Data Characteristics
Market access quality documents typically include registration application materials, clinical trial reports, manufacturing process specifications, quality standards, batch inspection records, and stability study reports. These documents often come in PDF, Word, or Excel formats. They have complex structures and contain extensive normative text, charts, and data. Data update frequency is relatively low, primarily concentrated at key points in the product lifecycle, such as registration applications, change applications, or periodic reports. Document content is highly specialized, covering fields like pharmaceutics, medicine, and statistics. Fields and units adhere to strict industry norms, for example, dosage units (mg/kg), concentration units (% w/v), purity (%), and expiry dates (months/years). Specific abbreviations and terminology are also common.
Constraints on Vector Models and Indexing
The complex structure and specialized terminology of market access documents demand advanced segmentation strategies for vector models. Standard text segmentation can split critical information, compromising semantic integrity. A low update frequency means initial index construction quality is crucial. Subsequent incremental updates are less frequent, but each update may involve revisions to many related documents. Table and chart data within documents cannot directly capture structured semantics through text vectorization, requiring additional processing mechanisms. Strict field and unit norms require vector models to identify and differentiate these specialized entities, preventing inaccurate recall due to semantic confusion. For example, differing registration requirements for various drugs in different countries/regions necessitate that the model effectively capture these regional differences.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances context completeness with vector model processing efficiency |
Overlap Length | 100–200 characters | Ensures critical information is not lost at segment boundaries |
Recall count (Recall Count) | Top 8–12 entries | Enhances recall relevance while managing processing load |
Similarity threshold (Similarity Threshold) | Calibrate by measurement | Balances recall precision and generalization ability based on actual query results |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses long parsing times for large PDF/Word documents |
chunk_strategy | semantic_split | Prioritizes semantic integrity for complex structures in specialized documents |
Common Pitfalls
- Files remain in "Creating Index" status for an extended period after import. This usually indicates a file parsing timeout or an overloaded vectorization service.
- Query results show abnormally high and identical recall scores. This may point to improper vector model configuration, such as using an unsuitable embedding model or indexing algorithm.
- Vectorization is slow after uploading large PDF documents. This often results from insufficient file processing concurrency or server resource limitations.
How to Verify Configuration
- Upload typical market access documents and check if segmentation results maintain semantic integrity, especially around tables and key parameters.
- Query specific professional terms and normative expressions from the documents. Verify the relevance and accuracy of recall results, ensuring no irrelevant content appears.
- Simulate actual query scenarios. Ask different types of market access questions to evaluate the quality and information coverage of the system's responses.
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.