Data Characteristics
Cardiovascular R&D documents come from various sources. These include clinical trial reports, research papers, patent literature, drug inserts, and internal experimental records. Documents are updated frequently. Clinical trial data and new research findings may update weekly or even daily. Document structures are often complex. They contain many specialized terms, abbreviations, charts, and tables. For example, clinical trial reports follow ICH GCP guidelines. They include sections like study protocols, subject information, adverse event reports, and statistical analysis results. Fields cover drug dosage, treatment cycles, patient baseline characteristics (e.g., age, sex, BMI), biomarkers (e.g., troponin, BNP), electrocardiogram (ECG) parameters, and imaging indicators (e.g., ejection fraction LVEF, vessel stenosis). Units strictly follow international standards, such as milligrams (mg), milliliters (mL), millimeters of mercury (mmHg), millimoles per liter (mmol/L), and seconds (s). Statistical information like upper and lower limits, averages, and standard deviations often accompanies these units.
Constraints on Document Parsing and Chunking
The complexity of cardiovascular R&D documents imposes specific requirements on document parsing and chunking. First, precise identification of numerous specialized terms and abbreviations is necessary. This prevents semantic loss or ambiguity. Chunking must maintain contextual integrity. Second, key data in charts and tables, such as clinical trial results or drug interactions, must be effectively extracted. This data must be linked to text content. Simple text chunking is insufficient. Third, the highly standardized document structure, like fixed sections in clinical trial reports, makes structure-based parsing more efficient and accurate than pure text length chunking. Finally, the precision of data units and statistical information requires chunking to differentiate values from units. It must also preserve their association. This is crucial for subsequent knowledge extraction and question-answering accuracy. Traditional chunking by character count or simple delimiters often truncates key data or loses context.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800-1200 characters | Balances contextual relevance of cardiovascular specialized terms. Avoids fragmentation from overly short chunks. Controls the information volume per chunk. |
Chunk Overlap Length | 100-200 characters | Ensures overlap of critical information in adjacent chunks. Improves recall rate. Reduces semantic boundary truncation issues. |
Yes noEnabledPDFEnhanced Parsing | Yes | PDF documents are common in the cardiovascular field. Enhanced parsing more accurately identifies charts, tables, and complex layouts. |
Maximum Paragraph Depth | 3 | Documents like clinical trial reports typically have about three levels of heading structure. This setting helps maintain the logical integrity of paragraphs. |
Index Size | 128 | Balances indexing efficiency and recall accuracy. Suitable for vector representation of specialized terms in the cardiovascular domain. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses potentially long parsing times for large clinical trial reports or complex PDF files with many charts. |
Common Pitfalls
- Document parsing times out, returning a 504 Gateway Timeout status code. The default parsing time is insufficient for large or structurally complex PDF documents.
- Key data (e.g., drug dosages, biomarker values) separate from units or statistical descriptions after chunking. This occurs when the chunking logic fails to recognize the strong association between values and units, or truncates them at chunk boundaries.
- The API file library interface call returns an empty list, failing to retrieve expected documents. This indicates incorrect API interface configuration, or improper
offsetandlimitparameter settings, causing the query range to exceed available data.
Verification
- Randomly sample multiple cardiovascular R&D documents of different types. Check parsing results. Confirm charts and tables are effectively identified and converted to retrievable text without significant structural errors.
- Perform keyword searches and Q&A tests on the parsed chunks. Evaluate recall accuracy and contextual completeness for key information like specialized terms, drug names, and disease symptoms.
- Review log output. Confirm no timeout or memory overflow errors occurred during document parsing. Ensure the parsing success rate meets the expected threshold.
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.