Data Characteristics
Documentation for respiratory system disease products and reagents comes from many sources. These include drug inserts, clinical trial reports, academic papers, product technical manuals, diagnostic kit instructions, and medical device registration certificates. Data updates frequently. New drug approvals, revised clinical guidelines, and reagent iterations generate significant new or updated documents. Document structures are often complex. They contain specialized terminology, dosage units (e.g., mg/kg, ml/min), diagnostic criteria, side effect lists, charts (e.g., pharmacokinetic curves, pulmonary function graphs), and references. Fields cover drug ingredients, indications, contraindications, dosage and administration, adverse reactions, pharmacological effects, shelf life, batch numbers, and storage conditions.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complexity of respiratory product documentation creates specific requirements for document parsing and chunking. First, the high density of specialized terminology requires parsers with high recall and accuracy. This prevents tokenization errors that could lead to semantic loss. Second, critical information like dosages and diagnostic criteria often appear in tables or specific formats. Parsers must effectively identify and extract this structured data. Chart content (e.g., pulmonary function indicators, imaging features) frequently contains important diagnostic evidence. Pure text parsing cannot capture its semantics. This requires image content recognition and description capabilities. Frequent document updates mean the knowledge base needs efficient incremental updates and version management to ensure information timeliness. Additionally, the coexistence of multilingual documents (e.g., original English and Chinese translations) increases processing difficulty.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness and recall efficiency. Avoids overly long or short chunks that lead to information redundancy or loss. |
Overlap Length | 100–200 characters | Ensures contextual continuity at chunk boundaries. Improves accuracy for cross-chunk retrieval. |
maxContext | 4096 tokens | Accommodates long documents. Ensures sufficient context to understand complex pathological descriptions and diagnostic processes. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Handles parsing time for large clinical trial reports or detailed product manuals. Prevents parsing interruptions. |
Recall count (Recall Count) | Top 8 | Increases coverage of relevant information, especially for complex queries involving multiple knowledge points. |
Image Recognition Module | Enabled | Extracts key information from images such as pharmacokinetic curves and pulmonary function graphs. |
Three Common Mistakes
- Missing critical dosage or diagnostic criteria in parsing results: The parser failed to correctly identify table structures or specific numerical fields.
- Question-answering results cannot reference image content in documents: The image recognition module was not enabled or configured, preventing image information from being extracted and converted into retrievable text descriptions.
- After a knowledge base update, the model still references old version information: The knowledge base synchronization strategy or version management mechanism was improperly configured, failing to update the index promptly.
How to Confirm Proper Configuration
- Upload typical documents (e.g., new drug inserts, clinical guidelines). Check if parsed chunks contain all key information and are semantically complete.
- For documents containing charts, test if the model can answer questions related to the chart content. Evaluate the effectiveness of the image recognition module.
- Simulate a document update scenario. Upload a revised document. Verify that information in the knowledge base has synchronized to the latest version.
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.