What This Category's Data Looks Like
Data sources for this category include 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 papers, suspicious item lists, interview records, handling decision documents, and rectification reports. Data is updated monthly. Fields include violation type, involved entity, involved amount, processing progress, and others. Common units are yuan, count, and specific time nodes.
Constraints Imposed on Knowledge Base Retrieval and Recall
Monthly updated data requires the knowledge base synchronization cycle to align with the monthly business update rhythm, preventing retrieval of expired supervision rules or settlement data. Multiple types of heterogeneous documents—structured suspicious item lists, semi-structured interview records, and formatted handling decision documents—require retrieval adapted to different text structures, ensuring recalled content accurately matches audit needs. Fine-grained association between medical insurance settlement data and drug and consumable traceability codes requires retrieval to cover all associated fields, avoiding missed potential violation suspicious points. The rigor requirement for violation determination in audit scenarios limits the redundancy of recall results, preventing interference with the efficiency of subsequent ledger summary and report generation.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets the rigor requirements of medical insurance supervision rules, avoids recalling irrelevant low-similarity documents |
limit | Top 10 entries | Controls the number of recall results, reduces information redundancy during ledger summary, and meets the efficiency needs of audit scenarios |
searchMode | vector | Adapts to the vector association characteristics of medical insurance settlement data and traceability codes, prioritizes recalling semantically matching content |
usingReRank | Enabled | Rearranges initial recall results, prioritizes matching core audit documents such as suspicious item lists and violation rules |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands synonymous expressions related to audits, covers regulatory terminology variants such as fraudulent settlement and insurance fraud |
collectionIds | Bind supervision rule base and settlement data set | Limits the retrieval scope, avoids introducing irrelevant knowledge base content unrelated to medical insurance supervision |
Three Common Mistakes
- Phenomenon: After switching the knowledge base vector model, the interface shows no progress, and the original configuration cannot be restored. Cause: The cache index associated with the old model was not cleared in dataset management, causing the system to fail to complete the model switch verification.
- Phenomenon: After adding a specified knowledge base, the overall question-and-answer workflow triggers an error, but separate knowledge base search tests return results normally. Cause: No cross-knowledge base context splicing rules were configured, causing format conflicts when merging multiple knowledge base contents.
- Phenomenon: When no text understanding model is configured, violation clause details in audit notices cannot be accurately recalled. Cause: Audit documents contain a large number of professional regulatory terms, relying on text understanding models to parse structured regulatory clauses in long texts. Without configuration, semantic matching accuracy will decrease.
How to Verify Proper Configuration
- Perform a separate knowledge base retrieval test, check whether the returned results cover specified data sources such as medical insurance settlement data and supervision rule bases.
- Start a simulated audit ledger summary task, check whether the types of recalled documents match the current audit scenario requirements.
- View the system configuration panel, confirm that the values of parameters such as
similarityandusingReRankmatch the preset configuration. - Check the dataset association list, confirm that
collectionIdshas bound the correct medical insurance supervision-related knowledge base collections.
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.