What the Data for This Category Looks Like
This category of data comes from administrative law enforcement systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases of law enforcement agencies including market supervision, ecological environment, and transportation. The update frequency is daily. Daily documents include on-site inspection records, interrogation records, evidence lists, administrative penalty prior notice letters, penalty decision documents, service receipts, and more. The core focus is on illegal facts and penalty decisions, requiring the formation of traceable penalty evidence chains and decision documents. Most fields cover law enforcement-specific content such as law enforcement subjects, illegal circumstances, discretion benchmarks, and document numbers.
What Constraints These Characteristics Impose on the "Knowledge Base Retrieval and Recall" Link
Law enforcement documents are a mix of structured and semi-structured formats. Strict matching of evidence chains and legal clauses is required, so retrieval must accurately connect to specific law enforcement scenarios. High-frequency daily updated data demands that the retrieval pipeline has low latency to avoid impacting case handling efficiency. Document formats vary significantly across different law enforcement scenarios, so chunking and recall logic must be adapted for multiple document types such as records and decision documents. Penalty case files have extremely high compliance requirements. Recall results must cover key information including discretion benchmarks and legal clauses, and irrelevant content must be prevented from interfering with the review process.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.65–0.75 | Meets the precise matching requirements of law enforcement documents, avoids recalling irrelevant non-law enforcement content, and covers reference cases with similar illegal circumstances |
limit | Top 8–12 results | Balances context length and information richness, aligns with the information viewing habits of legal review processes, and avoids information overload from excessive content |
searchMode | Semantic Retrieval | Adapts to professional terminology and structured content of law enforcement documents, and is better able to match cross-field law enforcement scenario-related information compared to keyword retrieval |
usingReRank | Enabled | Performs secondary sorting on recall results, prioritizes content directly related to penalty discretion and legal application, and improves review efficiency |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands professional term queries related to law enforcement, covers more relevant documents, and improves recall comprehensiveness |
Three Common Configuration Mistakes
- Issue: After adjusting the
similarityandlimitparameters, the number of recall results does not stay fixed as the configuration changes. Cause: The system enables document content-based deduplication by default, and chunking of law enforcement documents filters out overly short invalid fragments. The actual recall upper limit is restricted by the number of valid chunks. - Issue: Table content visible in the knowledge base is not displayed in retrieval output results. Cause: Tables in law enforcement documents are mostly split into structured metadata chunks. The default retrieval mode only recalls plain text fragments and does not associate the structured storage content of tables.
- Issue: Large documents over 1000 MB cannot be paused or have their upload priority adjusted. Cause: Queue management rules for chunked uploads are not configured. The system processes all documents in upload order by default, making it impossible to temporarily prioritize small document uploads.
How to Verify the Configuration Is Correct
- Manually enter a query for a law enforcement scenario, and check whether the
similarityscores of the recall results match the configured range. - Check the number of retrieval results to confirm whether they fall within the range specified by the
limitconfiguration. - Verify whether the recall results include core content such as discretion benchmarks and legal clauses required for law enforcement, and confirm that
searchModeandusingReRankare working as intended. - Upload test small and large documents, and confirm that queue priority can be adjusted as needed.
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.