What the Data for This Category Looks Like
The data for medical beauty intelligent due diligence reports mainly comes from the practicing qualification documents of medical beauty institutions, project compliance filing materials, compliance publicity information from regulatory authorities, and archived consumer feedback. Data updates are triggered irregularly alongside changes to institution qualifications, new project launches, and adjustments to regulatory policies, with no fixed cycle. Document structures fall into two categories: structured tables and unstructured descriptions. The structured section includes fields such as institution qualification number, project filing number, and compliance judgment result. The unstructured section includes compliance rules, service descriptions, and other content. Field units use standard administrative number formats, date formats, and RMB currency units.
Constraints on Knowledge Base Retrieval and Recall
The data characteristics of medical beauty due diligence reports impose multiple constraints on the retrieval and recall link. First, the mixed structured and unstructured document structure requires retrieval to support both precise field matching and semantic association matching, to avoid breaking the associated logic of structured fields during splitting. Second, data sources are scattered across multiple independent datasets, requiring support for targeted retrieval to limit scope and exclude irrelevant content outside the medical beauty field. Third, the irregular update rhythm requires configuration to support incremental updates and scheduled synchronization, to ensure the timeliness of retrieval results. Fourth, strict compliance-related judgment requirements need to filter low-relevance non-compliance content, to avoid interference with due diligence conclusions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Number of Recalled Entries | Top 8-12 entries | Medical beauty due diligence reports need to cover multi-dimensional compliance information. Too few entries will miss associated projects, while too many will increase context redundancy |
similarityThreshold | 0.72-0.85 | Medical beauty compliance-related content has high precision requirements. A threshold that is too low will introduce non-compliant association results, while a threshold that is too high will miss some compliant filing documents |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Medical beauty institution qualification documents often include multi-page scanned materials and structured tables, which take longer to parse. The default timeout may cause parsing failures |
chunkSize | 800-1200 characters | Medical beauty due diligence documents include long sections of compliance descriptions and structured fields. This chunk length can retain the associated semantics of fields and avoid splitting that destroys compliance judgment logic |
datasetIdFilter | Configure according to the target dataset ID | Medical beauty due diligence data is scattered across independent datasets such as compliance filings, institution qualifications, and regulatory publicity. Targeted retrieval can exclude content from irrelevant fields |
enableFieldRetrieval | Enabled | Medical beauty documents include structured fields such as qualification numbers and filing numbers. Field-level recall can accurately match compliance query keywords |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. Testing on internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Non-specified dataset content is returned when calling the retrieval interface, with no clear exception prompt. Cause: The
datasetIdFilterparameter is not correctly configured, and the retrieval scope is not limited. - Phenomenon: An error
datasetId is required for S3 filesis prompted when importing CSV compliance data from medical beauty institutions. Cause: When uploading CSV files stored in S3, the corresponding knowledge base ID is not bound in the upload configuration, causing the system to fail to associate the stored files with the dataset. - Phenomenon: Garbled characters or field loss occur when parsing medical beauty qualification scanned documents after enabling the enhanced PDF parsing function. Cause: The minerU version is not confirmed to be compatible with the current FastGPT deployment environment, or the scanned document OCR switch is not enabled, resulting in failed structured field extraction.
How to Verify Successful Configuration
- Run a targeted retrieval test using the keyword of a medical beauty institution qualification number, and verify that returned results only come from the preset compliance filing dataset.
- Import a single test CSV file, check for the error
datasetId is required for S3 files, and confirm that the upload configuration binds the knowledge base ID. - Enable the enhanced PDF parsing function, upload a medical beauty qualification scanned document, and verify that the parsed document retains structured fields and complete text content.
- Adjust the similarity threshold, use fuzzy compliance keywords, and verify that the number and relevance of returned results align with expectations.
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.