What the data for this category looks like
Typical entities for this category include medical insurance fund supervision departments, medical insurance handling institutions, flight inspection teams, and designated medical and pharmaceutical institutions. Daily processed documents include audit notices, inspection work drafts, suspicious item lists, interview records, decision documents, rectification reports, and similar materials. Data sources cover medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. The update frequency is monthly. Core time-consuming business steps include filtering suspicious points from settlement data, retrieving medical records, prescriptions and charging records, identifying unauthorized expenses, and forming handling opinions. Operational errors may lead to fund losses, recovery of disallowed payments, protocol suspension, or administrative penalties.
What constraints do these characteristics impose on the vector model and index link
The monthly update frequency means real-time indexing is not necessary. Periodic full or incremental indexing can be used to reduce resource consumption. Multi-source heterogeneous data types include structured settlement data, unstructured documents, and traceability code information. This requires vector models to adapt to mixed-format text and encoded data. Core time-consuming business steps rely on rapid recall of associated rules and historical audit cases. As a result, index recall efficiency and accuracy directly affect overall business progress. Document lengths vary widely, ranging from short suspicious item lists to long inspection work drafts. Proper chunking logic must be configured to preserve semantic integrity.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity requirements of conventional documents such as audit notices and inspection work drafts |
| Overlap Length | 100–200 characters | Avoids semantic breaks across chunks, and supports context coherence for long documents |
| Similarity Threshold | Calibrated based on actual testing | Must match the suspicious point association accuracy requirements of audit scenarios |
| Single Search Limit | Top 10 entries | Balances recall efficiency and the volume of associated data required for audits |
maxToken | 4096 token | Adapts to the text length limits of medical insurance rule bases and settlement data |
defaultToken | 2048 token | Meets the context input requirements of daily audit queries |
Three common configuration mistakes
- Phenomenon: Parameter verification errors occur after configuring the vector model. Cause: The appropriate vector model is not selected based on the text data type of the audit scenario, and unnecessary multimodal processing logic is enabled.
- Phenomenon: Index construction takes longer than expected. Cause: Incremental indexing tasks are not set according to the monthly update frequency, and full indexing is executed directly without limiting the data source scale processed in a single run.
- Phenomenon: Index tasks cannot be triggered in a docker-compose deployment environment. Cause: Port mapping for the vector service is not opened in the docker-compose configuration file, preventing the indexing process from normally calling the model interface.
How to confirm the configuration is correct
- Upload a sample audit notice, and check whether the chunked text fragments retain complete semantic units without obvious sentence breaks.
- Initiate a suspicious point query, and verify that the number of returned search results matches the single search limit setting, and that the associated content is highly relevant to the audit suspicious points.
- Check the index task logs, and confirm that the task completes normally within the set monthly update cycle with no timeout errors.
- Verify that the
maxTokenanddefaultTokenconfigurations match the input length limits of the currently used vector model.
The values given 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.