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 coding database, and cross-regional medical treatment settlement platform. The update frequency is daily. Daily documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Data fields cover diagnosis and treatment item codes, pharmaceutical and consumable codes, expense amounts, insured person information, settlement status, and more. A single record is associated with multiple types of business documents, with a relatively structured format but complex logical relationships between fields.
What constraints do these characteristics impose on the vector models and indexes link
The daily update frequency requires indexes to support incremental synchronization, to avoid performance loss caused by full reconstruction. Significant structural differences exist across multiple types of business documents, so chunking rules need to be adapted for different document types such as settlement lists and prescriptions. Complex field association logic requires retaining contextual associations between documents, to avoid losing business association information after splitting. The scale of hundreds of thousands of records requires balancing recall speed and accuracy, while vector models need to adapt to professional semantic scenarios such as diagnosis and treatment codes and expense rules to ensure matching precision.
Configuration Settings
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field length and business semantic integrity of medical insurance documents, avoiding splitting that breaks the association of key information such as codes and amounts |
| Overlap Length | 100–150 characters | Retain business context of adjacent chunks, preventing breaks in cross-chunk code matching |
| Similarity Threshold | Calibrated via actual testing | Must match the semantic matching accuracy of medical insurance policy Q&A, balancing recall rate and precision |
| Single Search Limit | Top 10 results | Balance retrieval speed and recall coverage, adapting to the business query volume of single reimbursement audit |
maxToken | 4096 | Adapt to the concatenated length of policy documents and business documents, meeting long-context Q&A requirements |
defaultToken | 512 | Control the output length of a single embedded text, adapting to vector database storage and retrieval performance |
Three Common Mistakes
- Phenomenon: Search speed is too slow after the knowledge base index is completed, with a single query taking more than 10 seconds. Cause: No incremental index strategy is configured for daily incremental updates, and full vector matching is triggered for each retrieval.
- Phenomenon: text-embedding-ada-002 is configured, but the retrieval prompts that no associated content is available. Cause: The interface permissions of the embedding model are not correctly bound in the system configuration, or the chunk length exceeds the token limit supported by the model, leading to embedding failure.
- Phenomenon: The corresponding medical insurance policy clauses are not matched in the retrieval results. Cause: The similarity threshold is set too high, filtering out policy content with similar semantics but different expressions, or the association context between policies and documents is lost during chunking.
How to Confirm Proper Configuration
- Perform a simulated query for a single reimbursement audit, check that the return speed of retrieval results meets business expectations, and adjust relevant configuration items to optimize performance.
- View the vector database storage usage and retrieval logs, confirm that the incremental update task is triggered normally, and there are no failure logs.
- Manually enter policy-related query terms, check that the semantic matching degree of retrieval results meets the accuracy requirements of business audits, and adjust the similarity threshold to a suitable range.
- Check the configuration parameters of the embedding model, confirm that the interface call permissions are normal, and there are no connection errors or insufficient permission prompts.
The values provided here 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.