What this category of data looks like
Typical data stakeholders include social security handling agencies, human resources and social security bureaus, tax authorities, and employer personnel and finance departments. Daily documents include insurance registration forms, payment base declaration forms, personnel change forms, social insurance premium verification notices, and personal benefit records. Data sources include social insurance handling systems, tax social insurance premium collection systems, market entity registration information, and shared public security and civil affairs data. Update frequency is daily. Core fields include personnel identity identifiers, labor relationship status, payment base, personnel change status, verified payment amount, and others. Most documents are structured forms with a small amount of unstructured explanatory text.
What constraints do these characteristics impose on the vector model and index link
Daily updated data requires the index link to support incremental updates, avoiding time-consuming full reindexing. Multi-source heterogeneous data sources require indexes to adapt to field formats from different systems, and cross-source field alignment logic must be configured. Documents with a high proportion of structured forms require vector chunking to retain the integrity of core business fields, avoiding splitting that breaks business logic. Frequently changing personnel change data requires controlling the index refresh interval to ensure data timeliness. Additionally, sensitive data such as personal benefit records requires dedicated vector storage isolation rules to ensure data security.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the structured field length of insurance registration documents, avoids splitting that breaks the integrity of core business fields such as personnel identity and payment base |
| Overlap Length | 100–200 characters | Ensures semantic coherence of continuous fields such as personnel change status and payment base across chunks |
| Similarity Threshold | 0.7–0.8 | Filters low-match non-business-related retrieval results, meets the field matching accuracy requirements of social security data |
defaultToken | Calibrated based on actual testing | Adapts to the context length requirements of party building learning Q&A, ensures complete input for policy interpretation and business process questions |
maxToken | 4000–6000 token | Matches the batch retrieval context length of social security documents, avoids truncating core business data |
| Single Retrieval Limit | Top 8 entries | Balances retrieval efficiency and coverage of multi-source data required for insurance registration and premium verification |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Mistakes
- When creating a knowledge base, the corresponding model does not appear in the text understanding model dropdown menu. Phenomenon: Models such as ollama qwen2.5 and bge-m3 have been added in the model channel, visible in the available model list, but no corresponding options appear in the text understanding model dropdown. Cause: The association binding between the index model and the text understanding model has not been completed. Mapping configuration must be completed on the model management page.
- Duplicate indexing occurs in the knowledge base. Phenomenon: When uploading a single document, it shows 8 chunks, but becomes 13 chunks after being ready, with duplicate chunk entries. Cause: The incremental index trigger logic for daily updated data does not filter temporary cache files, leading to repeated chunk calculation.
- Bulk retraining of vector models is not supported. Phenomenon: Only single-file parameter adjustment followed by re-upload is supported, with no bulk training entry. Cause: The bulk vector training configuration switch has not been enabled in system settings. The corresponding function must be enabled.
How to Confirm Proper Configuration
- Navigate to the model management page, confirm that the index model and text understanding model have been associated and bound, and the corresponding models can be seen in the dropdown menu on the knowledge base creation page.
- Upload a single insurance registration form, check the chunk preview interface, and confirm that the number of chunks is stable with no duplicate chunk entries.
- Launch a social security policy Q&A related to party building learning, verify the matching degree of retrieval results, and confirm that the similarity threshold meets business requirements.
- Upload the daily updated personnel change form, check the index refresh log, and confirm that the incremental index task is executing normally with no timeout errors.
The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.
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.