What the data for this category looks like
Sources of medical aesthetics investment research data include clinical operation records of medical aesthetics institutions, product specification documents from consumable suppliers, compliance announcements released by industry regulatory authorities, regional medical aesthetics project fee disclosures, post-operative feedback archives of patients, and more. Update cycles vary by data source type. Compliance announcements are updated irregularly alongside policy adjustments. Consumable parameters are updated monthly alongside new product launches. Clinical records are archived daily. Document structures include long-form white papers, structured tables with fields such as project name, qualification number, specification parameters, and short single-case clinical text. Fields include project name, compliance qualification number, consumable specification, applicable body parts, and more. Units include yuan, milliliters, square centimeters, and more.
What constraints these characteristics impose on deployment and upgrade
Medical aesthetics investment research data contains a large number of structured tables and long text documents. Specialized configurations for table parsing and long text splitting must be adapted during deployment. Update frequencies vary widely across different data sources. Flexible incremental synchronization rules must be configured. The upgrade process must be compatible with legacy investment research workflows to avoid business interruptions from function changes. For offline deployment scenarios, local image storage paths and dependencies must be configured in advance to prevent image startup failures.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TABLE_MODE | Enable table structured parsing | Medical aesthetics data contains a large number of structured tables for consumables and fees. Structured fields must be extracted for investment research retrieval |
SYNC_INCREMENTAL_CRON | 0 0 2 * * * | Most medical aesthetics compliance announcements are updated at night. Performing incremental synchronization at 2 AM daily covers the latest data sources |
LEGACY_TEXT_PROCESSOR_ENABLE | Enable | Compatibility with legacy investment research workflows after upgrade. Retain original text processing logic |
LOCAL_PG_IMAGE_PATH | /opt/fastgpt/local_images | For offline deployment scenarios, specify the local image storage directory to avoid failures when pulling remote images |
PARSE_CHUNK_LENGTH | 800–1200 characters | Adapt to paragraph splitting requirements for long medical aesthetics documents. Balance retrieval accuracy and context completeness |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Support uploading large documents such as regional medical aesthetics industry white papers. Meet full data import requirements |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on internal samples before finalizing settings.
Three common configuration mistakes
- Issue: When using the v4.9.10 version interface to create a text collection, passing
split_modeasparagraphresults in split outputs that do not follow paragraph divisions. Cause: ThePARSE_PARAGRAPH_PRIORITYconfiguration item is not enabled, so the paragraph-first splitting mode does not take effect. - Issue: After upgrading from v4.9.0 to v4.9.10, existing text processing functions cannot be called normally. Cause: The
LEGACY_TEXT_PROCESSOR_ENABLEconfiguration item is not enabled after the upgrade is complete. The system enables the new processor by default, and the legacy logic is not loaded. - Issue: After copying images during offline deployment, the
aiproxy_pgimage fails to start. The console reportsimage pull failed. Cause: TheLOCAL_IMAGE_REGISTRY_PATHis not configured to point to the local image storage directory. The system attempts to pull a remote image, causing the failure.
How to confirm configurations are properly set
- Call the create text collection interface, pass
split_modeasparagraph, and check if the returned text split results follow natural paragraph divisions. Confirm that the paragraph-first splitting configuration is active. - View the system upgrade log to confirm that the
LEGACY_TEXT_PROCESSOR_ENABLEconfiguration item is enabled. Enter the knowledge base management interface and check if text processing functions load normally. - Run the
docker pscommand to confirm that theaiproxy_pgimage is running. Check if the local image path configuration matches the value ofLOCAL_PG_IMAGE_PATH. - Manually upload a medical aesthetics table document containing consumable parameters. Check if the parsed structured fields are fully extracted. Confirm that the table parsing configuration is active.
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.