What This Category of Data Looks Like
Data comes from daily business documents of local education bureau basic education departments, school academic affairs offices, enrollment examination institutions, and street community collaborative windows. This includes enrollment application forms, student status information forms, transfer applications, enrollment plans, admission rosters, and student status change approval forms. Data sources cover the National Primary and Secondary School Student Status Management System, local enrollment platforms, household registration and property data, and school academic affairs systems. The update frequency is daily. Data includes structured fields such as student identity identifiers, household registration addresses, student status numbers, and enrollment qualification conditions. It also includes unstructured text for enrollment policy explanations.
Constraints Imposed on Vector Models and Indexing
Decentralized daily updated data sources require indexes to support incremental updates, eliminating the time cost of full reindexing. Documents include structured tables and unstructured text, so chunking strategies must adapt to content splitting across different formats. Fields center on student identity and enrollment qualifications, so vector models must focus on relevant semantic features. Errors directly affect students’ normal school enrollment, so retrieved results need high confidence. Similarity thresholds and the number of recalled entries must be strictly controlled.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the typical content length of documents such as enrollment application forms and student status information forms, avoid semantic fragmentation caused by overly small chunks |
| Overlap Length | 100–150 characters | Ensure semantic consistency between adjacent chunks, cover key context of long texts such as student status change approval forms |
| Similarity Threshold | 0.75–0.85 | Match the semantic matching accuracy of enrollment policies and student status data, avoid low-relevance results interfering with inquiry and response |
| Maximum Search Results per Query | Top 10 | Control the amount of returned information, adapt to the quick verification needs of the inquiry and response scenario |
defaultToken | 4096 | Adapt to the total field length of student status data, meet the context carrying requirements of single-round conversations |
maxToken | 8192 | Match the system parameter range of FastGPT v4.17.0, support vector embedding of complete enrollment plan documents |
Three Common Mistakes
- When building an index, the progress bar stops at the last set of files with no log updates. The cause is that incremental index trigger rules are not configured. Loading all data for updates daily causes excessive system resource usage, leading to process freezes.
- During search testing, an "embedding failed" error is returned. The cause is that empty household registration fields in student status information forms are not preprocessed. Invalid content is not filtered during vector embedding, causing the embedding process to interrupt.
- After importing household registration and property data, the generated index has no valid matching results. The cause is that unstructured household registration text is not converted to structured fields. The vector model cannot accurately extract semantic features related to enrollment qualifications.
How to Confirm Correct Configuration
- Import a single enrollment application form, check that the chunked text length falls within the 800–1200 character range.
- Trigger an incremental index task, verify that updated student status data completes index updates within the measured time frame.
- Search for questions related to "transfer application process", check that the similarity scores of returned results fall within the 0.75–0.85 range.
- View the system configuration page, confirm that the values of
defaultTokenandmaxTokenare 4096 and 8192 respectively.
The values provided 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.