What the Data for This Use Case Looks Like
Medical aesthetics research reports targeting finance, insurance, and wealth management fields draw from multiple sources. These include industry analysis documents published by the China Association of Plastic Surgery and Aesthetics, quarterly operational data publicly released by compliant medical aesthetics institutions, product and technical materials from medical aesthetics consumable manufacturers, medical aesthetics institution registration information published by local health commissions, claims reports from medical aesthetics insurance institutions, and risk control data from medical aesthetics installment platforms.
Update frequencies vary by data source:
- Industry analysis documents are updated semi-annually or annually
- Institutional operational data is updated quarterly
- Consumable manufacturer materials are updated with new product launches or policy adjustments
- Regulatory registration and financial data are synchronized in real time
Typical document structures include overall industry overview, segmented service type analysis, compliance standard interpretation, institutional operation reference data, and financial product correlation analysis.
Fields included in the data are:
- Institution registration number
- Project service type
- Single-service charging standard
- Consumable model number
- Compliance regulatory clause matching items
- Number of claims cases
Units for these fields are: none, service category, yuan per service, model number, clause number, case count.
Constraints During Model Integration and Configuration
Medical aesthetics research reports targeting finance, insurance, and wealth management fields have scattered data sources and significant format differences. Sources include PDF-format industry white papers, Excel-format institutional operational and financial risk control data, and web-format registration information. Adaptive parsing rules for multi-source data must be configured to avoid parsing failures.
Update cycles vary widely across data sources. Real-time financial risk control data and semi-annual industry reports have different synchronization periods. Incremental synchronization trigger conditions must be configured to ensure the timeliness and compliance of recalled data.
Data includes structured charging standards, unstructured compliance clauses, and financial claims data. Field extraction mapping rules must be configured to ensure accurate matching of target information during retrieval.
Long documents account for a large share of the dataset. Some industry reports exceed 50 pages. Reasonable segment lengths must be configured to avoid overly long single segments that impair model understanding.
Configuration Parameter Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | 8–12 entries | Medical aesthetics research reports contain multi-dimensional segmented data and financial correlation information. Too few entries will lose critical content, while too many will exceed the model's context limit |
RERANK_ENABLE | Enabled | Medical aesthetics research reports mix structured and unstructured data. Reranking models improve the efficiency of accurately matching compliance clauses and financial data |
RERANK_TOP_N | Top 6 entries | Retain highly relevant segmented projects and financial correlation data after reranking, avoiding redundant information interfering with model inference |
maxContext | 8000–12000 characters | The average length of segmented single medical aesthetics research reports is moderate. This range covers complete segmented service and financial analysis content |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing some long documents takes a long time. This duration avoids triggering recall before parsing is complete |
SIMILARITY_THRESHOLD | 0.72–0.78 | Medical aesthetics research reports contain professional medical terminology and financial data. This threshold filters low-relevance non-target data |
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 individual analysis. It is recommended to test on your own samples before finalizing values.
Three Common Configuration Mistakes
- Phenomenon: After enabling the Rerank model and completing knowledge base indexing, the online recall test returns results that have not been reranked, and the result count matches the initial recall count. Cause: The rerank model is not bound in the knowledge base retrieval configuration, or the rerank model's API key configuration is invalid.
- Phenomenon: After setting the knowledge base reference upper limit to 3000, no recalled knowledge base content is received during large model conversations. Cause: The configured total reference character count exceeds the FastGPT
maxContextthreshold, and the system automatically filters all recalled results. - Phenomenon: When using a locally deployed open-source indexing model, charging standards and compliance clause fields in medical aesthetics research reports cannot be correctly extracted. Cause: The selected indexing model is not optimized for structured fields in medical and health documents, and the word segmentation rules cannot match industry-specific terminology.
How to Verify Successful Configuration
- Navigate to the knowledge base retrieval configuration page, check the
RERANK_ENABLEswitch status and bound rerank model information, and confirm consistency with preset configurations. - Perform a single knowledge base recall test, compare the initial recall count and the final returned result count, and confirm that the rerank model is active.
- Upload a medical aesthetics research report document containing financial correlation data, check the parsed segment length and field extraction results, and confirm compliance with preset configuration rules.
- View FastGPT system logs, confirm there are no
Request Timeoutor field parsing failure error messages, and verify configuration stability.
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.