What the data for this category looks like
This category of data primarily comes from enterprise databases, data dictionaries, masking tools, and access approval systems. Data updates are rolled out per project or data batch. Daily produced documents include masking plans, field mapping tables, masking verification reports, and approval records. Data fields include sensitive field identifiers, masking rule codes, verification result statuses, approval workflow nodes, and more. There are no unified fixed units. Specific fields and units adjust based on the compliance requirements and configuration of individual projects. The core time-consuming links of data processing are concentrated in the sensitive field identification and masking rule configuration phases.
What constraints these characteristics impose on the vector models and indexes link
Decentralized data sources updated per project require vector indexes to support dynamic access to multi-source heterogeneous data, and adapt to masking document structures of different formats. Text features of fields such as sensitive field identifiers and masking rules vary significantly. A vector model capable of distinguishing fine-grained text features must be matched to avoid feature confusion. Data volume per batch fluctuates with project scale, and core processing links take a long time. The index structure must support flexible scaling to avoid excessive single-index time consumption. The update frequency of approval records and verification reports is not fixed. An incremental indexing mechanism must be configured to avoid repeated calculations and resource waste.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average text length of documents such as masking plans and field mapping tables, avoiding overly dispersed single-block text features |
| Overlap Length | 100–200 characters | Retains context information at chunk boundaries, reducing missed sensitive field identifications |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant masking rules and approval records, improving retrieval accuracy |
| Single Recall Limit | Top 10 entries | Controls the number of results returned per retrieval, adapting to the lightweight retrieval needs of technical documentation and API Q&A |
defaultToken | 4096 | Matches the total text length limit of most masking documents, avoiding truncation of critical rule content |
maxToken | 8192 | Reserves sufficient context space to support integration of associated information from multiple masking verification reports |
Three common configuration mistakes
- Search response times out, and the number of returned results is far lower than expected. The cause is that the incremental index is not configured. Full indexing of hundreds of thousands of masking data entries takes too long, leading to cache misses during retrieval.
- Vector retrieval results fail to match the target masking rule. The cause is that the chunk length is set incorrectly, splitting a document containing a complete sensitive field mapping into multiple blocks with incomplete features.
- Irrelevant outdated approval records appear in retrieval results. The cause is that the incremental indexing mechanism is not enabled, and expired approval data is not filtered, leading to redundant results being recalled.
How to confirm the configuration is correct
- View the vector index update log to confirm that only newly added or modified masking data is included in the index, verifying that the incremental index configuration takes effect.
- Retrieve masking plan documents, check that the similarity scores of returned results fall within the preset range, verifying that the similarity threshold configuration is reasonable.
- Test masking rule text of different lengths, confirm that no content truncation occurs, verifying that the
defaultTokenandmaxTokenconfigurations adapt to the current data scale. - Count the response duration of single retrieval, confirm that it meets the latency requirements of the business scenario, verifying that the single recall limit configuration is appropriate.
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 2026-10-19.