What this category of data looks like
The data for this category originates from risk control systems, audit records, user behavior logs, and report evidence. Update frequency is daily or real-time. Daily processed documents include account ban records, appeal materials, review opinions, and user notifications. Common fields include account unique identifiers, violation types, appeal submission times, and disposition results. The length of individual documents varies widely, ranging from short audit conclusions to complete user behavior evidence materials. This configuration guide is based on FastGPT v4.17.0.
What constraints do these characteristics impose on knowledge base retrieval and recall
Decentralized data sources and real-time updates require the retrieval system to support multi-data source synchronization access and incremental update mechanisms. Wide variation in document length, from short audit conclusions to complete behavior evidence materials, requires adaptation to text chunking and recall strategies for different lengths. Fields include sensitive identifiers such as account IDs, requiring strict implementation of content access permission controls during retrieval. Individual documents involve violation determination logic; semantic matching accuracy directly affects review efficiency, requiring precise recall of content related to appeal reasons and historical dispositions. Errors carry compliance risks, requiring recall results to meet the confidence requirements of business audits.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches appeal reasons and historical records related to account bans. This range balances recall coverage and precision, avoiding false recalls of unrelated violation records |
limit | Top 10 entries | For operational on-call scenarios, core historical data needs to be obtained quickly. Recalling 10 entries at a time balances information completeness and page loading efficiency |
searchMode | semantic | Addresses the semantic matching needs for appeal reasons. Semantic retrieval can accurately associate similar violation determination logic, adapting to the account review business scenario |
usingReRank | Enabled | Reranking recall results improves the display priority of highly relevant content, helping operational staff quickly locate core review basis |
datasetSearchUsingExtensionQuery | Enabled | Generates extended queries based on the original question, covering more implicit historical appeals and review records, improving recall comprehensiveness |
collectionIds | Bind a dedicated ban appeal knowledge base collection by business scenario | Isolates account data from different business lines, prevents unauthorized access to sensitive ban and appeal information, and complies with compliance requirements |
Three common mistakes
- Symptom: Violation evidence images uploaded to the knowledge base cannot be displayed properly, and evidence content cannot be fully presented in retrieval results. Cause: No access permissions for knowledge base image resources are configured, or image metadata is not associated with corresponding violation records during chunk processing.
- Symptom: When using the
bge-m3vector model, the relevance scores returned by semantic retrieval are abnormally high, leading to false recalls of a large number of unrelated historical ban records. Cause: The similarity threshold is not calibrated for the account appeal review scenario, or the semantic matching logic of the vector model is not adapted to violation determination text. - Symptom: Retrieval results are empty or exceed the configured
limitnumber of entries. Cause: The dedicated ban appeal knowledge basecollectionIdsis not correctly bound, resulting in the retrieval scope covering unrelated datasets, or incremental synchronization tasks are not executed on time leading to missing data.
How to confirm configurations are correctly set
- Submit a simulated user appeal query, check whether the relevance scores of the returned results fall within the range calibrated by the business, and confirm that the
similarityconfiguration is effective. - Check whether the total number of retrieval results matches the configured
limitparameter, and confirm that the retrieval scope only covers the boundcollectionIdsdedicated knowledge base collection. - Upload a violation evidence document with images, initiate a retrieval, and verify that images can load normally, confirming that image resource access and association configurations are correct.
- After enabling
usingReRank, check whether the content most relevant to the current appeal ranks first in the reranked results, confirming that the reranking logic is operating normally.
The configuration values listed above are common starting points. They should be evaluated against the reader’s own specific 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.