Data Characteristics
Cardiovascular intervention R&D documents originate from clinical trial reports, device design specifications, biocompatibility test reports, manufacturing process files, registration submission data, and post-market surveillance data. Update frequency depends on the R&D stage and regulatory requirements. For example, clinical trial reports may update quarterly or annually, while design specifications update only with major revisions. Document structures typically include numerous charts, images, experimental data, medical terminology, and specialized abbreviations. Fields and units are highly specialized, such as "stent diameter (mm)," "balloon pressure (atm)," "vascular stenosis (%)", and "fractional flow reserve (FFR)." Strict requirements exist for numerical precision and unit consistency, often involving multiple international standards (e.g., ISO, ASTM, FDA guidelines).
Constraints on Context and Token Processing
The specialized and data-intensive nature of cardiovascular intervention R&D documents demands a long context window and robust token processing capabilities. Detailed clinical reports and design specifications are often lengthy, containing complex causal relationships and multi-variable data. The model needs to understand these relationships within a broader context to avoid information loss. Embedded charts and images require additional processing during structured parsing, potentially through OCR or image-to-text generation, which significantly increases input token counts. The frequent appearance of specialized terminology and abbreviations requires the model to accurately identify and maintain contextual consistency to prevent parsing errors due to ambiguity. Precise unit matching and conversion also depend on a sufficiently long context to identify the associated measurement dimensions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 token | Meets the context requirements for lengthy clinical trial reports and design specifications, reducing the risk of critical information truncation. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic completeness and retrieval efficiency, avoiding excessive fragmentation or overly long segments. |
Recall count (Retrieval Count) | Top 8–12 entries | Ensures coverage of potentially scattered key information points within documents, especially when multiple relevant paragraphs exist. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Based on the dataset's specialized terminology density and semantic similarity distribution, to avoid false positives or missed retrievals. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the OCR and parsing time for large PDFs or image-intensive documents, preventing timeout failures. |
Rerank result count (Reranked Return Count) | Top 5 entries | Provides sufficient high-quality context for precise question answering while maintaining model processing efficiency. |
Common Mistakes
- Symptom: Large language model output is incomplete or abruptly stops, with logs showing "LLM tokens: Input/Output = 31945/12288" before output truncation. Reason:
maxContextis set too low, causing the total input plus output tokens to exceed the model's limit, leading the model to stop generating before reaching its output cap. - Symptom: Many specialized terms or abbreviations are misunderstood in the parsing results, e.g., "FFR" is interpreted as a general financial term. Reason: The knowledge base lacks definitions and context for specific domain vocabulary, or retrieved relevant paragraphs do not provide sufficient contextual information.
- Symptom: After uploading large PDFs or documents with many images, there is a long period of unresponsiveness or an error "File parsing failed." Reason:
PARSE_FILE_TIMEOUT_SECONDSis insufficient to cover the OCR and parsing time for complexly formatted documents, or the file size exceeds theUPLOAD_FILE_MAX_SIZElimit.
Configuration Validation
- Select representative long and complex documents from this category (e.g., clinical trial reports) and conduct multi-round question-answering tests to verify accurate extraction and understanding of core information.
- Randomly sample parsed document segments and compare them with the original documents to check the accuracy of specialized terminology, data, and unit recognition, especially the conversion quality of charts and tables.
- Monitor system logs for token usage and file parsing times. Ensure stable system operation without frequent timeouts or truncation warnings during peak periods or when processing maximum file sizes. Use this data to calibrate
maxContextandPARSE_FILE_TIMEOUT_SECONDS.
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.