What the Data for This Category Looks Like
Data for aesthetic medicine yield rate and market daily reports comes from regional aesthetic medicine service pricing filing systems, compliant aesthetic medicine institution operation ledgers, and public industry monitoring APIs. The data statistical cycle is one natural day, with updates occurring once per day. The document structure for individual data entries uses a structured table format, including fields such as project identifier, project name, covered region, service category, operating cost per unit, actual revenue per unit, profit coefficient, and data statistics date. Field units are yuan, yuan, and dimensionless respectively. Covered region and service category are text classification fields. The overall data volume is moderate, with no overly long unstructured content.
What Constraints These Characteristics Impose on Deployment and Upgrade
The daily update rhythm of aesthetic medicine data requires precise scheduled synchronization tasks during deployment to avoid conflicts with other system update windows. When upgrading, retain the original scheduled task configuration to ensure data continuity. The structured document format simplifies basic parsing workflows, but differences in multi-field classifications and units require dedicated field mapping rules. During upgrade, verify that mapping rules are compatible with new interface fields. Classification fields for covered region and service category require classification indexes for the vector database; during upgrade, rebuild indexes to adapt to new classification dimensions. Multi-source data access requirements require configured data merging verification rules to avoid broadcast errors caused by field conflicts.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Aesthetic medicine yield rate data is mostly structured tables with short parsing times; 300 seconds covers batch parsing needs during abnormal scenarios |
recall_top_n | Top 8 entries | Aesthetic medicine yield rate daily reports need to cover core service categories and regions. Too many recall results cause redundant broadcast information, while too few fail to cover core data |
similarity_threshold | 0.75 | Aesthetic medicine project names have similar naming conventions (such as "hyaluronic acid filler" and "hyaluronic acid injection"); a 0.75 threshold filters low-correlation recall results |
cron_expression | 0 2 * * * | Adapts to the T+1 update rhythm of aesthetic medicine data, triggers synchronization daily at 2:00 AM to avoid conflicts with other system update windows |
field_mapping_config | Map to standard fields | Aesthetic industry data fields have a high degree of standardization; direct mapping reduces custom configuration workload |
max_chunk_size | 1000 characters | Individual aesthetic medicine yield rate data entries are short; a 1000-character segment length ensures data integrity and facilitates model processing |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When parsing structured PDF daily reports for aesthetic medicine yield rates, FastGPT returns a
504 Gateway Timeouterror, while the parsing service log shows parsing succeeded. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted; the default timeout period is too short to accommodate batch parsing needs for aesthetic medicine data. - Symptom: Docker-deployed FastGPT services crash abnormally every 30 minutes, and restore to normal after manual restart. Cause: The
docker_restart_policywas not configured asunless-stopped; the system's default restart strategy does not cover periodic exceptions. - Symptom: After upgrading FastGPT to version 4.9.3, the knowledge base still shows version 4.9.2. Cause: The post-upgrade image pull and container rebuild steps were not executed correctly; the old version container was not fully replaced.
How to Verify Proper Configuration
- Manual synchronization tasks are executed, and confirmation is made that daily aesthetic medicine yield rate data entries are generated in the knowledge base, with fields aligned to the configured mapping rules.
- Scheduled synchronization task logs are reviewed to confirm that daily 2:00 AM synchronization tasks have no timeout or failure prompts.
- The similarity recall function is tested, with an aesthetic medicine project name input, and confirmation made that recall result relevance meets the preset threshold.
- The Docker container's restart policy is checked to confirm that the container restarts automatically following abnormal exit.
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.