What This Category of Data Looks Like
Data for this category comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. The update frequency is daily. Daily documents include on-site inspection records, interrogation transcripts, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and receipts of service.
Data fields include law enforcement subject information, descriptions of illegal facts, discretionary benchmark matching results, penalty decision content, receipt and filing status, and more. The structure centers on law enforcement process nodes, and strong relational dependency exists between different documents.
Constraints Imposed on Vector Models and Indexing
Data sources for this category are scattered and have a high update frequency. Indexes must support incremental synchronization and batch updates to avoid time-consuming issues caused by full reconstruction.
Multiple types of law enforcement documents have strong relationships. For example, a penalty decision relies on the content of interrogation transcripts and evidence lists. Indexes must support cross-document related recall to ensure that complete evidence chains can be associated during Q&A.
Data is directly linked to law enforcement results, which imposes strict requirements on the accuracy of vector recall. This avoids compliance risks caused by irrelevant documents being incorrectly recalled. Law enforcement documents include structured fields and unstructured text. Indexes must adapt to mixed-format vectorization processing.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of law enforcement documents, avoids information fragmentation within chunks, and ensures appropriate vector dimensions after single-chunk vectorization |
| Overlap Length | 100–200 characters | Preserves cross-chunk related law enforcement details, such as continuous Q&A content in interrogation transcripts, and prevents key information from being split across different chunks |
| Similarity Threshold | 0.75–0.85 | Balances recall accuracy and coverage, avoids compliance risks caused by irrelevant law enforcement documents being recalled, and meets the strict requirements of administrative penalty case files |
defaultToken | 4096 | Adapts to the total length requirements of most law enforcement documents, ensures that complete associated document content can be loaded in a single round of Q&A |
maxToken | 8192 | Supports context input containing multiple associated law enforcement documents, meets the requirements for complete traceability of law enforcement processes in party building learning Q&A |
| Maximum Recalls per Request | Top 6 entries | Controls context length to avoid exceeding model input limits, while covering core associated documents to ensure sufficient basis for Q&A |
Three Common Mistakes
- Phenomenon: Index construction tasks cannot be triggered in a docker-compose deployment environment, and index configuration items are empty on the interface. Cause: The corresponding data volume and index storage directory are not mounted in docker-compose.yml, causing the platform to be unable to access the law enforcement document data source.
- Phenomenon: The indexing process takes too long, and the task progress is stuck for more than 30 minutes on the interface. Cause: Incremental index synchronization is not configured, and full reconstruction of a dataset containing law enforcement recorder footage and multiple documents leads to excessive computing resource usage.
- Phenomenon: The vectorization result has insufficient semantic matching with law enforcement documents, and party building Q&A cannot accurately associate illegal facts with penalty basis. Cause: A suitable vector model is not selected based on document type, and a general text vectorization approach is misused to process datasets containing law enforcement recorder footage.
How to Confirm Proper Configuration
- A single penalty decision document is uploaded, the vectorization task is executed, and the task log is checked for normal chunking and vectorization progress, with no field errors.
- Party building learning-related law enforcement questions are input, such as "the penalty process for a certain type of illegal situation", and the recalled documents are verified to include corresponding associated content such as interrogation transcripts and penalty decisions.
- The disk usage of the index storage directory is checked, and the incremental update task is confirmed to be triggered automatically daily, with no accumulated tasks.
- The similarity threshold is adjusted, the number of recall results before and after the adjustment is compared, and the threshold configuration is confirmed to take effect.
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.