What This Category of Data Looks Like
Daily data for this category includes inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, case closure reports, and more. Data mainly comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint tickets. Update frequency is daily. Individual documents typically contain fields such as case number, violation type, responsible entity, on-site location coordinates, rectification requirements, and disposal progress. Some documents include unstructured image clips for on-site evidence verification.
Constraints on Vector Models and Indexes Workflow
Daily updated bulk data requires indexes to support incremental synchronization, avoiding performance loss from full reconstruction. Mixed document types (text, images) require vector models to support both text and visual feature generation, ensuring retrieval capability for on-site photos. Strong associative attributes between fields such as violation type and penalty basis require retaining contextual associations during chunking, preventing loss of key associated information after splitting. Additionally, the timeliness of case handling requires vector recall latency to match the decision-making rhythm of on-site disposal, avoiding disruption to law enforcement processes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the standard paragraph length of text documents such as inspection records and penalty decisions, avoiding cross-semantic splitting |
| Chunk Overlap Length | 100–200 characters | Ensures cross-paragraph associated information such as violation type and responsible entity is not truncated |
| Similarity Threshold | 0.72–0.85 | Matches the precise matching requirements of urban management cases, filtering irrelevant law enforcement records |
| Single Search Limit | Top 6 entries | Controls the context length of retrieval results, adapting to rapid decision-making scenarios during law enforcement on-site |
defaultToken | 4096 | Adapts to vector generation requirements for long documents such as case closure reports. FastGPT v4.17.0 supports this parameter configuration |
maxToken | 8192 | Adapts to context splicing requirements for multiple associated documents, covering common full-process documents for case handling |
Three Common Mistakes
- Symptom: An error pop-up reading "No available index model detected" appears after refreshing the knowledge base page. Cause: The dedicated vector model for urban management cases is not bound to the corresponding knowledge base, or model configuration has not completed synchronization and taken effect.
- Symptom: Tabular case data such as filing approval forms fails to trigger multi-vector retrieval. Cause: Multi-vector support for tabular data is not enabled in knowledge base settings, and vectors are only generated for plain text content.
- Symptom: An Invalid format error is returned when accessing multimodal Embedding models. Cause: Images are not preprocessed according to the standard resolution requirements for urban management on-site photos, or model call parameters do not match FastGPT’s input format specifications.
How to Verify Proper Configuration
- Upload a single typical urban management case document such as an inspection record, wait for vector generation to complete, and check whether the vector index status of the knowledge base displays normally.
- Input a simulated law enforcement scenario question such as "Disposal process for unauthorized material stacking in a certain area", verify that document fragments from this category can be retrieved.
- After enabling multimodal support, upload an on-site photo, test whether corresponding vector features can be generated and participate in retrieval.
- Call the FastGPT API interface, pass query terms related to urban management cases, and check whether the returned context contains document content from this category.
The values provided are common starting points and should be measured against the samples used in actual deployment.
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.