What the data for this category looks like
Medical beauty investment research data comes from public annual reports of medical beauty institutions, industry association survey materials, medical beauty product registration and filing documents, clinical treatment case reports, and regulatory policy documents. Update frequency varies by document type: product filing documents update with new product launches, industry research reports update quarterly or semi-annually, and regulatory policies are released irregularly. Document structures include long-text research reports, structured parameter tables, clinical cases with mixed text and images, and collaborative project documents with multi-level directories. Fields include single treatment cost, maintenance cycle, compliant filing number, physician qualification level, and corresponding units: yuan, month, number, and qualification level names.
Constraints imposed on document parsing and chunking
Medical beauty investment research data has a high proportion of structured parameter tables. Table structures must be retained to ensure parameter accuracy during retrieval. Long-text research reports and multi-level directory collaborative documents often have mixed content across chapters. Chunks must be split according to directory hierarchy. Treatment before-and-after comparison images in clinical cases contain extensive visual information. Image OCR must be supported to extract text for retrieval. Feishu collaborative project documents are often stored by subdirectory classification. All subdirectory content must be traversed to complete full parsing. Large-volume research report files often exceed default parsing limits. Larger file processing thresholds must be adapted.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Medical beauty industry research report single files often exceed 500 MB, so large file upload limits must be adapted |
maxChunkSize | 800–1200 characters | Medical beauty documents contain many professional terms and structured parameters. Excessively long chunks will lose context association |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing long clinical reports requires significant time, to avoid mid-parsing timeout interruptions |
enable_table_parse | Enabled | Medical beauty documents contain many product parameter tables, so structured table information must be retained for precise recall |
parse_image_ocr | Enabled | Clinical cases contain before-and-after comparison images, so text extracted from images must be added to retrieval content |
PDF_MAKER_API_KEY | Key obtained from the external parser service provider backend | The external pdf-maker parser requires a key for identity verification. Parsing cannot start without configuration |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- File upload parsing returns a 413 error. The cause is that the
UPLOAD_FILE_MAX_SIZEconfiguration value is smaller than the actual size of the uploaded file. - Doc documents and embedded images in Feishu multi-level directories cannot be retrieved. The cause is that the
enable_subdir_parseandparse_image_ocrconfiguration items are not enabled. - No results are returned after calling the API to upload a parsing task, or external pdf-maker parsing fails. The cause is that
PDF_MAKER_API_KEYis not configured, or the API callback receiving address is not enabled.
How to Confirm Configuration is Correct
- Upload a single medical beauty research report PDF exceeding 500 MB, confirm that no 413 error prompt appears.
- Upload a Feishu document package containing multi-level directories, confirm that all doc documents and embedded images in subdirectories are parsed.
- Submit a clinical report containing structured parameter tables and comparison images, confirm that the parsing result retains the table structure and OCR-extracted image text.
- Call the API to upload a parsing task, wait for the task to complete, and confirm that the returned result contains parsed chunked content.
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.