Some data may reside in LIMS (Laboratory Information Management System) or ERP systems. Document structure is rigorous, often containing numerous tables, figures, and standardized fields such as batch number, production date, expiration date, test item, test method, technical indicators (sensitivity, specificity, accuracy), and units (IU/mL, ng/mL, U/L, mol/L, etc.).
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The low update frequency and strict document structure of IVD diagnostic reagent quality documents require special attention to complete historical document import and version management capabilities during deployment. The data involves extensive specialized terminology and units of measurement. This demands high accuracy in text segmentation and entity recognition to prevent loss or misinterpretation of critical information. The prevalence of tables and figures in documents means traditional text parsing methods may be insufficient for effective information extraction, requiring more intelligent document parsing capabilities. The deployment environment must meet regulatory requirements for data security and traceability, typically favoring private deployments. During upgrades, ensure new versions are compatible with existing knowledge bases and that the transition is smooth without disrupting current quality management processes. Pay particular attention to how iterations in parsing models and recall strategies affect result consistency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | IVD report files are large; this ensures complete document upload in one go. |
maxContext | 3000 Tokens | Addresses complex technical requirements and clinical reports, ensuring sufficient context length. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing is time-consuming; this prevents parsing timeouts. |
Chunk size | 800–1200 characters | Preserves contextual coherence while preventing individual segments from becoming too long and diluting core information. |
Recall count | Top 8 entries | Ensures coverage of multiple relevant technical details or standard clauses in specialized queries. |
Similarity threshold | 0.75 | Guarantees high relevance of recall results to IVD quality document queries. |
Three Common Pitfalls
- Some table data is not correctly parsed or content is missing after document upload. This occurs due to insufficient support for complex table structures by the document parser or low accuracy in Optical Character Recognition (OCR) for scanned documents.
- After private deployment, new user accounts cannot be created or existing accounts fail to log in. This often results from incorrect database or authentication service configurations in the
docker-compose.ymlfile, such as port mapping conflicts or theDEFAULT_PASSWORDenvironment variable not taking effect. - Key technical indicator values or units are confused or incorrect in query results. This happens when segmentation strategies fail to effectively isolate numerical information with units, or the model misunderstands specialized terminology.
How to Verify Correct Configuration
- Upload an IVD product technical requirements document containing complex tables and figures. Check the parsing results of this document in the knowledge base to confirm that all key fields and table contents are correctly extracted.
- Use the
docker logs <container_name>command to view the logs of relevant FastGPT containers. Confirm there are no significant error messages, especially that database connection and user authentication services are in a healthy state. - Ask specific questions about IVD diagnostic reagents (e.g., "What is the sensitivity standard for a certain batch of reagents?", "What is the unit of measurement for a specific test item?"). Check if the returned technical indicator values and units are accurate in the answer, and compare them with the original document to verify the precision of the recall results.
Note: The values provided are common starting points. Measure them 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.