What the data for this category looks like
The data for this category mainly comes from the national student financial aid management information system, school student registration system, civil affairs low-income and special hardship data, and fiscal payment system. The update frequency is every semester. Daily processed documents include family economic hardship certification forms, financial aid application forms, scholarship and grant review forms, disbursement lists, and public notification materials. Data fields cover student identity identifiers, family economic status certification information, financial aid application materials, review records, fund disbursement details, and more. Both structured form data and unstructured public notification documents are included.
What constraints these characteristics impose on the "vector models and indexes" link
The semester-level update frequency requires indexes to support a strategy combining periodic full refresh and incremental updates, to avoid frequent full synchronization consuming resources. Access to multi-source heterogeneous data requires unified field mapping rules to ensure that student identity, family status and other data from different systems are aligned in the vector space. The coexistence of structured forms and unstructured public notification documents requires a chunking strategy that balances short-field structured matching and long-text semantic understanding. At the same time, since errors directly affect student rights and audit compliance, the recall accuracy of vector retrieval must be prioritized to avoid missing key verification information.
How to set the configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Balances single-chunk semantic completeness and retrieval efficiency, adapts to the mixed data scenario of structured forms and unstructured public notification documents |
| Overlap Length | 100–150 characters | Prevents cross-chunk semantic breaks, ensures retrieval continuity for long-text public notification materials |
maxToken | 4096 token | Adapts to the input limit of mainstream vector models, covers the context requirements of single-batch retrieval |
| Single Search Limit | Top 8 results | Balances recall scope and manual verification workload, avoids excessive redundant results |
| Reranking | Enabled | Improves the ranking priority of highly relevant results, ensures the accuracy of financial aid qualification verification |
defaultToken | Calibrated based on actual testing | Adapts to the input parameter requirements of custom vector models, ensures consistency of data vectorization |
Three common mistakes
- No matching results after retrieving with a custom index configuration: Core fields such as student registration number and family economic status are not included in the index mapping, or the chunk length is set too small, causing key verification information to be truncated.
- Abnormal number of vector retrieval results: The single search limit is set beyond a reasonable range, or the value of
maxTokendoes not match the input limit of the selected vector model, leading to abnormal retrieval logic. - Unable to recall data after connecting to a custom vector database: The vector data format is not aligned according to platform requirements, or the database's similarity calculation rules do not match the platform's default configuration.
How to confirm the configuration is complete
- Upload a single family economic hardship certification form to trigger vector index construction, and check that the index status shows completed.
- Enter a simulated consultation question, such as "A student's family economic status certification materials", and check whether the matching fields of the retrieval results include core verification information.
- Adjust the chunk length or
maxTokenparameter, compare the changes in the recall scope of retrieval results to confirm that the configuration takes effect. - After connecting to a custom vector database, verify that there are no format error prompts in the data synchronization log.
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.