What the data for this category looks like
Personal care product research and investment data mainly comes from official brand filing documents, e-commerce platform sales and review data, third-party ingredient test reports, industry association sampling announcements, and consumer survey data. Update rhythms vary significantly: new SKU launches occur frequently, regulatory filing update cycles align with policy adjustments, and ingredient and efficacy claim updates follow formula iterations. Document structures include standardized fields such as filing number, ingredient percentage, applicable skin type, and package specification, plus unstructured content like product usage instructions and user review texts. Units involved include volume, concentration, price range, and others.
Constraints Imposed on Deployment and Upgrade by These Characteristics
Personal care product research and investment data has multiple sources, high update frequency, and exclusive fields. These traits create multiple constraints for deployment and upgrade workflows. Multi-source data requires adaptation to different interface permissions and format specifications. Configure multi-data source synchronization rules during deployment. High-frequency updated new product and regulatory data needs incremental synchronization mechanisms. This avoids resource waste from full synchronization tasks. Exclusive fields such as ingredient percentage and applicable skin type require custom parsing rules. General parsing modules cannot extract these fields accurately. Upgrades must support new filing formats and ingredient labeling specifications. This prevents existing parsing logic from failing. API call frequency limits for e-commerce data require adjusting request interval parameters. This prevents access bans.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Personal care product documents often contain long-text ingredient lists and test reports; the default timeout cannot complete full parsing |
UPLOAD_FILE_MAX_SIZE | 500 MB | Bulk imported industry sampling reports and e-commerce data compression packages have large volumes; the default limit will truncate valid content |
maxContext | 8000–12000 characters | Research and investment documents need complete ingredient descriptions, filing information, and user reviews; insufficient context length will lose key research and investment information |
Recall count | Top 8 entries | Personal care product SKUs have high similarity; multi-dimensional research and investment data need to be considered, and too many retrieved entries will exceed the context capacity limit |
Similarity threshold | 0.72–0.78 | Avoid retrieving irrelevant competing products or missing similar category alternative products, adapting to the segmented market characteristics of personal care products |
Incremental sync interval | Every 7 days | Match the average rhythm of new product launches and regulatory updates, balancing data timeliness and server load |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Configuration Errors
- Phenomenon: After importing personal care product filing documents, exclusive fields such as "ingredient percentage" and "applicable skin type" are empty. Cause: The
PARSE_FIELD_EXTRACTIONswitch is not enabled, or custom field mapping rules are not configured to adapt to the exclusive field format of personal care products. - Phenomenon: Calling a locally deployed model interface returns a
404 status codeor no valid answer, which occurs when adapting to FastGPT v4.15 and above versions. Cause: The model access address is not correctly configured inconfig.json, or the model name of oneapi does not match the model identifier configured in FastGPT, or the model call format of xinference V1 is not adapted, or the call parameters of CosyVoice2-0.5B are not correctly configured. - Phenomenon: The knowledge base cannot load data normally after associating with the database, and a
500 Internal Server Erroroccurs. Cause: The database connection string is not correctly configured, or the database port is not open for access permissions, and the initialization steps of database setup are not completed.
How to Verify Successful Configuration
- Upload a single brand filing document, check whether the parsing result extracts preset fields such as filing number and ingredient list, and verify the completeness of field extraction.
- Initiate an incremental synchronization task, check that the system only synchronizes documents with update time later than the last synchronization, and there are no duplicate import records.
- After configuring the model access information, initiate a test call, check whether it can normally return answers based on personal care research and investment data, and there are no model call error logs.
- Import a batch of sampling report compression packages, check that system resource usage does not have abnormal fluctuations, and there are no memory overflow or request timeout prompts.
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.