Solving the Precision-Recall Tradeoff: Search Result Aggregation

Semantic search retrieves documents by vector similarity but can include irrelevant results, while re-ranking improves precision by selecting stronger matches but remains limited by the number of returned documents and can reduce recall for questions requiring multiple sources. Supermemory’s Aggregation feature addresses this trade-off by synthesizing information from multiple relevant memories into each result slot, preserving a small search limit while supplying broader context. In an example query about Supermemory and its team, two conventional results provide incomplete details, whereas two aggregated results separately summarize the company’s purpose and identify team members. The approach is intended to help LLM applications answer complex, multi-session questions with high precision and recall while reducing token use, reasoning workload, and latency through the API option `aggregate: true`.

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