What data for this category looks like
This category of data originates primarily from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders. It updates daily. Daily documents include inspection records, case filing approval forms, on-site photos, correction notices, penalty decisions, and closing reports. Each document has dedicated fields:
- Inspection records include law enforcement time, location, and personnel information
- On-site photos include shooting location and timestamp
- Case file documents are linked to unified case filing numbers and violation type tags
Constraints on vector models and indexing from these data characteristics
This category of data mixes structured fields, short text, and unstructured image metadata. It receives daily batch updates, which creates a clear requirement for the index to support incremental synchronization. Document lengths vary widely: inspection records are mostly short text, while descriptive text attached to on-site photos can be long. A chunking logic that adapts to multiple content types is needed. Case review relies on a complete evidence chain. Index-retrieved content must accurately match violation types and law enforcement processes. This prevents irrelevant information from disrupting the integrity of the evidence chain for law enforcement review.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content density of common texts such as inspection records and penalty decisions, avoiding too much irrelevant information in a single chunk |
| Overlap Length | 50–100 characters | Retains contextual connections across chunks, ensuring logical coherence of on-site evidence descriptions |
| maxToken | 4096 | Adapts to the input limit of most general-purpose vector models, preventing chunked content from exceeding the model's processing range |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Matches the text similarity judgment standards for urban management case files, reducing false recall probability |
| Single Recall Limit | Top 8 entries | Balances review efficiency and evidence integrity, avoiding excessive redundant content interfering with law enforcement personnel's judgment |
| defaultToken | 1024 | Unifies default chunking parameters for unstructured metadata, adapting to short descriptive text attached to photos |
Three Common Misconfigurations
- An index error occurs after upgrading the version. The interface prompts "chunking failed" or returns a 500 status code. The root cause is that the new version adjusts the default verification logic for
maxToken, and the old configuration's chunking length exceeds the new model's input limit. - Knowledge base indexing gets stuck and does not complete. The status continuously shows "processing" with no progress updates. The root cause is that the incremental indexing switch is not enabled, and the batch data volume during full indexing exceeds the system's single-processing limit.
- Abnormal recall results appear after switching the vector model. A large number of non-urban management law enforcement-related contents appear in search results. The root cause is that the vector index for the new model is not regenerated, and the vector library data from the old model is still being used.
How to Confirm Proper Configuration
- Upload a single typical document such as a case filing approval form. Manually trigger chunking. Check that the chunked text length matches the set chunk length.
- Run a small-batch indexing task. Wait for completion, then check the system logs to confirm no chunking failures or timeout errors.
- Input a simulated review question such as "View the evidence materials for a certain illegal road occupation case". Check whether the recall results include the core content of the corresponding case file, and that the quantity matches the set single recall limit.
- After switching the vector model, regenerate the index and verify the consistency of the recall results, confirming that the model switch has taken effect.
The values provided here are common starting points, and should be evaluated against local sample data.
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.