Characteristics of IVD Diagnostic Reagent Data
Core data for IVD diagnostic reagents primarily originates from product manuals, registration certificate attachments, technical specifications, and batch inspection reports. These documents are typically in PDF format; some may be scanned images. Product manuals have a rigorous structure, including fixed sections such as product name, intended use, assay principle, main components, storage conditions, applicable instruments, sample requirements, assay method, result interpretation, limits, and precautions. Registration certificate attachments detail technical indicators and performance validation data. Batch inspection reports update frequently with each production batch, containing information like batch number, production date, expiry date, and test results. Documents often include chemical formulas, biological terms, specific units (e.g., IU/mL, ng/dL, OD value), and tabular data.
Constraints from These Characteristics on "Document Parsing and Chunking"
The fixed sections and rigorous structure of IVD diagnostic reagent documents require the document parser to accurately identify section boundaries and prevent content confusion. The presence of scanned images necessitates OCR capabilities and handling potential recognition errors. The large number of specialized terms, chemical formulas, and biological terms challenges tokenization and semantic understanding; these terms must remain intact to avoid incorrect segmentation. Tabular data requires special handling to preserve row and column relationships for accurate subsequent queries. The high update frequency of batch inspection reports demands efficient parsing and incremental update mechanisms to ensure knowledge base timeliness. The presence of specific units indicates that chunking should consider the association between units and numerical values, avoiding isolated numbers.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances semantic completeness and recall efficiency. Avoids excessive length causing information redundancy or insufficient length leading to context loss. |
Chunk Overlap | 100–200 characters | Ensures semantic continuity at chunk boundaries, improving accuracy for cross-references. |
Parsing Mode | Prioritize By Title, supplement with Fixed Length | IVD documents are highly structured; chunking by title effectively preserves section semantics. Fixed length serves as a supplement for untitled content. |
OCR Recognition | Enable | Scanned PDFs exist; enabling ensures all document content is parsable. |
Process Tables | Enable | IVD documents contain significant critical tabular data, such as performance indicators and result interpretation tables, whose structure must be preserved. |
Recall Count | Top 5–8 chunks | Ensures comprehensive information while reducing interference from irrelevant information and improving response speed. |
Three Common Mistakes
- Uploading a PDF file results in empty content, despite the file containing text. This typically occurs due to complex internal PDF encoding or the PDF being a scanned image, preventing the parser from correctly extracting text or causing OCR recognition failure.
- After importing documents with many specialized terms and chemical formulas, query results are inaccurate or missing. This may happen if the default tokenization strategy incorrectly segments specialized terms, disrupting their complete semantics.
- After uploading data via the
pushdataAPI, the status remains "indexing" for an extended period. This could be due to excessively large document volumes, network transmission interruptions, or a backlog in the backend parsing task queue, leading to processing timeouts.
How to Verify Correct Configuration
- Randomly select multiple IVD documents from different sources (e.g., product manuals, registration certificates) and formats (e.g., native PDF, scanned PDF). Upload them to the platform and check if the parsed text content is complete and free of garbled characters, paying special attention to table and specialized term recognition.
- Formulate simulated questions based on key information points in IVD documents (e.g., "intended use," "storage conditions," "limits"). Compare FastGPT's answers with the original document content to evaluate answer accuracy and relevance.
- Examine the chunking of different documents in the knowledge base. Ensure each chunk has semantic coherence and appropriate length, without semantic fragmentation or excessive redundancy. Determine reasonable thresholds for chunk length and overlap to meet subsequent retrieval and generation quality requirements.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.