What the Data for This Category Looks Like
The core data for this category comes from the national medical insurance information platform, hospital HIS and medical insurance interfaces, medical insurance catalog and coding library, and cross-regional medical settlement platform. Data is updated daily, covering documents such as medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Documents include structured fields like diagnosis and treatment item codes, medical supplies codes, expense amounts, settlement status, and insured person information. Some semi-structured documents contain multiple sections of business-related content.
Constraints Imposed by These Characteristics on Vector Models and Indexes
Multiple data sources with varying formats require field standardization before entering the vector processing workflow. The daily data update requirement means indexes must support incremental updates to avoid resource consumption from full reprocessing. Most documents are business-related, so chunking must align with business field boundaries to avoid losing key associated information such as codes and amounts after splitting. Additionally, the audit link requires matching consistency between diagnosis and treatment items and expenses, so vector models need higher recognition accuracy for professional codes and semantic associations, and indexes must support joint recall across multiple documents.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the length of single business fields in settlement lists and expense details, avoiding loss of key information such as diagnosis and treatment item codes and expense amounts after splitting |
| Overlap Length | 100–150 characters | Covers cross-chunk diagnosis and treatment item code associations, avoiding loss of contextually related content during matching |
| Similarity Threshold | 0.75–0.85 (calibrated via actual testing) | Meets the accuracy requirement for matching diagnosis and treatment item code and expense consistency, balancing recall rate and false positive rate |
| Single Search Limit | Top 10 entries | Adapts to the number of associated documents for single-settlement audit, avoiding context overload that affects audit logic |
defaultToken | 4096 | Adapts to the long-text context requirement of combining party building learning Q&A with medical insurance business, and falls within the supported range of FastGPT v4.17.0 |
maxToken | 8192 | Handles multi-document combined context for batch settlement audits, meeting the length requirement for cross-document associated queries |
Three Common Mistakes
- When creating a knowledge base, no model is specified in the text understanding model dropdown menu. The symptom is that after adding ollama qwen2.5 and bge-m3 via the model channel, the corresponding options do not appear in the dropdown list. The cause is that the association permission for the vector model is not enabled in the knowledge base configuration, or the model deployment node has not completed synchronization with the platform service node.
- Vector training only supports single-file adjustments. The symptom is that batch triggering of dataset retraining is not possible, and only individual file uploads followed by parameter adjustments and triggering are supported. The cause is that the trigger rule for batch vector updates is not configured, or the platform has not enabled the corresponding batch processing function.
- Abnormal number of index recall results. The symptom is that the number of search return results is far lower than expected, with only a small number of irrelevant documents matched. The cause is that the chunk length is set too large, leading to incomplete semantic splitting, or the similarity threshold is set too high, filtering out valid matching content.
How to Confirm Proper Configuration
- Upload a single medical insurance settlement list document, confirm that the vector generation progress bar completes normally with no error prompts.
- Input medical insurance policy questions related to party building learning, verify that the returned results are associated with the correct medical insurance catalog documents and settlement rules.
- Test batch upload of multiple rejection lists, confirm that the index automatically updates without timeout or failure logs.
- Check that the ollama qwen2.5 and bge-m3 configured in the model channel are visible in the vector model dropdown list of the knowledge base.
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.