Data Characteristics
Regulatory submission documents for neurodegenerative diseases have diverse data sources. These include clinical trial reports, non-clinical study reports, pharmaceutical research data, informed consent forms, and ethics review documents. Documents typically exist as PDFs, Word files, or structured data tables. Update frequency is high, especially with clinical research progress and regulatory policy changes. Document structures are complex, containing numerous specialized terms, abbreviations, charts, and cross-references. Fields and units are highly specialized, for example, dosage units like mg/kg, time points like Week 12, biomarker concentrations like pg/mL, and gene mutation sites like APP V717I. Literature citation and traceability requirements are extremely strict.
Constraints on Vector Models and Indexing
The complexity of neurodegenerative disease regulatory submission documents imposes specific requirements on vector models and indexing. First, specialized terms and abbreviations in documents require models with strong domain understanding to prevent semantic drift. Second, high update frequency means the index needs to support efficient incremental updates and version management to ensure retrieval result timeliness. Complex internal document structures, such as nested tables and text within charts, require accurate key information extraction during the preprocessing stage. Furthermore, precise field and unit identification and matching directly impact retrieval accuracy. Vector models must distinguish subtle differences like mg/kg and mg. These constraints collectively necessitate selecting vector models with high semantic recognition capabilities and flexible segmentation strategies, and they challenge index update mechanisms.
Configuration Decisions
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | bge-large-zh or text-embedding-ada-002 | Balances understanding of Chinese specialized terms with general semantic capabilities |
Chunk size (Segment Length) | 500–800 characters | Ensures individual segments contain sufficient context and avoids excessive length leading to information redundancy or semantic generalization |
Chunk Overlap Length (Segment Overlap Length) | 50–100 characters | Maintains semantic continuity between segments, especially during cross-segment references |
Recall count (Recall Count) | Top 8–12 items | Increases initial recall coverage, providing richer candidates for subsequent re-ranking |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements 0.75–0.85 | High domain specificity requires careful calibration; too low introduces noise, too high may miss relevant items |
Maximum Index Document Size | 200 MB | Accommodates the volume of large clinical trial reports and pharmaceutical research documents |
Common Mistakes
- During search testing, even with a completed index, query results are empty or irrelevant. This may occur if the vector model's domain does not match the document content, leading to poor semantic embedding quality.
- Original document content cannot be traced after vectorization, with only vector data visible in the database. This typically happens when the indexing strategy does not configure original text storage, making it impossible to pinpoint the source of issues during debugging.
- After using a specific vector model, semantic retrieval returns abnormally high or low similarity values. This may be because the model's output vector norms are not normalized, affecting similarity calculation accuracy.
Confirmation of Correct Configuration
- Select representative key concepts and specialized terms from the category. Conduct multiple rounds of query tests to check if recall results include expected documents and segments, and verify relevance ranking.
- For specific fields and units in documents, such as
dosageorroute of administration, construct precise queries. Validate if the system can accurately identify and recall segments containing this information. - After an index update, perform retrieval tests on newly added and modified documents. Confirm that updated content is recalled promptly and accurately, and distinguished from older versions.
Note: 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.