What data looks like for this category
Data for this category comes primarily from the national student aid management information system, school student registration systems, civil affairs subsistence and extreme poverty data, and fiscal payment systems. Daily processed documents include family economic hardship certification forms, aid application forms, scholarship and grant review forms, disbursement lists, public notice materials, and similar documents. Data updates follow a semester cycle. Fields include student identity identifiers, family economic status information, aid application amounts, bank card information, and more. Single documents link to multiple system data sources, with relatively complex structures.
What constraints these characteristics impose on vector models and indexing
Cross-source data from multiple systems creates field heterogeneity. Index configurations must support cross-source data field mapping and association alignment to avoid business semantic fragmentation. The semester-level update frequency means high-frequency incremental indexing is not needed. A full-refresh index update strategy can be used. Documents contain multi-dimensional associated fields. Chunk processing must retain complete business semantics, and excessive splitting should be avoided. At the same time, errors in aid review directly impact student rights and interests. The index’s recall matching accuracy must meet the confidence requirements of business review. The defaultToken configuration must match the average length of documents in this category to prevent truncation of critical business information.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category's documents contain multi-field linked business content. This length retains the integrity of single business semantics |
| Overlap Length | 100–150 characters | Prevents cross-chunk semantic breaks, and adapts to the structure of review materials linked by multiple fields |
defaultToken | 8192 | Adapts to the vector model input limit of FastGPT v4.17.0, and covers the average length of documents in this category |
maxToken | 16384 | Handles long documents with attachment links, and meets the semantic requirements of complete review materials |
| Similarity Threshold | 0.75–0.85 range | Balances recall precision and coverage, and adapts to the strict business requirements of qualification verification |
| Single Recall Limit | Top 20 entries | Reduces redundant data and improves the verification efficiency of review staff |
Three common errors
- Symptom: Index tasks stay in "processing" for long periods, and logs show shard loading timeouts. Cause: Full indexing batch settings are not adjusted for the semester-level update frequency, and too many cross-system linked data are loaded at once.
- Symptom: Recall results have poor matching after selecting a vector model, such as unexpected semantic matching for family economic status. Cause: The model was not selected for the multi-modal document scenario, and a general text vector model was misused to process review materials with multiple formats.
- Symptom: Empty fields appear in the vectorized dataset, such as missing bank card numbers or student identity identifiers. Cause: Chunk configuration did not retain associated mapping for business fields, leading to loss of some critical business information.
How to confirm the configuration is set correctly
- Navigate to the index management page in FastGPT, and verify that chunk length and overlap length configurations match preset values.
- Upload a test family economic hardship certification form, and check that the vectorized document fields have no empty values and fully retain business linked information.
- Run a small-batch index test task, and confirm that the task has no timeout errors and the completion time meets expectations.
- Submit a test retrieval request, and check that the similarity of recall results falls within the preset range and meets business verification requirements.
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.