What data looks like for this category
Typical participants in this category include urban management law enforcement bureaus, street comprehensive law enforcement teams, sanitation centers, and grid supervisors. Daily generated documents cover patrol records, case filing approval forms, text associated with on-site photos, rectification orders, penalty decisions, closing reports, and more. Data mainly comes from urban management law enforcement platforms, urban operation grid platforms, text parsed from video surveillance, and citizen complaint tickets. The update frequency is daily. Data fields include on-site evidence location information, violation type determination, responsible party identification, rectification execution status, and others. There is no unified fixed statistical standard.
What constraints these characteristics impose on vector models and indexing
Multi-source heterogeneous data sources require the indexing system to adapt to different text parsing logic for various formats. The daily update frequency requires support for incremental index updates to avoid resource consumption from full reindexing. Case documents contain a large number of law enforcement-specific terminology and on-site detail descriptions, which places requirements on the professional semantic adaptation capability of embedding models. Text associated with on-site scenes and structured law enforcement documents need to match associated fields to ensure retrieval results accurately correspond to core case elements. The strict nature of law enforcement scenarios requires no semantic deviation in retrieval results, as this could lead to law enforcement disputes.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the natural paragraph length of texts such as patrol records and penalty decisions, avoiding semantic fragmentation |
| Overlap Length | 100–150 characters | Retains contextual connections across chunks, matching the continuity of case descriptions |
defaultToken | 8191 | Adapts to the input limit of mainstream embedding models, prevents law enforcement texts from being truncated, and complies with FastGPT v4.17.0 calling rules |
maxToken | 16384 | Matches the reasonable length of knowledge base context splicing, adapts to the comprehensive retrieval needs of a single case file |
| Similarity Threshold | 0.72–0.78 calibrated via actual testing | Filters low-relevance patrol records and tickets, avoids invalid recall interfering with law enforcement judgments |
| Maximum Search Results per Request | Top 10 | Controls the scale of returned results, adapts to scenarios where law enforcement personnel quickly access core information |
Three common configuration mistakes
- Symptom: An error "No callable embedding model available" pops up when using the knowledge base. Cause: The target embedding model’s call configuration was not correctly added to the configuration file, or local deployment verification of the model was not completed. This corresponds to the typical issue reported in the community where errors persist after configuration.
- Symptom: Search response times out after vector indexing is completed. Cause: The chunk length is set too large, causing the single-batch embedding calculation volume to exceed the hardware’s bearing capacity, or incremental index updates are not enabled, leading to excessive time spent on full reindexing. This corresponds to the slow search issue mentioned in the community.
- Symptom: A large number of irrelevant general texts or non-law-enforcement tickets are mixed into search results. Cause: The similarity threshold is set too low, failing to effectively filter low-match-degree retrieval results.
How to confirm configurations are correct
- Check the embedding model configuration items in the configuration file, confirm they match the actual deployed model parameters.
- Import a single typical law enforcement case document, perform a chunking test, and verify whether the chunk results cover complete semantics without excessive truncation.
- Initiate a simulated law enforcement inquiry retrieval request, check whether the match degree and quantity of returned results meet business expectations.
- View the vector index update log, confirm that the daily incremental update task is triggered normally.
The values provided here 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.