What the Data for This Category Looks Like
The data for this category mainly comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. Daily documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. The data update frequency is monthly. Most documents contain fields related to institution qualification identification, inspection item details, rectification requirements, and penalty-related information. Some are structured forms, while others are semi-structured law enforcement records.
What Constraints These Characteristics Impose on Vector Models and Indexes
The monthly update frequency requires indexes to support incremental synchronization and regular full refresh, to avoid excessive resource usage from full reconstruction. Multiple document types include structured forms and semi-structured law enforcement records, so the system must adapt to text chunking logic for different lengths. The data focuses on compliance-related fields such as institution qualifications and rectification requirements, so vector models must accurately capture these semantic connections. Long-text law enforcement records may cause excessive token consumption, so the vector generation length for single text entries must be reasonably limited.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text lengths of structured forms and semi-structured law enforcement records for this category |
| Overlap Length | 100–150 characters | Preserves contextual connections across chunks, such as institution qualifications and rectification requirements |
| Similarity Threshold | 0.72–0.80 | Accurately matches compliance-related semantics of qualifications and inspection items, avoids false matches |
| Single Search Limit | Top 8–12 results | Focuses on highly relevant supervision and compliance data, adapts to query needs in law enforcement scenarios |
maxToken | 8000–10000 token | Adapts to vector generation requirements for long-text law enforcement records, avoids truncating critical compliance information |
defaultToken | Calibrated based on actual measurements | Adapts to token limits of different embedding models, supports normalized configuration |
Three Common Misconfigurations
- Phenomenon: After switching the knowledge base vector model, the interface shows no progress, and it is not possible to revert to the original model. Cause: Only the model parameters were modified without triggering vector regeneration, and the incremental index task was not started, causing the index task to stall.
- Phenomenon: A single query consumes more than the preset
maxToken, leading to query timeout or empty results. Cause: The chunk length of single text entries was not limited, and long-text law enforcement records were directly sent to the vector model, exceeding the token limit. - Phenomenon: Vector matching results are unrelated to compliance qualifications, and matching accuracy is insufficient. Cause: The similarity threshold was set too low, introducing a large number of irrelevant complaints or non-law-enforcement data that interfere with retrieval results.
How to Confirm the Configuration Is Correct
- Manually import a typical document for this category, check if the chunked text length matches the preset chunk length configuration.
- Initiate a query targeting institution qualifications, verify that the number of returned results matches the single search limit setting.
- Check if the vector model’s normalized configuration is enabled, matching the update requirements of FastGPT v4.17.0.
- Trigger an incremental index task, confirm that the task progress bar updates normally with no stalls.
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.