What the data for this category looks like
Data for this category originates from medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint leads. Updates occur monthly. Commonly processed documents include audit notices, inspection work papers, suspicion lists, interview records, decision notices, and rectification reports. These documents contain audit item numbers, involved entity information, and associated fields for relevant business records. The length of individual documents varies widely, ranging from several hundred-word suspicion lists to tens of thousands-word inspection work papers.
Constraints imposed by these data characteristics on the vector models and index stage
These data characteristics impose multiple constraints on the vector models and index stage. First, data sources include structured settlement data and unstructured documents. The system must support mixed-modal vector generation and index construction. Second, the monthly update frequency means real-time incremental indexing is not required. Batch full updates can be used instead. Third, the wide variation in individual document lengths requires flexible chunking strategies to accommodate different document sizes. Fourth, audit work requires associating multiple data sources to complete suspicion screening. Indexes must have cross-data-source associated recall capabilities. This ensures the relevance and accuracy of retrieval results, and avoids interfering with subsequent violation determination processes.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Accommodates the wide variation in document sizes for this category, balances semantic integrity and index construction efficiency |
| Overlap Length | 100–150 characters | Prevents chunking from breaking coherent semantics, matches the tightly structured paragraph logic of audit documents |
| Similarity Threshold | 0.75–0.85 | Accurately recalls highly relevant audit-related data, filters low-match invalid content |
| Single Search Limit | Top 10 entries | Focuses on core suspicions and associated records, reduces manual sorting workload |
maxToken | 8192 | Matches the context window of mainstream embedding models, ensures vector generation quality for chunked long documents |
defaultToken | 1024 | Matches the basic token counting standard for medical insurance-specific terminology, ensures accuracy of chunking and indexing |
Three Common Misconfiguration Errors
- Issue: The number of vector retrieval results far exceeds the preset limit. Cause: The single search limit parameter was not configured correctly. This leads to recall of excessive irrelevant medical insurance settlement data, increasing subsequent audit sorting costs.
- Issue: Semantic breaks appear in vectors generated after chunking. Cause: Chunk length was set too small, or overlap length was insufficient. This splits coherent violation descriptions in inspection work papers, preventing complete association of semantic information.
- Issue: Token count results do not match actual document content. Cause: The
defaultTokenparameter was not configured correctly. This causes embedding models to have inaccurate token counts for medical insurance-specific terminology, affecting the accuracy of chunking and indexing.
How to Verify Correct Configuration
- A single typical audit document is imported. Semantic integrity of chunked content is checked. Chunk length and overlap length are confirmed to match the logical structure of business documents.
- A test retrieval is run. The number of recall results and similarity match rate are verified. The single search limit and similarity threshold are confirmed to meet the screening requirements for audit work.
- The embedding model's token usage logs are viewed. The
maxTokenanddefaultTokenparameters are confirmed to match the context window and counting standards of the selected embedding model, with no truncation errors. - The reranking function is enabled. Result rankings before and after retrieval are compared. The reranking logic is confirmed to effectively filter low-relevance non-audit data.
The values given 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.