What this category of data looks like
Typical data for this category comes from central enterprise legal compliance departments, chief compliance officer offices, business departments, and external law firm business systems. The data includes compliance risk lists, contract legal review opinions, major issue legal opinions, compliance review forms, and violation report investigation reports. Data sources cover enterprise contract management systems, compliance risk databases, law libraries, case management systems, and report platforms. Update frequency is monthly. Data structure includes two categories: structured forms and unstructured professional documents. Fields include risk level, review opinion, processing result, and others. There is no fixed unified unit, and values adjust dynamically based on document type.
Constraints for vector models and indexes workflows
This category of data is updated monthly. Vector indexes must adapt to the rhythm of batch incremental construction and regular synchronization. This avoids resource consumption caused by full reconstruction. Document types include structured forms and unstructured professional documents. Targeted vector extraction for different fields must be supported. This prevents irrelevant content from interfering with retrieval results. Data involves compliance-sensitive information. Index processes must ensure data isolation and access permission control. Retrieval must respond quickly to high-frequency business scenarios, including compliance risk identification and contract review. Clear requirements exist for recall speed and accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits paragraph length of compliance documents, balances context integrity and retrieval accuracy |
| Overlap Length | 100–200 characters | Prevents chunk truncation of critical compliance clauses, ensures cross-chunk information coherence |
| Similarity Threshold | Calibrated based on actual testing | Matches semantic similarity distribution of compliance documents in this category, avoids false recalls or missed recalls |
| Single Search Limit | Top 10 results | Controls returned result count, aligns with business habits of legal professionals for quick review |
defaultToken | 2000 token | Meets context splicing needs for party building learning Q&A and compliance content, ensures information integrity |
maxToken | 8000 token | Complies with FastGPT platform default limits, prevents exceeding model processing boundaries |
Three Common Misconfigurations
- Phenomenon: Calling the knowledge base interface to generate indexes takes too long, and a timeout status code is returned. Cause: Batch processing parameters and chunk settings are not adjusted for monthly updated compliance documents. This causes the single batch processing data volume to exceed platform thresholds.
- Phenomenon: Imported compliance documents return irrelevant results during retrieval, and sorting priority does not meet business expectations. Cause: The similarity threshold is not adjusted based on the semantic characteristics of compliance documents, or targeted result reranking logic is not configured.
- Phenomenon: After switching the embedding model, the imported file set cannot directly reuse the existing index, and files must be reimported. Cause: The platform's built-in index reconstruction function is not used to update the vector database, and existing vector data generated by the original embedding model is directly relied upon.
How to Verify Correct Configuration
- Perform a single batch import test for monthly compliance documents. Check that index generation time meets business expectations. Confirm that chunk and batch processing parameters adapt to data scale.
- Input test questions related to compliance risks. Check that retrieved results cover core content of corresponding documents. Confirm that similarity threshold and recall limit configurations are reasonable.
- After switching the embedding model, trigger vector database updates via the platform's built-in index reconstruction function. Verify that file reimport is not required to complete model switching.
- Review the sorting logic of retrieval results. Confirm that priority rules align with business scenarios. Verify the effectiveness of reranking configurations.
Additional Note
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.