What the data for this category looks like
Data for this category comes from the national integrated government service platform item database, administrative licensing item list, departmental business approval systems, and electronic certificate repositories. The update frequency is daily. Documents processed daily include service guides, application forms, commitment letters, acceptance notices, correction notices, administrative licensing decision documents, and license certificates. Bound by legal licensing items, the data includes structured and unstructured fields such as item lists, application material requirements, approval processes, and electronic certificates. No custom units are included.
What constraints these characteristics impose on the vector models and indexing link
Daily data updates require adaptation to high-frequency incremental index construction. This avoids excessive time spent on full index reconstruction. Documents include structured item lists and unstructured application materials. They need to support both field matching and semantic relevance retrieval. Electronic certificate repository data has varying degrees of format standardization. It needs to support vector encoding for multiple file types. Administrative licensing item boundaries are clear. Retrieval must strictly match item dimensions. This avoids cross-item confusion. Approval workflows have high requirements for result confidence. Index recall needs to balance accuracy and recall scope.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of documents such as service guides and application forms, avoid breaking single-block information |
| Overlap Length | 100–150 characters | Cover cross-block semantic connections, adapt to the contextual coherence of approval materials |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Match the strict matching requirements of administrative licensing items, avoid incorrectly recalling irrelevant items |
| Single Search Limit | Top 10 results | Control the scale of retrieval results, adapt to the quick verification needs of approval staff |
defaultToken | 4096 | Adapt to context carrying for long documents, support combined retrieval of complete item rules and application materials |
maxToken | 8192 | Support batch retrieval scenarios containing multiple application materials and approval files |
Three Common Mistakes
- Retrieval response timeout occurs. Single retrieval takes more than 30 seconds. The cause is failure to set up incremental indexing according to document update frequency. Full index reconstruction processes are still used. This leads to excessive load on the vector retrieval link.
- Retrieval result count does not match expectations. A large number of irrelevant licensing item materials are recalled. The cause is failure to set a reasonable similarity threshold, or failure to perform index grouping according to administrative licensing item dimensions. This leads to cross-item semantic confusion.
- Vector encoding failure occurs. Some electronic certificate files cannot generate valid vectors. The cause is failure to adapt to the standardization format of certificates. Multi-format compatible vector model configuration is not enabled. This leads to failed encoding of unstructured files.
How to Confirm the Configuration Is Set Up Properly
- Manually upload a standard application form and service guide. Verify in the knowledge base management interface of FastGPT v4.17.0 that the character count of each chunk after splitting falls within the set range.
- Initiate a retrieval for a specific administrative licensing item. Check that the similarity scores of returned results fall within the set threshold range.
- View the index update log. Confirm that the daily incremental indexing task executes normally, with no failed error reports.
- Test vector encoding for multiple file formats, such as PDF application forms and electronic certificate images. Confirm that there is no batch failure.
The values listed are common starting points. They should be evaluated against the samples relevant to the specific use case.
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.