What This Category's Data Looks Like
Core data for this category comes from four channels: social insurance handling systems, personal entitlement records, labor relations and work injury recognition systems, and electronic social security card platforms. Daily processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury recognition materials, and unemployment insurance benefit application materials. All types of documents contain fixed fields such as insured years, recognition numbers, and applicant identity information. Data is updated daily, and review must be completed by synchronizing data from multiple systems.
Constraints on Vector Models and Indexing
The data characteristics of this category impose clear constraints on the vector model and indexing link. Data from multiple channels has large format differences. Unified format alignment must be completed before chunking to avoid vector space drift. The daily full data update volume is large, so incremental indexing must be supported to reduce processing time. Fields such as insured years and recognition numbers included in documents are core basis for review. When chunking documents, field association relationships must be retained to avoid core information being split and damaged. At the same time, the review results of benefit verification are directly linked to personal benefit payments. The accuracy of vector retrieval must strictly match business logic.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field length and core information density of social security documents, avoid including too many irrelevant contents in a single chunk |
| Overlap Length | 100–200 characters | Retain associated information of adjacent document chunks, avoid core fields such as payment period and recognition number being split into different chunks |
| maxToken | 4096 token | Match the average content length of social security documents, ensure that core information is fully covered when generating single-chunk vectors |
| defaultToken | 1024 token | Serve as the basic unit for default chunking, balance the accuracy of vector generation and processing efficiency |
| Similarity Threshold | Calibrated based on actual tests | Adapt to the core matching logic of social security review, avoid misjudging or missing key materials |
| Single Search Limit | Top 5 entries | Control the number of returned results, focus on the most relevant declaration materials and case content |
Common Configuration Mistakes
- Custom-chunked document chunks in the knowledge base are automatically deleted after duplication, resulting in index order differing from expectations. The cause is that the document deduplication configuration is enabled by default, and the switch was not turned off for the scenario where social security declaration materials are submitted from multiple sources.
- After configuring a custom vector model, the large language model is still used for inference during actual calls. The cause is that the vector model channel was not correctly linked to the knowledge base indexing link, and the default large language model interface is still bound.
- The knowledge base indexing speed cannot meet the daily update requirements. The cause is that no incremental indexing strategy was configured, and full social security data was processed for each indexing instead of only synchronizing content updated on the current day.
How to Verify Correct Configuration
- View the knowledge base settings page of FastGPT v4.17.0, confirm that the document deduplication switch status matches business requirements.
- Upload a single social security declaration document, test the vector generation results, confirm that the returned embedding data format meets expectations.
- Simulate a single indexing operation, verify that the indexing completion time matches business requirements.
- After adjusting the similarity threshold, input review-related questions, verify that the number and relevance of returned search results match business logic.
Note: The configuration values listed above are common starting points. They should be evaluated against local 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.