What this category of data looks like
Data for this category comes from social insurance handling systems, personal benefit records, labor relations and work injury determination systems, and the electronic social security card platform. Daily processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury determination materials, and unemployment benefit application materials. These cover structured fields and unstructured text. Data update frequency is daily. Fields include payment period, benefit standards, material numbers, and corresponding units are years, yuan, and identification numbers. Service entities include social insurance handling agencies, human resources and social security bureau business sections, and street social security service windows. Data directly correlates to final results of individual benefit verification.
What constraints these characteristics impose on vector models and indexing
Daily updated bulk data requires indexes to support incremental updates. This avoids time-consuming full reindexing. Mixed multi-type document structures need segmentation logic adaptable to different text lengths. This prevents long text truncation or chunk loss. Fields contain clear business-related information. Vector indexes must retain the correspondence between fields and documents. This ensures retrieved results can be traced back to original materials. The rigor of benefit verification requires retrieved results to match semantic relevance of business scenarios. This avoids irrelevant content interfering with the accuracy of Party building learning Q&A.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to text lengths of daily documents, avoids chunk loss, and complies with FastGPT v4.17.0 segmentation logic |
| Overlap Length | 100–150 characters | Retains cross-chunk context association, ensuring semantic integrity of continuous business logic in Party building Q&A |
| Similarity Threshold | 0.72–0.85 | Matches the rigor requirements of benefit verification, filters low-relevance retrieved results |
| maxToken | 8192 | Adapts to long context requirements of Party building learning Q&A, complies with token limits of mainstream vector models |
| defaultToken | 512 | Sets default token quota for each text chunk, balancing segmentation accuracy and indexing efficiency |
| Single Search Limit | Top 5 entries | Focuses on core related materials, avoiding redundant results interfering with business review and learning scenarios |
Three common mistakes
- Knowledge base upload status stays in indexing for an extended period: The interface shows no change in indexing progress, and logs prompt that the full indexing task is incomplete. The cause is failure to configure incremental indexing trigger rules, as processing daily updated bulk data via full indexing takes too long.
- Chunk loss occurs when chunk length is set to 3000 characters: Some text fails to generate index chunks after document upload, and logs show token limit exceeded errors. The cause is chunk length exceeding the maximum token limit of the vector model, with the segmentation logic automatically truncating part of the text.
- m3e vector model access fails via aiproxy: The interface returns 403 errors or connection timeouts. The cause is failure to correctly configure the aiproxy vector model call endpoint, or missing matching authentication parameters.
How to confirm configurations are correct
- Upload a test benefit application form document, check if the indexing status completes within 10 minutes with no abnormal error logs.
- Submit a benefit verification question related to Party building learning, verify that retrieved document chunks include core content of corresponding business materials, with no irrelevant information mixed in.
- Check chunk information after indexing, confirm no empty fields or incompletely truncated text chunks fall within the preset chunk length range.
- Call the vector model interface, verify that returned embedding results are normal, with no token limit exceeded or format error prompts.
The values given 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.