What the data for this category looks like
This category's data comes from the national primary and secondary school student status information management system, local enrollment platforms, household registration and real estate databases, and school academic affairs systems. Commonly processed documents include enrollment application forms, student status information forms, transfer applications, enrollment plans, admission rosters, and student status change approval forms. Data is updated daily. Core fields include student identity identifiers, household registration addresses, real estate information, enrollment qualification criteria, student status, and more. Each data entry includes structured form fields and unstructured explanatory text.
Constraints Imposed on Vector Models and Indexing
Multi-source data access requires the indexing link to support format alignment across systems, to avoid duplicate data entry and field conflicts. The daily update feature requires configuring incremental indexing strategies to reduce resource consumption from full indexing. Documents include structured forms and unstructured approval text. Chunk processing must retain field associations to avoid splitting that breaks the integrity of student status information. Core fields use student identity as the unique identifier. Vector matching must prioritize associating this field to ensure retrieval results accurately correspond to the target student’s ledger.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the structure of documents such as enrollment application forms and student status change approval forms, and retains the integrity of field associations |
| Chunk Overlap Length | 50–80 characters | Retains contextual association between adjacent chunks, and avoids splitting that disrupts approval logic for student status changes |
| Similarity Threshold | 0.72–0.85 | Distinguishes subtle differences in student identities, and avoids matching incorrect student status information |
maxToken | 4096 | Adapts to the text length of enrollment and student status documents, and avoids truncation of critical approval information |
defaultToken | 2048 | Balances retrieval context length and system response speed |
| Single Search Limit | Top 10 entries | Controls the relevance and quantity of returned results, and adapts to batch retrieval needs for enrollment ledgers |
Three Common Configuration Mistakes
- Symptom: Replacing the embedding model returns abnormal similarity values, including results above 10000. Cause: No similarity threshold filtering rule is configured. Raw model output scores are used directly, without adapting to the score range of the new model.
- Symptom: No indexing progress is displayed after uploading files to the knowledge base, and the file status remains unindexed. Cause: No available channel for the corresponding model is added, or channel configuration parameters do not match system requirements.
- Symptom: The vector database cannot connect normally after deployment, and retrieval tasks time out. Cause: The sandbox container and vector database image are not started simultaneously, preventing the retrieval workflow from executing.
How to Verify Successful Configuration
- Upload a single enrollment application form and student status information form, and check that the indexing progress completes normally with no error prompts.
- Enter student identity keywords for retrieval, and verify that the similarity scores of returned results fall within the preset threshold range.
- Test the incremental indexing function by uploading student status change data updated on the current day, and confirm that the indexing task triggers and completes automatically.
- Check system logs to confirm that the vector database connection status is normal, with no timeout or connection failure records.
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.