What This Category of Data Looks Like
Data sources for urban sanitation and urban management law enforcement cases include urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders. Data is updated daily. Daily documents cover inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Data fields include case ID, responsible entity, violation type, rectification requirements, execution results, penalty content, and more. Some fields are enumeration values, while others are free text descriptions. No unified fixed units apply to these fields.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
Daily updated data requires the knowledge base to support incremental synchronization. This avoids performance loss caused by full re-imports. Multi-source heterogeneous document types require unified vector ingestion rules. On-site photo documents must complete OCR text extraction before being added to the retrieval scope. The association logic between different documents requires cross-collection recall during retrieval. For example, link filing approval forms with subsequent penalty decisions. The rigor of case handling requires retrieval results to accurately match law enforcement scenarios. This prevents irrelevant content from being recalled. It also requires retaining source document traceability information to support the legality of law enforcement bases.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Match business relevance of urban management law enforcement cases, filter low-relevance non-law enforcement documents |
limit | Top 8 entries | Cover reference to similar cases required for a single ledger summary, while controlling total context length |
searchMode | Hybrid search | Combine full-text search and vector search, adapt to multi-source heterogeneous law enforcement document formats |
usingReRank | Enabled | Perform secondary ranking on initial recall results, prioritize core penalty and rectification-related documents |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand query terms, supplement common law enforcement scenario terms such as "rectification requirements" and "penalty basis" |
collectionIds | Bind law enforcement ledger, inspection record, and penalty document collections | Limit retrieval scope, avoid recalling irrelevant knowledge base content |
Three Common Misconfigurations
- Phenomenon: AI responses only return the first half of knowledge base content, omitting subsequent penalty or case closure steps. Cause: The
limitparameter is set to fewer than the actual required number of recall entries, failing to cover complete law enforcement process documents. - Phenomenon: Generated ledger reports do not include cited source document references. Cause: Traceability display is not enabled in the retrieval configuration, or collections containing document metadata are not bound.
- Phenomenon: Data source loading errors occur during deployment or retrieval. Cause: The
collectionIdsparameter does not specify the correct law enforcement document collection ID, resulting in an empty retrieval scope.
How to Confirm Configuration Is Complete
- Initiate a test query containing "rectification process for a certain type of violation case". Check if the number of returned results matches the
limitsetting. - Review the end of the generated report. Confirm that traceability information such as source document name and upload time is included.
- Check the
collectionIdsitem on the configuration page. Confirm that target collections such as law enforcement ledgers, inspection records, and penalty documents are bound. - Use the FastGPT v4.17.0 test tool to run a query with non-law enforcement keywords. Confirm that irrelevant content is not included in retrieval results.
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.
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.