What this category of data looks like
Data for this category primarily comes from social insurance management systems, personal benefit records, labor relations and work injury identification systems, and the electronic social security card platform. Daily processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Data is updated daily. Documents include fields such as insured person identification, payment period, benefit verification standards, and qualification review conditions. They cover business units like payment months and benefit calculation amounts, as well as unstructured supporting text content.
Constraints for vector models and indexes
Daily updated data requires the index workflow to support incremental updates. This avoids resource consumption and delays caused by full reconstruction. Documents contain both structured fields and unstructured supporting materials. Dual logic of structured field matching and semantic vector recall must be supported. Fields related to benefit verification have high precision requirements. Vector recall matching rules must adapt to business rigor. This prevents low-match content from being included in the result set. Data formats vary across multiple sources. Standardization processing must be completed before vector generation. This ensures consistency of index data.
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 paragraph length of benefit verification documents, balances semantic completeness and index efficiency |
| Overlap Length | 100–200 characters | Prevents key information such as payment period and benefit standards from being truncated during chunking |
maxToken | 4096 token | Adapts to context length limits of mainstream embedding models, covers complete benefit verification material text |
| Similarity Threshold | Set based on actual testing | Adapts to business rigor requirements, prevents low-match irrelevant materials from being recalled |
| Single Search Limit | Top 10 entries | Controls returned result quantity, facilitates subsequent manual review and qualification matching |
| Reranking | Enabled | Improves sorting priority of high-match documents, adapts to accuracy requirements for benefit verification |
Three common configuration mistakes
- Abnormally high similarity values after switching embedding models. No similarity normalization rule is configured, and the raw inner product of embedding vectors is used directly as the matching score. This causes values to exceed expected ranges.
- No index progress display after knowledge base upload. No access channel is configured for the corresponding embedding model, or vector database connection parameters are incorrect. This prevents index tasks from starting normally.
- Search result count does not meet business expectations. Single search limit is configured too low, or similarity threshold is set too high. This filters out matching results that meet requirements.
How to confirm configurations are correctly set
- Upload a standard benefit application form document. Check if index progress displays normally. Confirm that chunking and vector generation processes are running correctly.
- Input test queries containing payment period and benefit standards. Check if the matching logic of returned results aligns with business expectations. Adjust the similarity threshold to a reasonable range.
- View vector database storage space usage. Confirm that no cumulative overflow issues occur with daily incremental indexing.
- Test uploading documents from multiple sources. Confirm that vector index formats are unified after standardization processing, with no missing fields or format errors.
The values provided here 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.