Data Characteristics
Rehabilitation device R&D documentation primarily includes internal design specifications, test reports, clinical trial data, draft user manuals, and regulatory compliance files. These documents are updated frequently, especially during product iterations and regulatory revisions. Document structures are complex, often containing numerous charts, CAD drawing references, medical image analysis results, and multilingual versions. Fields include standard engineering parameters, biomechanical indicators, physiological signal data (e.g., EMG, EEG), rehabilitation assessment scales (e.g., FIM scores), and device performance data under specific pathological conditions. Diverse unit systems are present, encompassing both International System of Units (SI) and non-SI units common in specific medical fields, such as N·m for torque, ° for angles, and μV for biological signals. Units may be mixed or abbreviated inconsistently across documents.
Constraints on Document Parsing and Chunking
The complex structure and diverse sources of rehabilitation device R&D documentation impose high requirements on document parsing. Extensive charts and CAD references mean pure text parsing is insufficient to capture complete information; enhanced image recognition is needed to extract captions and key data. High update frequency necessitates an efficient incremental update mechanism in the parsing process to avoid re-parsing large amounts of unchanged content. The diversity and mixed use of fields and units increase the difficulty of information extraction. Models must recognize context and normalize units to prevent misinterpretation. For example, a "force" parameter might appear as "load," "pressure," or "acting force" in different documents, with units potentially being Newtons or pounds-force. Furthermore, the rigor of clinical data and regulatory files demands that parsing and chunking accurately identify and preserve the original logical structure, especially the integrity of section titles, list items, and tabular data, to prevent critical information from being cut or lost, which would affect the accuracy and reliability of subsequent Q&A.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Rehabilitation device documents are often technical specifications or test reports. Individual logical paragraphs contain significant information; overly short chunks risk losing context, while overly long ones increase recall noise. |
overlap_size | 100–200 characters | Ensures sufficient contextual overlap between adjacent chunks, especially for paragraphs involving complex logical deductions or multi-step descriptions. |
max_image_extract_tokens | 4000 | Charts and figures are dense in rehabilitation device documents. Captions and embedded text contain a large amount of critical information, requiring sufficient extraction. |
parse_table_as_text | true | Extensive tabular data in rehabilitation device documents carries test results and parameters. Parsing it as text aids retrieval. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Clinical reports and large design specification files can take a long time to parse. Ample time is reserved to prevent timeout interruptions. |
embedding_model | text-embedding-ada-002 or higher | Medical terminology is specialized and semantically complex, requiring a high-performance model to accurately capture its deeper meanings. |
Common Pitfalls
- After uploading a PDF file, the system reports "Request Error" or "Parsing Failed." This usually occurs because the PDF file has missing embedded fonts, encryption protection, or is corrupted, preventing the parser from reading the content correctly.
- After document parsing, some critical data or chart descriptions are not recalled during retrieval. This might be due to
chunk_sizebeing set too small, causing captions or tables to be incorrectly split, ormax_image_extract_tokensbeing insufficient to fully extract text information from images. - During Q&A, the model misunderstands specialized terminology or units unique to rehabilitation devices. This often results from an inappropriate
embedding_modelchoice, which fails to effectively learn and represent the semantics of medical domain-specific vocabulary.
Verification Steps
- Upload a sample rehabilitation device R&D document containing complex charts, tables, and specialized terminology. Check if the parsed chunks completely retain captions, table content, and their contextual relationships.
- Select paragraphs from the document containing key parameters (e.g., "maximum load 150 kg," "EMG signal 200 μV"). Verify that this information can be accurately recalled through keyword or semantic search.
- Upload multiple documents containing different unit systems (e.g., SI and non-SI units). Randomly select some chunks and manually verify if the parsed content correctly identifies units or infers context.
- Check the FastGPT backend parsing logs to confirm if any parsing failures occurred due to file format issues, timeouts, or memory overflow.
The values provided are common starting points and should be measured against the reader's 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.