Data profile for this use case
Data sources include the national medical security information platform, hospital HIS and medical insurance interfaces, medical insurance catalog and coding library, and cross-regional medical settlement platform. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Fields cover settlement numbers, expense amounts, diagnosis and treatment project codes, insured person identity identifiers, rejection reasons, and more. Units include yuan, person-times, and standard code numbers. Data updates occur daily. The workflow must process single or batch medical expense payment-related data.
Constraints on vector models and indexes
Daily updated batch data requires indexes to support incremental updates and efficient batch processing. This avoids time-consuming issues caused by full index rebuilding. Differences in structure across multiple document types require chunking configurations adapted to the information density of different documents. This ensures core fields such as diagnosis and treatment codes and expense details are not split apart. The accuracy of medical insurance data directly relates to fund security. Vector recall accuracy must be maintained at a high level. The system must also support precise matching of structured fields such as settlement numbers and codes. Requirements for multi-data source access mean index configurations must support import of medical insurance data in different formats. This includes structured settlement details and unstructured medical record documents.
Recommended configuration values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the length of single associated information in medical insurance settlement documents, avoids breaking the association logic between codes and expenses |
| Overlap length | 100–200 characters | Retains core field information from adjacent chunks, prevents breaks in key information such as codes and settlement numbers caused by chunking |
| Similarity threshold | 0.75–0.85 | Matches consistency between medical insurance expenses and medical records or prescriptions, filters low-relevance search results, ensures audit accuracy |
defaultToken | 16000 | Adapts to the context length requirements of batch settlement data, avoids truncation of core audit information due to insufficient token count |
maxToken | 32000 | Adapts to overall processing requirements for long documents, ensures full inclusion of all information such as settlement lists and associated prescriptions |
| Single search limit | Top 10 entries | Focuses on highly relevant search results, fits the business scenario of medical insurance audit requiring precise matching of a small number of key entries |
Three common configuration errors
- Symptom: FastGPT calls a vector model and returns an access denial. Curl testing the interface returns vector data normally. Cause: The vector model interface has not been configured to allow cross-origin requests from the domain where FastGPT is deployed, or the interface authentication parameters have not been correctly entered in the platform settings.
- Symptom: A 401 error is prompted when accessing the m3e vector model. Cause: The model API key has not been correctly filled in, or the permission scope corresponding to the key does not cover vector generation requests.
- Symptom: Duplicate medical insurance settlement number entries still exist after index merging. Cause: The settlement number was not retained as a unique identifier field during chunk processing, or the unique key matching rule for index deduplication was not enabled.
How to verify correct configuration
- Access the vector model access settings page in FastGPT v4.17.0, confirm that the correct model interface address and authentication parameters have been entered.
- Upload a single medical insurance settlement list document, generate vectors, then search and match related expense detail entries. Check that the relevance of recall results meets the preset threshold.
- Run an incremental index update task, confirm that the update time meets the daily data processing time requirements.
- View the index merging logs, confirm that the deduplication rule has taken effect and no duplicate entries have been generated.
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.