What this category’s data looks like
Data sources are log records generated by end users’ interactive operations including product queries, rate consultations, and policy consultations in finance, insurance, and wealth management scenarios. Single records are generated in real time after each user completes an interaction. Records are synchronized to the knowledge base in an incremental manner. Documents are structured short text with four core fields: query_text (original user query, string type), user_id (encrypted unique user identifier, string type), query_timestamp (ISO 8601 format timestamp of the operation), and related_content_ids (array of associated business content IDs). No additional nested levels exist.
What constraints these characteristics impose on the "knowledge base retrieval and recall" link
Data volume grows with user scale and updates in high-frequency incremental batches. This requires the retrieval system to support lightweight incremental synchronization logic, to avoid performance loss caused by full reconstruction. The data includes the user_id and query_timestamp fields. This requires the retrieval link to add user permission filtering and time range screening, to prevent cross-user data leakage or irrelevant historical recall, and meet privacy compliance requirements in the financial field. Single data entries are short text. This requires the retrieval strategy to adapt to short text similarity calculation, to avoid matching deviation caused by overly short text. It also requires controlling the amount of data recalled per batch to ensure terminal response speed.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
user_permission_enable | Enabled | Historical query records must be isolated by user ID to avoid recalling unauthorized data across users |
recall_top_k | Top 3-5 entries | Single historical query records are short text. Excessive recall introduces irrelevant information, and also controls response latency for in-terminal search |
similarity_threshold | 0.75-0.85 | Short text similarity matching must balance recall accuracy and coverage, to avoid incorrectly recalling irrelevant historical queries |
incremental_update_interval | 60 seconds | Historical query records have high incremental update frequency. Short update intervals ensure timeliness of data in the knowledge base |
max_single_doc_length | 200 characters | Single historical query record has short length. Limiting single document length avoids unnecessary word segmentation and calculation overhead |
rerank_enable | Enabled | Short text retrieval is prone to similarity misjudgment. Reranking improves the accuracy of result sorting |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Search results include historical query records from non-current users. Cause: The
user_permission_enableconfiguration is not enabled, and no user ID filtering condition is added during the retrieval link. - Phenomenon: The LLM-generated response references content outside the knowledge base. Cause: The
recall_top_kvalue is set too low, or thesimilarity_thresholdis set too high, resulting in necessary historical query records not being recalled. - Phenomenon: In-terminal search response times out. Cause: The
incremental_update_intervalis set too short, causing frequent refreshes of the retrieval library, or themax_single_doc_lengthis not limited, resulting in excessive single-data word segmentation time.
How to confirm configurations are properly set
- Log in to the system configuration backend, check the status and values of core configuration items such as
user_permission_enableandrecall_top_k, and confirm they meet preset requirements. - Submit a test historical query record, trigger in-terminal search, and verify that returned results only include historical data of the current user, and that quantity and relevance meet expectations.
- View system incremental update logs, confirm that newly submitted historical query records are synchronized to the retrieval knowledge base within the set update interval.
- Adjust the
similarity_thresholdparameter, initiate a test query, and verify that the matching accuracy of recall results changes as expected with the parameter.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.