Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
This category's data mainly comes from the health supervision information system, medical institution electronic registration system, complaint reporting platform, and law enforcement inspection records. Daily documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. The data update frequency is monthly. Most fields involve entity information such as institution qualification numbers, practice scope, rectification requirements, and penalty reasons. Some documents are semi-structured text or scanned copies, with no unified standardized format.
What constraints do these characteristics impose on the vector models and indexes link
Multi-source heterogeneous data sources and semi-structured document formats require the indexing system to support unified parsing and vector mapping of unstructured and semi-structured content. The monthly update frequency requires the incremental indexing scheduling logic to adapt to fixed cycles, avoiding resource waste caused by full indexing. Strongly associated entity fields in documents, such as penalty reasons and rectification requirements, require retaining contextual associations during chunking to prevent loss of business logic after splitting. The party building learning Q&A scenario needs to associate both business supervision documents and party building learning materials at the same time. The index needs to support collaborative retrieval across knowledge bases to ensure that retrieval results cover both types of content.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semi-structured characteristics of documents in this category, retain complete business context of supervision inspection forms and rectification notices |
| Overlap Length | 100–150 characters | Avoid splitting the association between penalty reasons and rectification requirements |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguish retrieval matching degrees between party building learning content and business supervision content |
| Single Search Limit | Top 8 entries | Adapt to the monthly updated data volume, control retrieval resource consumption |
defaultToken | 4096 | Adapt to the mixed context length of party building Q&A and business documents |
maxToken | 8192 | Support complete vector indexing of long documents such as penalty case files |
Three common misconfigurations
- A large number of irrelevant non-business content appears in retrieval results, or party building content and business content are mismatched. The cause is mixing vector models with different encoding rules, and failing to unify the mapping logic for entity fields in this category.
- Index tasks frequently time out or fail, unable to complete incremental synchronization of monthly data. The cause is that the chunk length exceeds the
maxTokenlimit of the vector model, resulting in failed vector generation. - Retrieval results fail to associate penalty reasons and rectification requirements, leading to information fragmentation. The cause is not retaining sufficient contextual overlap during chunking, causing core business logic to be split.
How to verify proper configuration
- Upload a single supervision inspection form and party building learning material, initiate a retrieval request, and verify that the results cover both types of content and the matching degree meets the preset threshold.
- Check the index task management interface, confirm that incremental indexing automatically triggers on a monthly cycle with no error logs.
- Select a penalty case file for chunk preview, confirm that the associated content of penalty reasons and rectification requirements is not split.
- Initiate a cross-knowledge-base retrieval request, verify that collaborative retrieval between party building learning content and business supervision documents works properly.
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.