Data Characteristics
Dermatology R&D documents include clinical trial reports, pathological analyses, drug mechanism studies, patient follow-up records, medical imaging reports, and literature reviews. Data sources are diverse, covering hospital information systems, research institution databases, and industry journals. The update frequency is relatively high, especially during new drug development and clinical trials. Document structures typically include standardized sections such as background, methods, results, discussion, and conclusion. However, narrative styles and detail levels vary significantly within sub-sections. Common fields and units include dosage (mg/kg), treatment duration (weeks), lesion area (cm²), scoring scales (e.g., PASI, EASI), and various biomarker concentrations (ng/mL). Units are highly standardized.
Constraints on Vector Models and Indexing
The diverse sources and rapid update frequency of dermatology R&D documents require vector models to effectively process data in various formats and structures. They must also support incremental indexing to avoid redundant full re-indexing. Documents contain extensive specialized terminology, abbreviations, and numerical data. This demands high semantic understanding from vector models to accurately capture relationships between medical concepts. For example, the implicit relationship between morphological features in dermatopathology descriptions and gene expression data requires vector models to represent them correctly in the embedding space. The presence of standardized fields and units allows for precise filtering and sorting using structured information after vector recall. Given the sensitivity of clinical trial reports, the indexing process must ensure data isolation and access control to prevent information leakage. This is a critical security consideration for the indexing system.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 512–768 characters (characters) | Balances semantic completeness and context window limits, preventing noise from overly long segments or loss of critical details. |
Chunk Overlap Length (Segment Overlap Length) | 64 characters (characters) | Ensures contextual continuity at segment boundaries, improving the coherence of recall results. |
Recall count (Recall Count) | 10–20 entries (items) | Controls computational resource consumption while maintaining coverage, providing sufficient candidates for subsequent re-ranking. |
Similarity threshold (Similarity Threshold) | Calibrated by empirical measurement | Evaluates recall quality to ensure relevance of recalled results, typically between 0.75–0.85. |
Rerank result count (Re-rank Return Count) | 3–5 entries (items) | Focuses on the most relevant results, reducing user screening effort and improving the precision of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses potentially long parsing times for large clinical trial reports or multimedia attachments. |
Common Pitfalls
- Files remain in an "indexing" state for an extended period. This can happen if the selected indexing model does not support the FastGPT version or if the model interface response times out.
- After configuring a locally deployed vector model, indexing tasks fail or recall results are poor. This may be due to incorrect model service address or port configuration, or insufficient model inference resources leading to slow responses.
- Attempting to add multiple configurations for the same model name in the configuration interface (e.g., for both indexing and re-ranking) results in new configurations overwriting old ones. This is due to the system's uniqueness constraint on model names; different model names are required for different purposes.
Verification Steps
- Upload a typical dermatology R&D document and check if its indexing status shows "completed".
- Query using key terms from the document. Compare the relevance and ranking of recall results to assess semantic understanding accuracy.
- Ask questions involving numerical information from the document. Verify if the model can correctly extract and process this data, and interpret it in conjunction with units.
Note: The values provided are common starting points. They should be measured against specific 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.