Document Parsing and Chunking for Imaging Equipment

Data Characteristics

Imaging equipment (e.g., MRI, CT, X-ray machines) regulations and SOP documents are typically provided by equipment manufacturers or created by medical institutions based on national regulations and internal management requirements. These documents have a low update frequency, revised mainly when equipment is upgraded, regulations change, or internal processes are optimized. Document structures are often hierarchical and chapter-based, containing numerous charts, flowcharts, and technical parameters. Fields commonly include equipment models, serial numbers, calibration parameters, maintenance cycles, operating procedures, and safety specifications, accompanied by specific units such as millitesla (mT), kilovolt (kV), milliampere (mA), and second (s). Documents are primarily in PDF and DOCX formats, with few scanned copies.

Constraints on Document Parsing and Chunking

The hierarchical structure and chart content of imaging equipment regulation documents require parsers with strong structured recognition capabilities. This prevents information loss from flattening. The precision of technical parameters and units means that statements containing critical values cannot be arbitrarily truncated during chunking, as this could affect subsequent Q&A accuracy. The low update frequency allows for more resources to be invested in fine-grained processing during initial parsing. However, subsequent incremental updates need to quickly identify and handle localized changes. Common mixed Chinese and English terminology in documents demands specific text encoding and lexical analysis. Additionally, documents are often lengthy, requiring robust chunking strategies and good performance for large files.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Imaging equipment regulation documents can contain many images and charts, leading to large file sizes.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Ensures each chunk contains complete operating steps or parameter descriptions, while avoiding excessive length and redundant information.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–200 characters` (characters)	Maintains contextual continuity, especially when referencing or connecting across paragraphs.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large PDF or DOCX files can be time-consuming.
`Chunking Strategy`	`By Title` or `By Paragraph`	Prioritizes chunking based on the document's hierarchical structure, suiting the formal nature of regulation documents.
`Text Cleaning Rules`	`Remove headers/footers, standardize units`	Eliminates non-content information from documents and standardizes technical parameter units.

Common Mistakes

Key technical parameters (e.g., exposure dose, scan time) in the parsed results are empty or inaccurate. This occurs when statements are truncated during chunking, separating values from units.
Uploading large PDF documents results in a long wait or a 504 Gateway Timeout error. This usually happens when the PARSE_FILE_TIMEOUT_SECONDS configuration is too small, not allowing enough time for complex document parsing.
The document parsing node cannot extract response fields from the preceding PDF document parsing result component. This may be due to incompatibility between the parsing service component version and the model version, for example, Vllm0.10 having different specific parsing library dependencies for certain models.

Verification Steps

Upload a regulation document containing complex charts and technical parameters. Check if the parsed text content completely retains critical information and values.
Select a chapter with multi-level headings from the document. Verify that the parsed chunks follow the original document's chapter structure without significant disorder or flattening.
Upload a document larger than 200MB via API or interface. Observe if the parsing process completes normally and check logs for HTTP 404 or timeout errors.
Compare before and after parsing to see if units like millitesla and kilovolt remain consistent or have been standardized according to the configured rules.

Note: The values provided are common starting points. Measure them against your own samples for optimal results.

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.