Document Parsing and Chunking for Infectious Disease

Data Characteristics in this Category

Data for infectious disease products and reagents primarily comes from drug inserts, diagnostic kit instructions, clinical trial reports, disease prevention and control guidelines, academic papers, and regulatory approval documents. These documents update frequently, especially for new infectious diseases or drug-resistant pathogens, leading to rapid iteration of product information. Document structures for inserts typically include standard sections like product name, ingredients, indications, dosage, contraindications, adverse reactions, pharmacology and toxicology, and storage conditions. Clinical reports focus more on research methods, results, statistical data, and conclusions. Numerical fields, such as dosage, concentration, detection limit, sensitivity, and specificity, are critical and accompanied by strict unit identifiers (e.g., mg/kg, IU/mL, %). Text descriptions often involve complex medical terminology and abbreviations, and may include non-text content like tables and graphs.

Constraints from these Characteristics on "Document Parsing and Chunking"

The update frequency of infectious disease data requires the parsing system to have efficient document synchronization and processing capabilities to ensure knowledge base timeliness. Unique medical terminology and abbreviations in documents challenge word segmentation and entity recognition accuracy, requiring more refined text preprocessing. Standardized chapter structures help leverage structured parsing strategies, preventing content confusion. However, clinical trial reports and academic papers contain extensive unstructured or semi-structured text, as well as nested tables and charts, increasing information extraction difficulty. Accurate identification of critical numerical fields like dosage and concentration, along with their units, is essential; any parsing error can lead to significant consultation deviations. When parsing fails, the system needs to identify and flag problematic documents for manual intervention.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Accommodates PDF documents with numerous charts or high-resolution images.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient time for parsing large or complex documents, preventing timeout interruptions.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Balances context completeness and recall efficiency, adapting to high medical terminology density.
`Chunk overlap` (Chunk Overlap)	`100–200 characters` (characters)	Ensures contextual continuity between paragraphs, preventing critical information from being split.
`Enable Enhanced PDF Parsing`	`Yes`	Processes PDF documents with complex layouts, tables, and graphs, improving extraction accuracy.
`Multi-Vector Support` (Multi-vector Support)	`Calibrate by actual measurement` (Calibrate based on actual measurements)	Enhances retrieval capabilities for non-textual information like table data and chart titles.

Three Common Mistakes

After uploading documents containing complex charts or scanned images, content parsing is empty or incomplete. This usually occurs when enhanced PDF parsing is not enabled or improperly configured, preventing the system from effectively recognizing and extracting non-textual information.
Query results show incorrect or missing units for numerical information such as dosage and concentration. This may be due to inaccurate identification of the relationship between numbers and units during document parsing, or a chunking strategy that separates numbers from their units.
After local deployment of FastGPT, PDF document parsing fails to work correctly, with an error indicating an inability to connect to the parsing service. This suggests that external parsing services like MinerU are not properly deployed, started, or FastGPT's configuration parameters such as MINERU_URL are pointing to the wrong address.

How to Verify Proper Configuration

Upload typical infectious disease product inserts. Check if the parsed chunks are complete, if key chapter information is accurately retained, and if there are no obvious semantic breaks.
Upload clinical trial reports containing complex tables. Verify if table data is correctly identified and converted into retrievable text, or if it can be recalled via multi-vector support.
For documents containing numerical information like dosage and concentration, verify that numbers and their corresponding units are closely associated in the parsed results, and that unit types (e.g., mg, mL, %) are correctly identified.
Test PDF documents of varying sizes and complexities to ensure most documents are successfully parsed within the PARSE_FILE_TIMEOUT_SECONDS setting.

Note: The values provided are common starting points. Always measure 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.