What data in this category looks like
Data for this category originates from data asset inventory, database metadata, business system descriptions, and regulatory documents. Daily documents include data catalogs, classification and grading rules, important data identification tables, and review minutes. Data updates occur quarterly or when the corresponding business system changes. Most documents use a mixed structure of structured tables and paragraphs. Fields include data asset identifiers, sensitivity levels, compliance tags, and affiliated business domains. There are no uniform fixed units. Values adjust flexibly based on document type.
What constraints these characteristics impose on vector models and indexes
This category’s data sources include structured metadata and unstructured regulatory documents. The mixed format requires vector indexes to support cross-structure text chunking. The quarterly or business change update frequency means indexes do not need frequent incremental synchronization. Only full or incremental refreshes run when updates are triggered. Most documents use a mixed table and paragraph structure. Chunking operations must retain the association between core fields such as data asset identifiers and sensitivity levels. Splitting must not break business logic. Most fields are classification label content. Vector retrieval needs to accurately match compliance tags and sensitivity levels. This raises higher requirements for result relevance. For index data scales of hundreds of thousands of entries, the system must adapt to resource consumption limits for batch processing. This avoids index task timeouts.
How to Set Configurations
The following configurations are based on the vector model and index functions of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of mixed documents such as data catalogs and review minutes. Avoids splitting that breaks core field associations |
| Overlap Length | 50–100 characters | Retains context for classification and grading rules across chunks. Ensures retrieval continuity for sensitivity level tags |
maxToken | 4096 token | Matches the processing limit for long texts such as regulatory documents. Prevents truncation of critical compliance clauses |
| Similarity Threshold | 0.75–0.85 | Accurately matches classification tags and sensitivity levels. Avoids retrieving unrelated data asset entries |
| Single Search Limit | Top 10 entries | Adapts to the regular retrieval needs of data governance teams. Balances retrieval efficiency and result relevance |
defaultToken | Calibrated based on actual testing | Adapts to resource constraints for locally deployed PGSQL vector databases. Prevents excessive memory usage |
Three Common Misconfigurations
- Symptom: The number of search results is far lower than expected after a full index of the knowledge base, or returns data asset entries that do not match the search keywords. Cause: The similarity threshold was not adjusted for the classification tag fields of this category. Incorrect threshold settings (too high or too low) cause retrieval bias.
- Symptom: Core fields such as asset identifiers and affiliated business domains in data catalogs are split across different chunks after chunking. This prevents associated retrieval. Cause: Chunk length was set too small. This breaks the complete association of core fields in structured tables.
- Symptom: Indexes do not synchronize updated classification and grading rules from business systems in a timely manner during incremental updates, or update tasks time out. Cause: The index trigger timing was not configured to match the quarterly or business change update rhythm. The
maxTokensetting was not adapted to the processing needs of long-text compliance documents. This causes excessive task resource consumption.
How to Confirm Configurations Are Correct
- Select 5 typical classification and grading rule documents and data catalog entries. Run retrieval tests. Check whether returned results include matching sensitivity levels and compliance tags.
- View vector index chunking logs. Confirm that core fields of structured tables are not split across different chunks.
- Trigger an index update task after a business system change. Check task completion status and resource usage.
- Adjust the similarity threshold range. Verify whether the relevance of retrieval results matches the accuracy requirements for this category.
The values provided are common starting points. They should be measured against the specific samples and deployment environment in use.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.