What the data for this category looks like
Penalty case data comes primarily from two sources: historical penalty archives in internal compliance systems, and public announcement documents released by regulatory bodies. Updates are triggered by regulatory announcement releases and internal penalty implementation processes, with no fixed cycle. The number of documents updated in a single batch varies based on the scale of the violation incident.
Each penalty case document includes clear structured fields and unstructured text. Structured fields include penalty subject name, penalty document number, violation clause number, penalty amount unit (ten thousand yuan), and penalty decision date. Unstructured content covers details of violation reasons, original penalty basis text, and rectification requirement details. Overall document lengths vary widely, with some containing lengthy citations of legal provisions.
What constraints these characteristics impose on vector models and indexing
Multi-source data requires the indexing link to support cross-system document synchronization. It must connect to both internal compliance databases and external regulatory announcement interfaces.
No fixed update cycle means incremental indexing triggers must combine document modification times and compliance tags, rather than using fixed-cycle synchronization.
Documents contain both structured and unstructured content. Differentiated processing is needed for terminology-dense violation reason and penalty basis text, as well as standardized penalty amount and document number fields. This prevents structured information from being overlooked during semantic matching.
Lengthy legal citation paragraphs require vector models to support long-text semantic encoding, to avoid truncation of key information.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Core sections of penalty cases, such as violation reasons and penalty basis, mostly fall within this length range. This avoids semantic fragmentation and information truncation |
vector_model | bge-m3 | This model has high accuracy for semantic matching of professional terminology and long texts in the financial compliance field, and adapts to the text characteristics of penalty cases |
incremental_index_trigger | By file modification time + compliance tag | Penalty case updates have no fixed cycle. Triggering incremental synchronization by combining document update times and compliance classification tags reduces unnecessary indexing tasks |
retrieval_top_k | Top 8–12 results | The number of similar penalty cases in compliance scenarios is limited. Too many retrieved results will introduce irrelevant violation scenarios and reduce retrieval efficiency |
similarity_threshold | 0.72–0.85 | Compliance scenarios require strict matching of violation reason semantics. A threshold that is too low will lead to false recalls, while a threshold that is too high will miss relevant penalty cases |
structured_field_weight | Calibrated via actual testing | The impact of structured fields such as penalty amounts and violation clauses on retrieval accuracy needs to be adjusted based on business scenarios |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: Retrieval results only return vector fields, with original penalty document content missing. Cause: Vector storage and original documents are stored separately, and no associated mapping field is configured. This prevents retrieval from linking back to the original text.
- Symptom: When using the
bge-m3model, semantic retrieval similarity scores are generally high. Cause: Penalty case texts are dense with professional terminology. The model’s semantic similarity calculation baseline for same-domain texts is elevated, and the threshold has not been adjusted for compliance scenarios. - Symptom: Indexing tasks return no data for extended periods, or index status shows failure. Cause: Penalty cases contain special-formatted regulatory document numbers and legal clause numbers. No corresponding structured field parsing rules are configured, leading to document parsing failures.
How to Verify Correct Configuration
- Log in to the vector database management interface, check that both vector data and original document content for penalty cases are stored, and confirm that associated fields are correctly configured.
- Submit a test query for a known violation reason, review the similarity score distribution of retrieved results, and adjust
similarity_thresholdto a range that meets business requirements. - Trigger an incremental indexing task, check task logs for records of parsing failures or synchronization exceptions, and confirm that the update rhythm matches expectations.
- Compare the retrieval priority of structured fields, and verify that the
structured_field_weightconfiguration adapts to business rules.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.