What the data for this category looks like
Core data for this category comes from medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Daily processed documents include audit notices, inspection work drafts, suspicious point lists, interview records, handling decision documents, and rectification reports. Fields covered in single documents include involved entity information, expense details, descriptions of suspected violations, handling basis, and more. There is no unified fixed format. Some documents have attached associated scanned files or structured settlement fields. The data update frequency is monthly, requiring synchronization of newly added settlement, complaint, and rule adjustment content for the current month.
What constraints do these characteristics impose on the vector model and index link
The data characteristics of this category impose clear constraints on the vector model and index link. Multi-source heterogeneous data (structured settlement fields, unstructured documents, associated scanned files) requires indexes to support mixed format parsing and cross-source association. The monthly update frequency means an index update strategy of regular full construction plus small-scale incremental updates should be used to avoid redundant calculations. Documents have no unified format and diverse fields. Fixed chunking rules can easily split semantics, so chunking logic needs to be optimized combined with business key fields such as involved institutions and violation types. Audit data is associated with compliance determination, and index recall results must retain association identifiers with original data to ensure traceability of determination basis.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the core semantic unit of single documents in this category, avoids splitting key information such as audit suspicious points and handling basis |
| Overlap Length | 100–150 characters | Ensures semantic continuity between chunks, prevents cross-chunk violation descriptions from being split and lost |
defaultToken | 4096 | Adapts to the context length requirements of most documents in this category, ensures complete parsing of long texts such as rule bases and settlement details |
maxToken | 8192 | Covers the index construction requirements of ultra-long documents such as special inspection case files, avoids truncation of key handling process content |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Semantic similarity interval for distinguishing compliant and suspected non-compliant content, adapts to the rigor requirements of audit scenarios |
| Single Search Limit | Top 10 entries | Controls the number of recall results, avoids excessive irrelevant data interfering with audit personnel's determination |
Three common mistakes
- Phenomenon: Index recall results contain a large number of irrelevant daily settlement data and fail to accurately match audit suspicious points. Cause: Failed to optimize chunking and recall rules using key fields of audit scenarios such as violation types and involved expenses, only using general chunking logic.
- Phenomenon: Incremental index construction fails, with logs prompting that the context length exceeds the limit. Cause: Failed to adjust the
maxTokenconfiguration according to the document length of this category, using an excessively small parameter value to truncate long texts such as special case files. - Phenomenon: The number of search results does not match expectations, only a small amount of content is returned. Cause: Incorrectly set an overly high similarity threshold, or used an excessively small single search limit value, causing eligible suspicious point content to not be recalled.
How to confirm the configuration is correct
- Import a single audit notice document, view the chunking preview results, confirm that core suspicious points and handling basis are not split.
- Initiate a search for a specific violation type, verify that the recall results include key content of the corresponding document and have no obvious irrelevant entries.
- Execute a monthly incremental index construction task, confirm that the task is completed successfully with no context overrun or truncation errors.
- Compare recall results under different similarity thresholds, determine the value range that meets rigor requirements in combination with audit business scenarios.
Note: The configuration values listed above are common starting points. They should be evaluated 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.