What Data Looks Like for This Category
Daily documents for this category include audit notices, inspection work papers, suspicious item lists, interview records, handling decision letters, and rectification reports. Data sources include medical insurance intelligent monitoring systems, fund supervision rule databases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Data updates occur monthly. Most documents contain fields such as supervision rule entries, settlement details, suspicious point annotations, and basis for violation determination. There is no unified fixed unit, and field content is adjusted flexibly based on document type.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
Batch data updated monthly requires indexes to support bulk import and incremental updates, to avoid excessive time spent on full index operations each time. Significant structural differences across multiple document types require adapting chunking logic for different fields, to prevent key supervision information from being split. Settlement data and traceability codes contain large amounts of structured numerical values and codes. The vector model must support semantic extraction from mixed fields. The basis for violation determination in audit-related documents requires precise matching. The accuracy of index recall directly affects the accuracy of subsequent document drafting.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to long paragraphs of supervision rules and settlement details in documents of this category, avoiding splitting key violation determination basis |
| Overlap Length | 100–200 characters | Ensures semantic coherence across chunks, avoiding breaking continuous logic in suspicious item lists and interview records |
| Similarity Threshold | 0.75–0.85 | Precisely matches supervision rules and medical insurance settlement suspicious point data, reducing false recall probability |
| Single Search Limit | Top 8 entries | Focuses on core audit suspicious points, avoiding excessive retrieval results interfering with drafting of handling decision letters and rectification reports |
defaultToken | 4096 | Adapts to long text content in the fund supervision rule database, ensuring complete extraction of rule semantics |
maxToken | 8192 | Supports integrating audit basis from multiple documents, meeting context requirements for document drafting |
Three Common Mistakes
- Phenomenon: The index task stays in running status for a long time with no progress updates. Cause: Incremental indexing is not configured for monthly batch medical insurance settlement data, and full repeated indexing is used instead, resulting in task time far exceeding expectations.
- Phenomenon: Retrieved document chunks do not match audit suspicious points, or some fields are empty. Cause: Chunk length is set too small, splitting continuous violation determination content in interview records and handling decision letters, leading to semantic breakage.
- Phenomenon: Configuration error occurs when calling the specified vector model, and vectors cannot be generated. Cause: API access parameters for the model are not configured correctly, or the
maxTokeninput limit of the model is not adapted, resulting in request interception.
How to Confirm Configurations Are Correct
- Check the index task progress logs, confirm that monthly bulk imported data has completed sharding processing with no error messages.
- Launch a retrieval test for audit suspicious points, verify the matching degree between returned document types and business scenarios, and adjust relevant configuration items to meet business needs.
- Verify the calling status of the vector model, confirm that the model can normally generate vectors for corresponding document chunks with no connection or parameter errors.
- Check the chunk and overlap parameter configurations, ensure that long documents are not over-split, and key supervision fields are not separated.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.