What data for this category looks like
Medical device investment research data mainly comes from three categories of official public documents: medical device registration certificates, clinical trial summary reports, industry technical guidelines, as well as manufacturer public product manuals and medical insurance negotiation declaration materials. The data update rhythm fluctuates with industry policy adjustments and new product approval cycles, with no fixed schedule. Document structures center on structured tables, including fields such as registration certificate numbers, model specifications, technical parameters, and clinical sample sizes. Units involve professional metering identifiers like millimeters, kilovolts, and milligrams. The documents also include long-form clinical trial analysis content and embedded product appearance and parameter annotation images.
What constraints do these characteristics impose on the document parsing and chunking step?
Medical device investment research documents have a high proportion of structured tables, with fields tied to units such as registration certificate numbers, model specifications, and technical parameters. Plain text parsing easily loses the association between fields and units, so the original table structure must be retained during chunking. Professional analysis content in long clinical trial reports must be split by document chapter nodes, instead of using fixed character length truncation which breaks the context logic of clinical data. Embedded images in product manuals contain parameter annotations, so text within the images must be extracted and associated with the corresponding chunks. The data update frequency fluctuates with industry policy adjustments and new product approval cycles, so incremental parsing must be supported to avoid repeated processing of already ingested documents.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Medical device documents have a high proportion of tables, retaining table structure prevents loss of fields such as registration certificate numbers and technical parameters |
MAX_PARSE_CHUNK_SIZE | 800–1200 characters | Medical device documents contain long parameter descriptions and clinical analysis content, this range balances context completeness and retrieval accuracy |
PARSE_IMAGE_OCR_ENABLE | Enabled | Product manuals include embedded parameter annotation images, OCR can extract text from images and associate it with corresponding chunks |
PARSE_PDF_ENHANCE_ENABLE | Enabled (supported in version 4.9.0 and above) | This function optimizes image and table recognition effects for PDF-format medical device documents, adapting to professional document parsing needs |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Clinical trial reports have long lengths, extending the parsing timeout allows complete content extraction |
AUTO_CHUNK_BY_HEADING | Enabled | Medical device documents have clear chapter divisions, chunking by headings ensures logical integrity of professional content |
The parameter values provided on this page are general recommendations that serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After configuring the document parsing node, the knowledge base search results do not include the corresponding file content, or the model returns that no relevant documents were found. Some tasks return status code 504. Cause: The global parsing switch
PARSE_FILE_ENABLEis not enabled, the uploaded file is in an encrypted format and cannot be decoded correctly, or the parsing timeout period is set too short. - Symptom: After importing a Word-format medical device document, table content is missing and image text cannot be recognized. Cause: The
PARSE_TABLE_ENABLEandPARSE_IMAGE_OCR_ENABLEconfiguration items are not enabled, or the chunk length is set too small, causing tables to be truncated. - Symptom: After configuring a custom URL parsing task, the task shows success but no data appears in searches. Cause:
PARSE_CUSTOM_URL_HEADERSis not configured to add necessary authentication headers, or the content returned by the target URL is in a non-text format.
How to confirm the configuration is correct
- Upload a single typical medical device document, such as a product manual, and check the parsed text preview to confirm that the table structure and image OCR text are retained.
- Run a chunking test to verify that chunks are split by document chapters, avoiding fixed character count truncation of professional parameter content.
- Upload an encrypted or large PDF-format clinical trial report to confirm that the parsing task does not time out and valid chunks are generated.
- Configure a custom URL parsing task to confirm that the text returned by the target URL can be properly extracted and corresponding chunks generated.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.