What this category of data looks like
This category includes two types of data. Daily business documents include establishment registration applications, articles of association, shareholder resolutions, residence certificates, business licenses, and food business license applications. Party building learning Q&A materials include supporting policy interpretations and learning points. Data sources are the full-process electronic enterprise registration system, the national enterprise credit information publicity system, the electronic business license system, and the residence information database. The update frequency is daily. Fields include entity name, residence, shareholder information, license type, party building knowledge points, and others.
What constraints these characteristics impose on the vector model and indexing link
Daily updated business data requires indexes to support incremental synchronization, to avoid resource consumption caused by full reconstruction. Diverse document types include structured certificates and unstructured application documents, so different chunking rules must be adapted to preserve business integrity. Multi-source data from different systems requires unified vector mapping logic to ensure matching consistency across sources. Party building learning Q&A needs to associate business documents with knowledge points, so indexes must support mixed recall of cross-type content, while avoiding mismatching between business details and party building knowledge points.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of registration and licensing documents, avoid splitting that breaks business logic |
| Overlap Length | 50–100 characters | Retain cross-block context association, facilitate matching coherent content for party building Q&A |
defaultToken | 4096 token | Match the input limit of mainstream embedding models, adapt to the total length of registration documents and party building materials |
maxToken | 8192 token | Support complete vector generation for long documents, avoid truncating key licensing application materials |
| Similarity Threshold | 0.75–0.85 calibrated via actual testing | Distinguish party building Q&A knowledge points from registration and licensing business details, reduce mismatching |
| Single Search Limit | Top 5 entries | Match the quick verification needs of window staff, avoid information overload |
Three common mistakes
- Phenomenon: After calling the knowledge base interface to generate an index, the embedding model changes to
embedding-3, and the original matching logic deviates. Cause: The embedding model configuration was not locked, platform default updates caused the model to change, and index rules were not adjusted synchronously. - Phenomenon: When creating a QA split file collection via OpenAPI, the index recall speed is slow, and single queries time out. Cause: The chunk length and single search limit were not adjusted according to the length of registration and licensing documents, and the single batch data processing volume is too large.
- Phenomenon: After replacing the embedding model, the imported knowledge base cannot directly update the index, and all files must be re-imported. Cause: Vector regeneration operation was not performed, old vectors were still generated based on the original model, and the mapping rules of the new model were not associated.
How to confirm the configuration is correct
- Run a test query, input party building learning-related questions, and verify that the returned embedding model identifier matches the configuration.
- Import a single registration and licensing document, check that the number of chunked fragments matches the expected chunk length setting.
- Call the knowledge base search interface, confirm that the number of returned results does not exceed the configured single search limit.
- Confirm that the current FastGPT version is v4.17.0, and check that there are no errors in the daily incremental index task running logs.
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.