What the Data for This Category Looks Like
Cybersecurity financing daily report data mainly comes from public disclosure announcements from securities regulators, dynamic summaries from vertical cybersecurity industry media, and official financing announcements from leading security vendors. The data is updated daily. Each single document corresponds to one financing event. The document structure is standardized, including six core fields: full name of the financing subject, affiliated cybersecurity sub-segment, financing amount (unit: ten thousand yuan or hundred million yuan), investor list, financing round, and public disclosure date. Some supplementary documents include financing purpose descriptions and brief introductions of the subject's business scope.
What Constraints Do These Characteristics Impose on the Knowledge Base Retrieval and Recall Link?
Daily updated data sources require the knowledge base incremental sync frequency to align with the daily report release rhythm. Otherwise, recalled content will lag. The standardized but richly segmented sub-track document structure requires retrieval to support precise filtering by fields such as sub-segment, financing round, and amount range. This avoids recalling irrelevant financing events. The dense distribution of financing events in the same sub-segment requires adding deduplication logic in the recall link. This prevents repeated pushing of similar content. Additionally, each single document has a short length. The recall context splicing strategy must be adjusted to ensure retrieval results fully cover core required information, such as financing subjects and investors.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
rag_knowledge_sync_interval | Every 12 hours | The cybersecurity financing daily report is updated daily. Syncing every 12 hours can cover newly added events of the day and avoid frequent syncs consuming resources |
rag_chunk_size | 800–1200 characters | Each financing daily report document has a short length. This segment length retains complete core field information and avoids splitting that disrupts field relevance |
rag_similarity_threshold | 0.75–0.85 | Balance recall accuracy and coverage, filter low-relevance non-cybersecurity track financing events |
rag_top_k | Top 10 entries | Financing events in the same track are dense. Limiting the number of recalled entries avoids result redundancy while covering major recent events |
rag_filter_fields | ["sub-segment", "financing round", "disclosure date"] | Matches the core fields of the cybersecurity financing daily report, supporting retrieval by segmented dimensions |
rag_re_rank_top_n | Top 5 entries | Re-rank the initial recalled results, prioritize displaying financing events with higher matching degree to queries |
The parameter values provided on this page are all conventional recommendations, used as a starting point 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 the configuration.
Three Common Mistakes
- Phenomenon: An error occurs in the question answering link after adding the knowledge base, but the knowledge base independent search test results are normal. Cause: The
rag_knowledge_sync_intervalparameter is not configured, resulting in the incrementally synced document format not being adapted to the input requirements of the question answering engine. - Phenomenon: After switching the knowledge base vector model, the progress does not update, and the original model configuration cannot be restored. Cause: The
rag_model_cleanup_on_switchswitch is not enabled, and the index cache of the old model is not released, blocking the loading of the new model. - Phenomenon: The same query triggers knowledge base recall multiple times, and the returned financing event list is different. Cause: The
rag_chunk_overlapparameter is not set, and core fields are split into different segments during document splitting, resulting in inconsistent recalled context segments.
How to Confirm the Configuration Is Correct
- Execute the knowledge base batch sync task, check if the sync log includes the latest cybersecurity financing daily report events of the current day, to confirm the sync frequency matches the configured requirements.
- Input a query containing sub-segment and round on the knowledge base search test page, verify that documents matching the corresponding fields can be accurately recalled, to confirm the filtering rules are active.
- Manually switch the vector model and submit the task, check if the model loading progress completes normally, to confirm the model switching logic has no blockages.
- Trigger the same query multiple times, verify that the returned financing event list is consistent, to confirm the document splitting and recall logic is stable.
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.