Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from the national medical security information platform, hospital HIS and medical insurance interfaces, medical insurance catalog and coding library, and cross-provincial medical settlement platform. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Data is updated daily. Document fields include diagnosis and treatment item codes, pharmaceutical supplies codes, expense details, settlement time, insured person identity identifiers, and more. A single settlement data is associated with multiple types of business documents, with a strict structure and clear field boundaries.
What constraints these characteristics impose on the vector model and index link
The daily data update rhythm requires indexes to support scheduled synchronization or incremental updates, to avoid performance loss from full reconstruction. Mixed input of multiple types of business documents requires chunking rules adapted to the structure of different documents, to avoid confusion of cross-type content. Fields such as diagnosis and treatment item codes and pharmaceutical supplies codes coexist with semantic content. Vector models need to balance structured code matching and natural language semantic understanding. At the same time, the maxToken setting must match the model's input upper limit. The feature that a single settlement is associated with multiple documents requires indexes to support cross-document associated recall, to ensure complete audit basis.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-page text length of medical insurance documents, avoids splitting that destroys the semantic integrity of business documents |
| Overlap Length | 100–200 characters | Compensates for semantic gaps after long text chunking, ensures that the context of associated fields is not truncated |
| Similarity Threshold | Calibrated based on actual tests | Must match the business rigor of medical insurance audits, avoids false recalls or missed recalls |
| Single Search Limit | Top 10 results | Controls the scale of returned results, adapts to the viewing efficiency of audit personnel |
maxToken | 4096 token | Adapts to the input upper limit of mainstream vector models, ensures complete loading of document context required for audits |
defaultToken | 2048 token | Serves as the default context window, balances retrieval recall and inference efficiency |
Three Common Mistakes
- A search test returns an error indicating index matching failure. The cause is that structured data from the medical insurance catalog coding library is not synchronized to the vector index, only non-coding text content is synchronized.
- Only vector data is stored in the vector database, and original document content cannot be directly associated. The cause is that the storage path of original documents and the association mapping of vector indexes are not configured, only vectorized feature data is uploaded.
- Semantic retrieval scores are abnormally high, exceeding reasonable ranges. The cause is that the diagnosis and treatment item code fields are not separately processed structurally, causing semantic matching to be interfered with by the strong features of the code fields.
How to Confirm the Configuration Is Correct
- Run a full synchronization task, check whether there are classification markers for medical insurance document types in the synchronization log, confirm that the chunking rules adapt to all business documents.
- Enter a pending audit settlement detail, check whether the retrieved results include associated documents such as corresponding prescriptions and expense details, confirm that cross-document associated recall is effective.
- Adjust the similarity threshold and run multiple rounds of retrieval, observe whether the coverage range of returned results meets the business rigor requirements of audits, complete threshold calibration.
- Check the storage configuration of the vector database, confirm that the storage path of original documents and the vector index have established an association mapping, and original document content can be traced back through the vector ID.
The values provided here are common starting points, and should be evaluated against the relevant 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.