What the Data for This Category Looks Like
The data sources for this category include the national medical insurance 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. These documents contain fields such as diagnosis and treatment project codes, expense amounts, insured person identity identifiers, and settlement status. The data update frequency is daily, and it must cover all business documents related to single or batch medical expense payments.
What Constraints These Characteristics Impose on the Vector Models and Indexes Link
This category’s data is multi-source and fully updated daily. This requires the index module to support incremental updates and deduplication logic to avoid duplicate index entries. Documents include structured fields like diagnosis codes and expense amounts, plus free text descriptions. The vector model must therefore support embedding for both structured and unstructured content. Single business documents have many fields with wide length ranges. When chunking, the association between codes and corresponding expenses must not be broken, so chunking rules must align with document structure features. Settlement audits require linking multiple related documents, such as prescriptions and settlement lists. Indexes must therefore support cross-document associated recall to ensure information completeness during audits.
How to Configure
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Balances the integrity of diagnosis codes and text descriptions in medical insurance settlement documents, and avoids truncating key matching fields during chunking |
| Overlap Length | 100–200 characters | Ensures adjacent chunks cover associated content for the same diagnosis project or expense detail, reducing semantic breaks |
maxToken | 8192 token | Adapts to the context limit of mainstream embedding models, meeting the vector generation requirements for long settlement lists |
| Similarity Threshold | 0.75–0.85 | Balances the recall accuracy and miss rate of expense matching, adapting to the rigor requirements of medical insurance audits |
| Single Search Limit | Top 10 entries | Limits the number of recall results, avoiding information overload during audits, and matching the scale of associated documents in daily business |
defaultToken | Calibrated based on actual measurements | Adapts to the average token consumption of business documents, optimizing the stability of index generation |
Three Common Misconfigurations
- Symptom: Index tasks get stuck in the "Indexing" state for a long time with no progress updates, and do not recover after restarting the service. Cause: The
maxTokenparameter is not configured correctly, and vector generation for long settlement lists exceeds the model's supported range, causing task blocking. - Symptom: The number of index entries in the dataset automatically increases without manual operations, and duplicate document indexes appear. Cause: Deduplication logic for incremental indexing is not configured, and index entries for existing data are repeatedly generated during daily full updates.
- Symptom: Corresponding diagnosis and treatment project codes cannot be matched in retrieval results, or matching accuracy is low. Cause: Chunk length is set too short, cutting the semantic association between diagnosis codes and corresponding expense details, leading to failed vector recall.
How to Verify Correct Configuration
- Check the index generation logs of FastGPT v4.17.0, confirm that there are no
maxTokenover-limit errors for vector generation of long settlement lists, and verify that token consumption matches the configured value. - Manually trigger an incremental index task, verify that the final number of index entries matches the number of newly added documents that day, with no duplicate entries.
- Enter a pending audit expense detail, retrieve the associated settlement list and prescription, confirm that the similarity of the recall results meets the business rigor requirements.
- Check the binding configuration between the index model and the language model, confirm that their association has not been accidentally deleted, and that embedding and generation capabilities can be called normally.
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.