What Data for This Category Looks Like
Data for this category primarily comes from social insurance management systems, personal rights and interests records, labor relations and work injury identification systems, and electronic social security card platforms. Daily processed documents include benefit application forms, payment duration certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Data is updated daily. Most documents are a mix of structured forms and unstructured scanned files. Fields include payment duration, benefit calculation parameters, identity identifiers, material numbers, and similar details. There is no unified fixed format.
What Constraints These Characteristics Impose on Vector Models and Indexing
Full daily updated data requires indexes to support incremental synchronization, avoiding excessive computing resource usage from full reconstruction. The mixed structure of structured forms and unstructured materials requires targeted adjustments to chunking rules, to avoid breaking business associations when splitting structured fields. Field differences across multiple data sources require unified mapping during the indexing phase, ensuring cross-source matching of business information during retrieval. The strict requirements of benefit verification demand that vector recall results have clear business associations, preventing irrelevant materials from interfering with calculation logic.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the structured field length of mixed documents, avoiding splitting critical business parameters |
| Overlap Length | 100–150 characters | Retains business associations across chunks, ensuring the integrity of continuous fields such as payment duration |
defaultToken | 4096 | Matches the average length of benefit verification documents, covering complete information for a single material |
maxToken | 8192 | Supports batch processing of multiple related materials, meeting cross-document calculation needs |
| Similarity Threshold | 0.75–0.85 | Balances recall precision and coverage, preventing irrelevant materials from entering the calculation link |
| Single Search Limit | Top 10 entries | Controls the number of returned results, reducing the workload of subsequent manual review |
Three Common Misconfigurations
- Phenomenon: Unable to split files by business category when exporting the knowledge base; only full knowledge base export is supported. Cause: Failed to create independent knowledge bases in advance by benefit type (pension, work injury, unemployment), leading to reduced data management and retrieval efficiency.
- Phenomenon: The number of vector recall results exceeds expectations, increasing time spent on the calculation link. Cause: Failed to set reasonable similarity thresholds and single search limits, resulting in recall of a large number of irrelevant non-business materials.
- Phenomenon: Structured fields cannot be parsed correctly after importing Feishu multi-dimensional documents. Cause: Failed to enable indexing optimization configurations for structured documents, leading to loss of field association relationships.
How to Confirm Configurations Are Correct
- Run an incremental synchronization test, verify that only updated daily data is correctly indexed, with no full reconstruction trigger records.
- Select typical benefit verification materials, test that the similarity of vector recall results falls within the preset range, and that all associated business fields are included.
- Import Excel files and multi-dimensional documents from a Feishu folder, verify that structured fields can be correctly extracted and included in the index.
- After connecting external vector and language models, run a small-batch test task, confirm that the system does not report hardware resource overload errors.
The values listed 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.