Data Characteristics for This Category
Core data for molecular diagnostics products primarily comes from product manuals, registration certificates, technical guidelines, clinical evaluation reports, internal R&D documents, and regulatory updates. Document update frequency is relatively stable, typically aligning with product iterations, registration approvals, or regulatory changes, usually quarterly or semi-annually. Document structures are rigorous, often in PDF or DOCX format, containing numerous tables, figures, and specialized terminology. Key fields include product name, model, detection target, detection method, clinical significance, performance indicators (e.g., sensitivity, specificity), scope of application, storage conditions, and operating procedures. Units are strictly used, such as concentration (ng/µL, pmol/L), temperature (℃), and time (min, h), often accompanied by ranges, confidence intervals, and other statistical descriptions.
Constraints from These Characteristics on Document Parsing and Chunking
The strict structure and specialized nature of molecular diagnostics product documents require accurate extraction of table and figure content during parsing, not just relying on plain text recognition. The update frequency necessitates knowledge base support for incremental updates and version management to prevent outdated information. The large volume of specialized terminology, units, and performance indicators with ranges requires effective context preservation during chunking to avoid losing critical information or ambiguity due to segmentation. For example, a description like "reagent kit storage temperature is 2–8 ℃" loses significant meaning if "2–8 ℃" is chunked separately. Additionally, R&D and quality control documents on internal Confluence platforms require special handling for access permissions and parsing methods to ensure data security and content integrity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Preserves context integrity for key information like performance indicators and operating procedures. |
Chunk Overlap Length | 50–100 characters | Ensures semantic coherence at chunk boundaries, especially for long sentences and table descriptions. |
File Type Whitelist | pdf, docx, xlsx, md | Covers common document formats for molecular diagnostics products, ensuring parsing scope. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing time for large clinical reports or PDF files with complex diagrams. |
CHUNK_STRATEGY | By Title and Content chunks | Prioritizes segmentation based on document structure while maintaining content integrity. |
ENABLE_OCR | true | Ensures text recognition in scanned or image-based manuals and registration certificates. |
Common Pitfalls
- Table data missing or corrupted in parsing results. This occurs when OCR recognition is not enabled or incorrectly configured, or when the parser lacks support for complex table structures.
- Retrieved text segments are semantically incomplete, preventing effective answers to questions about product performance or operating procedures. This occurs when
Chunk sizeis too short, leading to truncation of critical information. - Internal network documents fail to parse, showing connection timeouts or permission errors. This occurs when the FastGPT deployment environment cannot access internal network resources, or when network proxy and authentication information are not configured.
How to Confirm Correct Configuration
- Select typical product manuals and clinical reports, upload them, and check the completeness and accuracy of chunked content in the knowledge base, especially tables and numerical values with units.
- Test multiple queries to verify that the parsed documents can accurately answer questions about product performance indicators, usage methods, and precautions. Also, check the contextual relevance of retrieved segments.
- For internal Confluence pages, attempt to crawl and parse them via FastGPT to confirm network connectivity, permission settings, and content parsing.
The values provided 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.