Data Characteristics for This Category
Smart triage systems primarily process data from biomedical literature, drug inserts, clinical guidelines, disease treatment pathways, and medical device manuals. These documents update frequently. New drug approvals, clinical trial results, or revised treatment protocols can lead to daily or weekly updates. Document structures vary, containing numerous charts, cross-references, specialized terminology, and abbreviations. Fields and units are highly standardized. For example, drug dosages are precise to milligrams (mg) or international units (IU). Test indicators include mmol/L and ng/mL. Disease diagnoses often use ICD codes. Medical device parameters include voltage (V) and power (W).
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
High update frequency requires document parsing workflows to support incremental updates and version management, ensuring knowledge base timeliness. Diverse document structures, especially charts and cross-references, challenge parser accuracy. The system must identify and correctly extract text from charts and handle contextual connections of references. Specialized terminology and abbreviations require chunking to maintain semantic integrity, avoiding breaks within critical terms. Highly standardized fields and units, such as mmol/L or ICD-10 codes, mean that chunking must preserve the atomicity of this information. This facilitates precise retrieval and matching, preventing units from separating from values or codes from truncating due to improper chunking.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Preserves contextual integrity, avoids truncating key information, and balances retrieval efficiency. |
Overlap Length | 100–200 characters | Ensures semantic coherence at chunk boundaries and improves recall. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large clinical guidelines or drug inserts, preventing upload failures. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles the parsing time for complex PDF or Word documents, preventing timeout interruptions. |
extract_table_content | true | Extracts structured data from tables, ensuring completeness of drug dosages, test indicators, and other information. |
extract_image_description | true | Parses text descriptions within images, obtaining medical information conveyed by images. |
Three Common Mistakes
- After importing a Word document, images do not display in the conversation. This can occur if image links are not correctly converted to externally accessible URLs during the conversion process.
- Parsing large PDF documents results in a timeout error. This often happens when
PARSE_FILE_TIMEOUT_SECONDSis set too low, failing to account for document complexity and page count. - When retrieving chunked index content via API, the number of returned entries does not match expectations. This may relate to the
Chunk sizeandOverlap Lengthsettings, leading to uneven chunk granularity.
How to Confirm Correct Configuration
- Upload typical documents (e.g., drug inserts, clinical guidelines). Check if the parsed content correctly recognizes and displays text, especially text descriptions within tables and images.
- Retrieve specific professional terms or disease codes via API. Verify that the returned chunks completely include the term and its contextual information, confirming semantic coherence.
- Simulate user queries. Evaluate the smart triage system's response quality to complex medical questions. Confirm that critical information (e.g., dosage, units, diagnostic criteria) is accurately cited from the parsed documents.
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.