What the Data for This Category Looks Like
This category's data primarily comes from cloud audit logs, IAM, CMDB, and security baseline scanning tools. Daily processed documents include compliance checklists, permission audit reports, resource lists, and rectification tracking forms. Data updates follow a monthly or quarterly cycle. Most documents combine structured tables and compliance entries. Fields include resource ID, permission configuration items, compliance status, rectification records, and others. Only original configurations and audit results are recorded per business scenario, with no additional custom statistical values.
Constraints Imposed on Vector Models and Indexing
These characteristics impose clear constraints on the vector models and indexing stage. Dispersed data sources and mostly structured formats require adapting vector extraction logic for different fields. This avoids unstructured text interfering with compliance matching. The monthly or quarterly update frequency means indexes do not need frequent incremental synchronization. A periodic full update strategy can reduce resource consumption for index construction. Fixed fields in structured documents require vector models to support joint embedding of multiple columns of data. This ensures semantic associations between key fields such as permissions and resource IDs are correctly captured. The rigor of compliance audits also requires that index recall results strictly match field definitions. No semantic deviation from unrelated configuration items can be introduced.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the structured field length of compliance checklists and resource lists. Prevents truncation of critical permission configurations in single chunks. |
| Overlap Length | 100–200 characters | Retains cross-chunk permission association information. Prevents semantic breakage when compliance entries are split. |
| Similarity Threshold | 0.75–0.85 | Meets the rigor requirements of compliance audits. Filters low-correlation resource configuration items. |
| Single Search Limit | Top 10 entries | Covers the typical number of entries in monthly resource lists. Avoids recalling redundant data. |
maxToken | 4096 token | Adapts to the input length limit of embedding models. Ensures core paragraphs of compliance reports are fully embedded. |
Three Common Mistakes
- An interface displays "No available index model detected", even though the model has been configured and passed conversation tests. The cause is failure to bind the corresponding vector model to the current compliance audit knowledge base in the knowledge base settings. Only global model configuration was completed.
- Clicking to enable the Doubao-embedding-large index model, filling in a custom request address and APIKey, then testing results in an error. The cause is failure to verify the connectivity of the custom address, or the APIKey does not have permission to access the vector embedding interface.
- Enabling multi-vector support for table data fails to recall structured fields. The cause is incorrect configuration of the embedding mapping for table columns. Only the multi-vector switch for the overall table was enabled, and key fields such as permissions and resource IDs to be embedded were not specified.
How to Confirm Successful Configuration
- Navigate to the knowledge base settings page in FastGPT v4.17.0, open the vector model configuration module. Confirm that an embedding model suitable for the compliance audit scenario has been selected, and the test connection returns a successful status.
- Upload a sample compliance checklist to trigger index construction. Check the index progress log for no prompts about field truncation or embedding failure.
- Initiate a query for permission anomalies. Verify that the resource ID and compliance status fields in the recalled results match the original document content.
- Adjust the similarity threshold. Verify that the number of recalled results changes as expected, with no large number of unrelated configuration items recalled.
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.