Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
This category of data includes audit notices, inspection work drafts, suspicious item lists, interview records, handling decision documents, rectification reports, and other documents. Data sources include intelligent medical insurance monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and medical supply traceability codes, and report clues. The data update frequency is monthly. Fields include treatment time, expense details, medical insurance codes, suspicious item IDs, violation types, handling opinions, and other items. Units include amount, number of visits, medical supply traceability code numbers, and other units.
What constraints do these characteristics place on the vector model and indexing workflow
Monthly update frequency allows incremental index updates, with no need for full rebuilds. This reduces computational overhead. Diverse document types cover structured settlement details, semi-structured inspection work drafts, and unstructured interview records. This requires adapted text splitting rules. Multi-source data includes short text traceability codes and long document handling decision documents. This requires balancing split lengths to retain complete semantic units. The need to accurately recall supervision rules and historical cases related to audit suspicious items requires index recall accuracy to match the strict requirements of auditing work. Avoid missing judgments or false judgments that cause fund losses. Embedding vector generation must comply with the maxToken limit to ensure complete semantics.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers semantic units of audit documents, avoids splitting critical violation information, and adapts to the context capability of Qwen3-Embedding-8B |
| Overlap Length | 100–200 characters | Retains semantic connections between adjacent chunks, prevents cross-chunk violation clues from being split, and is suitable for batch data processing with monthly updates |
| Similarity Threshold | 0.75–0.85 | Balances the strictness of auditing work and recall efficiency, avoids incorrectly recalling irrelevant settlement data or historical records |
| Single Search Limit | Top 10 results | Matches the typical review scope of audit personnel, avoids excessive redundant results that increase manual screening costs |
maxToken | 8192 | Adapts to the maximum context length of Qwen3-Embedding-8B, ensures complete semantics during embedding vector generation |
defaultToken | Calibrated via actual testing | Adjusts default token allocation for short texts from different sources (such as traceability codes, suspicious item IDs) to optimize embedding effects |
Three common configuration errors
- Phenomenon: Connection timeout or model unsupported error occurs when connecting to Qwen3-Embedding-8B deployed via VLLM. Cause: The model's API address and port are not correctly configured in FastGPT, or the model's embedding service port mapping is not enabled. This prevents normal calls to generate embedding vectors.
- Phenomenon: The number of knowledge base retrieval results does not match expectations, or irrelevant historical audit records appear. Cause: The similarity threshold is not adjusted based on the multi-source data characteristics of this category. Unreasonable threshold settings lead to false recalls or insufficient recalls. Or the single search limit is not set, resulting in redundant results.
- Phenomenon: Disk usage of locally deployed FastGPT exceeds expectations, and the knowledge base storage scale cannot be accurately counted. Cause: The storage paths of original files, split file chunks, and embedding vectors are not distinguished. Expired monthly update data is not cleaned regularly. This causes disk space to be occupied by invalid data.
How to confirm correct configuration
- Initiate an embedding request via FastGPT's model testing tool. Select a segment of audit document text. Confirm that normal vector data is returned without error messages.
- Select a specific suspicious item list sample to initiate a retrieval. Check that the recall results include relevant audit rules, historical handling records, and corresponding settlement data. This matches business scenario requirements.
- View the knowledge base storage directory. Confirm that original files, split text chunks, and embedding vectors are stored in their specified paths. Use system disk tools to view the usage of each part.
- Trigger an incremental index update. Confirm that only newly added audit documents and settlement data from the current month are processed. This aligns with the monthly update schedule.
The values provided are common starting points and should be evaluated 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.