What this category's data looks like
The primary entities for this category of data are education bureau financial aid centers, school student affairs offices or financial aid offices, and finance departments. Daily documents include family economic hardship assessment forms, financial aid application forms, scholarship and grant review forms, disbursement lists, and public notification materials. Data sources cover the national student financial aid management information system, school student registration systems, civil affairs low-income and special hardship datasets, and finance payment systems. Data is updated per semester. Core fields include student identity identifiers, financial aid eligibility levels, applied amounts, disbursement card numbers, public notification batches, and more.
Constraints on knowledge base retrieval and recall
Multi-source cross-department data sources require retrieval links to support cross-field associated matching, to prevent data disconnection between different systems. Semester-level update cycles require configuring regular synchronization mechanisms to keep knowledge base data consistent with the latest state of business systems. Single documents containing batch student information require retrieval recall granularity to align with single-student records, to avoid batch results interfering with audit judgments. Multi-dimensional business fields require retrieval to support multi-condition combined filtering, to ensure the accuracy and compliance of audit basis.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Similarity threshold | 0.75–0.85 | Student financial aid data requires precise matching of identity and eligibility, to avoid irrelevant low-similarity results interfering with audits |
| Single recall upper limit | 3–5 entries | The number of matching results for a single student must be controlled to avoid redundant information during audits |
| Search mode | Hybrid vector + keyword retrieval | Cross-source business data has both semantic associations and clear field matching requirements |
| Enable re-ranking | Enabled | Semantic similarity of multi-source heterogeneous data requires secondary calibration to improve the accuracy of retrieval results |
| Enable query optimization | Enabled | Financial aid audit queries often include multi-field combinations, and optimization can cover more business scenarios |
| Specify knowledge base collection | Bind the financial aid document collection for the corresponding semester, with no more than 1000 files per collection | Isolate data by semester to avoid confusion of historical data across batches |
Three common errors
- Phenomenon: After uploading a single document, the displayed number of chunks differs from the final ready chunk count, and duplicate indexing occurs. Cause: No reasonable deduplication logic is configured, causing the same document to be chunked and stored multiple times.
- Phenomenon: Retrieval results include cross-semester financial aid data, making it impossible to accurately locate content for the current audit batch. Cause: The knowledge base collection for the corresponding semester is not bound, so the retrieval scope covers all historical data.
- Phenomenon: The number of retrieval results exceeds expectations, making it impossible to quickly locate target student information. Cause: No reasonable single recall upper limit is set, resulting in too many irrelevant records being recalled.
How to verify correct configuration
- Run a single-student identity retrieval test, and check whether returned result fields include relevant information for the current audit batch.
- Review the knowledge base collection binding configuration, confirm that only financial aid documents and data for the corresponding semester are included.
- Trigger a full synchronization task, and verify that the number of chunks after synchronization matches the expected count for the original documents.
- Adjust values of retrieval-related parameters, and confirm that retrieval result matching accuracy and recall quantity meet business requirements.
The values listed are common starting points and should be measured against the target deployment’s own data 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.