What this category of data looks like
Data for this category comes from social insurance management systems, tax social insurance premium collection systems, market entity registration information, and shared public security and civil affairs data. Common documents include insurance registration forms, premium base declaration forms, staff adjustment forms, social insurance premium verification forms, and personal benefit records. Fields include personnel identity identifiers, unit qualification information, and payment declaration-related numerical values. Data is updated daily, covering the declaration, verification, and cross-system reconciliation stages of the full insurance registration workflow.
Constraints on Vector Models and Indexes
Cross-departmental multi-source data has format differences. This requires vector models and index pipelines to support chunking logic compatible with both structured tables and semi-structured documents. The daily update frequency requires index configurations to support incremental synchronization, avoiding performance losses from full index rebuilding. Field differences across multiple business document types require retaining core related fields such as personnel identity and unit qualifications during chunking. This ensures accurate matching of business scenarios during vector recall. Cross-system data field consistency requirements prioritize associating personnel identity identifiers and unit information during index construction, reducing matching errors.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the field density of documents such as insurance registration forms and premium base declaration forms, retaining complete business-related context |
| Overlap Length | 100–150 characters | Prevents core information such as personnel identity and unit details from being split across chunks, ensuring continuity during vector recall |
defaultToken | 8192 | Matches the maximum input limit of general embedding models, covering content after chunking of long documents |
| Similarity Threshold | Calibrated based on actual testing | Adapts to field differences across cross-system data, avoiding mismatches or insufficient recall |
| Single Search Limit | Top 8 results | Covers recall requirements for multi-source data, balancing retrieval performance and recall coverage |
maxToken | 4096 | Limits the maximum length of the context window, preventing exceeding the processing limits of conversational agents |
Three Common Misconfiguration Issues
- Issue: Field missing occurs in retrieval results after enabling multi-vector support for table data in knowledge base settings. Cause: No separate chunking rules are configured for structured table fields, resulting in the vector index failing to fully capture the association between table column names and data.
- Issue: An error prompt appears immediately after clicking test after enabling a specified embedding model and filling in a custom request address and API key. Cause: The interface format of the custom request address does not match FastGPT’s calling specifications, or the API key’s permissions do not cover the embedding model call scope.
- Issue: A newly created conversational agent functions normally, but "No available index model detected" still displays after refreshing the page. Cause: The index model configuration was not saved to the active knowledge base binding configuration, or the system cache has not synchronized the latest model status.
How to Verify Successful Configuration
- Navigate to the vector model configuration page for the target knowledge base, confirm that the embedding model adapted to the business has been selected, and that custom parameters are filled correctly.
- Upload a typical insurance registration form or premium base declaration form, trigger a manual index update, and check whether the chunk preview retains core business fields.
- Create a test conversation, enter a query containing personnel identity or unit information, and verify that the retrieval results match the uploaded business document content.
- Check the system status panel, confirm that the index model is running normally with no error prompts.
The values provided here are common starting points, and should be evaluated against the user’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.