What this category of data looks like
Data for this category comes primarily from enterprise OA systems, organizational structure repositories, internal policy documents, and user feedback tickets. Daily document maintenance covers process configuration sheets, approval node tables, form field descriptions, and change records. Process changes trigger data updates, which occur at least once per month on average. Data fields include approval node permissions, form validation rules, process flow paths, and other items. No unified fixed units exist, and values adjust based on specific configuration items.
What constraints these characteristics impose on the reranking and hybrid retrieval link
Data from office system process configurations has scattered sources and non-standardized structures. Hybrid retrieval must match both structured approval nodes and unstructured policy text simultaneously. This requires balancing vector recall rate and reranking accuracy. Data update frequency fluctuates with business changes, so incremental updates must be supported to avoid resource consumption from full reindexing. Data fields have no fixed units, so reranking models must support semantic matching across multiple field types, and cannot rely on single-dimensional numerical weights. In operations scenarios, tickets must be responded to quickly, so retrieval latency must be kept within a reasonable range. Hybrid retrieval resource usage must align with the enterprise’s existing hardware conditions.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | Office system process configuration data includes both structured and unstructured content; reranking improves the accuracy of approval rule matching |
similarity | 0.72–0.85 | Matches the semantic similarity matching range for process configuration sheets, filtering low-relevance node configuration content |
limit | Top 8 entries | Balances retrieval recall volume and operations ticket response speed, avoiding excessive redundant data entering the context window |
searchMode | hybrid | Combines vector retrieval and keyword retrieval to meet recall requirements for structured approval node tables and unstructured policy text; aligns with the hybrid retrieval logic of FastGPT v4.17.0 |
| Reranking model GPU memory quota | 6–7 GB per GPU | Matches the actual GPU memory usage of the bge-reranker model, preventing single GPU resource exhaustion |
| Reranking trigger threshold | Trigger after recalling the top 10 entries | Aligns with the data volume of office system process configurations, reducing unnecessary reranking resource consumption |
Three common misconfigurations
- Symptom: Hybrid retrieval response time exceeds 10 seconds, causing some ticket processing timeouts. Cause: The
limitparameter’s recall count is not restricted, or the reranking model trigger threshold is not configured, leading to excessive data entering the retrieval pipeline. - Symptom: Only the highest similarity result is retained after reranking, resulting in missed approval rule matches for low-relevance but critical node configurations. Cause: The
limitparameter is incorrectly set to return only 1 result, failing to retain enough candidate sets for reranking filtering. - Symptom: GPU memory usage is too high after reranking model invocation, triggering system resource alerts. Cause: The number of concurrent reranking model calls per GPU is not restricted, or the GPU memory quota is not calibrated based on actual testing, leading to multiple GPUs occupying large amounts of memory simultaneously.
How to confirm configurations are properly set
- Run a simulated operations ticket query, checking that the number of retrieval results matches the
limitparameter setting. - View reranking model invocation logs, confirming that the
usingReRankfunction is enabled normally and result sorting aligns with semantic matching logic. - Test the response latency of both hybrid retrieval and pure vector retrieval, confirming that hybrid retrieval latency stays within a reasonable range.
- View the GPU resource monitoring dashboard, confirming that reranking model GPU memory usage does not exceed the preset quota.
The values provided are common starting points and should be evaluated 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.