Data Characteristics
Respiratory system disease quality documents come from many sources. These include clinical trial reports, drug specifications, manufacturing process guidelines, quality standards, batch production records, validation reports for test methods, and various regulatory compliance documents. Document updates are driven by new drug launches, regulatory changes, process optimizations, and adverse event monitoring. Updates typically occur quarterly or annually. Some critical production records update per batch.
Documents are usually Word or PDF files. They often contain multi-level headings, charts, appendices, and cross-references. Fields and units are highly specialized. Examples include lung function indicators (FEV1, FVC in L/s or L), drug dosages (mg/kg), batch numbers, expiration dates (YYYY/MM/DD), and test results (e.g., microbial limits CFU/g). Specific terminology and abbreviations are common.
Constraints on Knowledge Base Retrieval and Recall
The characteristics of respiratory system quality documents impose specific constraints on knowledge base retrieval and recall.
Documents contain extensive specialized terminology and abbreviations. Vector models need strong domain vocabulary understanding. This prevents low recall due to vocabulary generalization. Multi-level headings and charts mean simple text segmentation can break contextual semantics. Structured information extraction needs consideration. Batch updates and regulatory changes create data timeliness requirements. The knowledge base must support efficient incremental updates and version management to ensure accurate retrieval.
Different document types (e.g., clinical reports vs. production guidelines) have significant structural and content differences. Differentiated segmentation strategies and metadata tagging may be necessary to optimize retrieval precision. For strict fields and units, recall results must accurately match or identify relevant values and units. Otherwise, misinterpretation can occur.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500-800 characters | Respiratory system documents often have long sentences and paragraphs. This length balances context integrity and retrieval granularity. |
Overlap Length | 50-100 characters | This ensures continuity of words at segment boundaries, improving recall of cross-paragraph information. |
Recall count (Recall Count) | 10 entries | Considering document complexity and cross-references, increasing the recall count improves coverage of relevant information. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | For precise matching of specialized terms and concepts, a higher threshold filters for strongly relevant results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | This provides sufficient time for parsing large PDF/Word documents, preventing file processing failures due to timeouts. |
ENABLE_CHUNK_OVERLAP_STRATEGY | true | Enabling chunk overlap strategy is suitable for long narratives and concept overlaps common in respiratory system documents. |
Common Pitfalls
- Retrieval results show many irrelevant fragments. Many items are recalled, but relevance is low. This may be due to a
Similarity threshold(Similarity Threshold) set too low, failing to filter noise effectively. - Uploading large batch production records or clinical reports results in long processing times or errors. The
PARSE_FILE_TIMEOUT_SECONDSparameter is usually insufficient for complex document parsing. - Queries about specific disease diagnostic criteria or drug dosages lack critical information in retrieval results. The
Chunk size(Chunk Length) may be too short, cutting off key context and affecting semantic completeness.
How to Verify Configuration
- Select complex queries from typical respiratory system quality documents. Check if recall results contain all expected key information and evaluate their contextual completeness.
- Use queries with specialized terms and abbreviations. Observe the matching accuracy of these terms in recalled items and the presentation of relevant values and units.
- Simulate uploading the largest quality documents. Confirm that the file parsing process completes smoothly, without timeouts or error messages.
- For documents with clear revision records, test queries for old and new versions. Confirm the knowledge base correctly distinguishes and recalls the latest valid information.
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.