What the data for this category looks like
Core 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 notices, hearing notices, penalty decisions, delivery receipts, and similar documents. A single case file consists of multiple associated documents. Fields include violation fact records, discretion basis, penalty decision content, document number, delivery date, and more. Strict logical links exist between each document.
Constraints Imposed on the Vector Models and Indexes Link
The daily update feature requires configuring an incremental indexing process to avoid resource consumption from full reindexing. The structure of multiple associated documents requires building associated indexes at the case file level. Do not store documents as separate chunks. This prevents key evidence from the same case file from being split across different index entries. Law enforcement recorder footage is unstructured data. It must first be converted to text format before vectorization processing. Law enforcement text with dense professional terminology requires the vector model to adapt to professional semantics. Index recall accuracy directly affects the efficiency and accuracy of subsequent legal review.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Matches the core content length of a single paragraph in a single law enforcement document, avoiding semantic breaks caused by cross-document concatenation |
| Overlap length | 100–150 characters | Retains association information for violation facts across paragraphs, preventing key evidence from being truncated by chunking |
| Similarity threshold | Calibrated based on actual testing | Must adapt to the semantic similarity of professional terminology in law enforcement case files, avoiding accidental recall of unrelated case files |
| Single search limit | Top 10 entries | Matches the limit on the number of associated documents that need to be reviewed during legal review |
defaultToken | 8192 | Adapts to the long text context length of administrative penalty decisions |
maxToken | 16384 | Covers the total text length after merging multiple documents in a single case file |
Three Common Configuration Mistakes
- A "training order creation failed" error occurs when calling the knowledge base API. The cause is failing to follow the daily incremental update configuration when triggering a training order, and directly adding data to the collection, leading to duplicate index conflicts.
- Irrelevant administrative inspection case files appear in search results. The cause is not setting a dedicated similarity threshold for law enforcement case files, leading to accidental recall of non-penalty inspection records.
- Law enforcement recorder footage cannot participate in retrieval synchronously. The cause is not first converting the footage to text format that can be vectorized, and directly uploading unstructured video files.
How to Verify Correct Configuration
- View the knowledge base update log to confirm that daily automatically triggered incremental indexing tasks execute normally, with no failed records.
- Enter violation fact keywords for retrieval, and verify that all recalled results are documents related to administrative penalty case files, with no unrelated record types.
- Test long text input to confirm that the
maxTokenconfiguration covers the total text length of a single case file, with no content truncation errors. - Check the model configuration list to confirm that supported embedding models have been added, with no missing model errors that cannot be selected.
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.