What does the data for this category look like?
The data for this category comes from social insurance handling systems, personal benefit records, labor relations and work injury identification systems, and the electronic social security card platform. The update frequency is daily. Commonly processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Most documents contain fields such as applicant identity information, payment period, identification result, and benefit standard. Units include fields like payment months, benefit amount, and identification number. Some materials mix structured forms and unstructured text.
What constraints do these characteristics impose on vector models and indexing?
Indexes must support incremental synchronization for daily updated data. This prevents excessive system resource use from full index reconstruction. Chunking strategies must adapt to mixed document types. Tailor strategies to split structured forms and non-lengthy policy text, preserving semantic integrity. Core business fields such as applicant identity and payment period have strict precision requirements. Vector recall must prioritize semantic matching for these fields. Fields with units such as benefit amount and identification number must avoid vector bias. Standardize field formats during preprocessing. Indexes must adapt to parsed multi-source text structures for mixed document formats. This ensures consistent vector generation across all document types.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to common text lengths for payment period explanations and policy clauses in this category’s documents, avoiding semantic breaks |
| Overlap Length | 100–150 characters | Compensate for context gaps caused by chunk cutting, enabling cross-chunk policy association recall |
| Similarity Threshold | 0.75–0.85 | Match the precision needs of social insurance policy Q&A, filtering low-relevance policy clauses |
| Single Search Limit | Top 5 entries | Control the scale of returned results, fitting the benefit verification Q&A business scenario |
maxToken | 8000 | Aligns with the general context limit of FastGPT v4.17.0, accommodating multiple policy references and business materials |
defaultToken | 1000 | Set a basic context window to ensure semantic integrity for basic Q&A |
Three common configuration errors
- Phenomenon: Retrieval results include policy clauses unrelated to pension, work injury, or unemployment benefit verification. Cause: No dedicated vector recall filtering rules are configured for this category’s business fields, such as payment period and benefit standard. Generic recall pulls in content from irrelevant categories.
- Phenomenon: Index updates fail. Logs show incremental synchronization timeout. Cause: No incremental indexing strategy adapted to daily update frequency is configured. Full index reconstruction uses excessive resources, leading to timeout.
- Phenomenon: Retrieval results include policy clauses with missing fields. Cause: No format standardization preprocessing is applied to structured document fields such as identity and payment period. Key business information is lost during vector generation.
How to verify correct configuration
- Upload a typical document from this category, such as work injury identification material. Check if chunked text length and overlap degree match preset values.
- Enter a standard query, such as "How to verify pension benefits". Confirm if the number of returned retrieval results falls within the preset single search limit.
- Review index update logs. Confirm that daily incremental synchronization tasks complete normally without timeout errors.
- Compare retrieval results to original policy documents. Confirm that results filtered by the similarity threshold are highly relevant to the query.
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.